1397
44
4
0
177
0
Investigation of Geostrophic and Ekman Surface Current Using Satellite Altimetry Observations and Surface Wind in Persian Gulf and Oman Sea
Investigation of Geostrophic and Ekman Surface Current Using Satellite Altimetry Observations and Surface Wind in Persian Gulf and Oman Sea
https://jesphys.ut.ac.ir/article_64857.html
10.22059/jesphys.2018.244979.1006938
0
The rise of satellite altimetry is a revolution in the ocean sciences. Due to its global coverage and its high resolution, altimetry classically outperforms in situ water level measurement. Ekman and geostrophic currents are large parts of the ocean’s current, playing a vital role in global climate variations. According to the classic oceanography, Ekman and geostrophic currents can be calculated through the pressure gradient force as well as the friction force assuming that the water’s density is constant. Investigation of Ekman and geostrophic currents existence along with the determination of their velocities can profoundly affect the various events of oceanography and different interactive processes between the atmosphere and the ocean. Additionally, the measurement of sea currents can be useful in determination of contamination transport, seawater exchange, fisheries, oil transfer, immigration of aquatic animals and several marine activities (e.g. military, telecommunication, fishing and research activities) and also has different effects on the regional climate. In the current study, local and climatic conditions, Ekman and geostrophic currents and their velocities have been investigated based on the solution of Ekman and geostrophic equilibrium equations in the region of the Persian Gulf and the Oman Sea. To this end, using data of Saral and Jeason-2 altimetry satellites and surface wind data measured by ASCAT satellite, velocities values of v and u as well as the value and the direction of Ekman and geostrophic currents were extracted in forms of monthly data. The results were compared with obtained measurements by AVISO and NOAA for the region of the Persian Gulf and the Oman Sea, and based on the obtained results of this study, the difference in the value of these currents is about 1 cm/s.
1
The rise of satellite altimetry is a revolution in the ocean sciences. Due to its global coverage and its high resolution, altimetry classically outperforms in situ water level measurement. Ekman and geostrophic currents are large parts of the ocean’s current, playing a vital role in global climate variations. According to the classic oceanography, Ekman and geostrophic currents can be calculated through the pressure gradient force as well as the friction force assuming that the water’s density is constant. Investigation of Ekman and geostrophic currents existence along with the determination of their velocities can profoundly affect the various events of oceanography and different interactive processes between the atmosphere and the ocean. Additionally, the measurement of sea currents can be useful in determination of contamination transport, seawater exchange, fisheries, oil transfer, immigration of aquatic animals and several marine activities (e.g. military, telecommunication, fishing and research activities) and also has different effects on the regional climate. In the current study, local and climatic conditions, Ekman and geostrophic currents and their velocities have been investigated based on the solution of Ekman and geostrophic equilibrium equations in the region of the Persian Gulf and the Oman Sea. To this end, using data of Saral and Jeason-2 altimetry satellites and surface wind data measured by ASCAT satellite, velocities values of v and u as well as the value and the direction of Ekman and geostrophic currents were extracted in forms of monthly data. The results were compared with obtained measurements by AVISO and NOAA for the region of the Persian Gulf and the Oman Sea, and based on the obtained results of this study, the difference in the value of these currents is about 1 cm/s.
1
18
Saeed
Farzaneh
Saeed
Farzaneh
Assistant Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering,
University of Tehran, Iran
Iran
saeed.farzaneh@gmail.com
Kamal
Parvazi
Kamal
Parvazi
Ph.D. Student, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran
Iran
kamal.parvazi@ut.ac.ir
Tayebe
Noroozi
Tayebe
Noroozi
M.Sc. Student, Department of Surveying Engineering, faculty of Engineering, University of Zanjan, Zanjan, Iran
Iran
noroozi_tayebe@yahoo.com
Ekman current؛ Geostrophic current؛ Surface wind؛ Wind stress
Satellite altimetry
[Andersen, B., 2010, Satellite derived reference surfaces for surveying.Dtu-Space. Copenhagen. Denmark.##Aken, H. M. van, 2007, The Oceanic Thermohaline Circulation: An Introduction. Springer Science & Business Media.##Apel, J. R., 1990, Principle, of Ocean Physics. Academic Press, chp. 6.##AVISO, 2012, DT CorSSH and DT SLA Product Handbook - Aviso. France.##Bowditch, N., 2012, The American Practical Navigator. CreateSpace Independent Publishing Platform.##Dawe, J. T. and Thompson, L., 2006, Effect of ocean surface currents on wind stress, heat flux, and wind power input to the ocean. Geophys. Res. Lett., 33, L09604. doi:10.1029/2006GL025784.##Deng, X., Griffin, D. A., Ridgway, K., Church, J. A., Featherstone, W. E., White, N. J. and Cahill, M., 2011, Satellite Altimetry for Geodetic, Oceanographic, and Climate Studies in the Australian Region. In: Vignudelli, S., Kostianoy, A.G., Cipollini, P., Benveniste, J. (Eds.), Coastal Altimetry. Springer Berlin Heidelberg, 473–508.##Ekman, V. W., 1905, On the influence of the Earth’s rotation on ocean currents. Arkiv for Matematik, Astronomi, och Fysik, 2(11).##Fleet, D. J. and Weiss, Y., 2005, Optical Flow Estimation.##http://coastwatch.pifsc.noaa.gov.##http://oceanwatch.pifsc.noaa.gov.##Forman, M. G. G., 1998, Manual for tidal currents analysis and prediction, pacific marine science report. Podaac and Podaac Merged Gdr (Topex/Poseidon) Users Handbook. Jpl, D-11007, November.##Stewart, R. H. 2008, Introduction to physical oceanography, Department of Oceanography, Texas A & M University.##Lagerloef, G. S. E., Mitchum, G. T., Lukas, R. B. and and Niiler, P. P., 1999, Tropical Pacific near-surface currents estimated from altimeter, wind, and drifter data. J. Geophys. Res., 104(C10), 23, 313–23, 326, doi:10.1029/1999JC900197.##Munk, W. H., 1950, On the wind-driven ocean circulation. J. Meteorology, 7(2), 79–93.##Nansen, F. and Sverdrup, O. N., 1898, Farthest north: being a record of a voyage of exploration of the ship" Fram" 1893-96, and of a fifteen month's sleigh journey by Dr. Nansen and Lieut. Johannsen (Vol. 1). Harper & brothers.##Nitta, T. and Yamada, S., 1989, Recent warming of tropical sea surface temperature and its relationship to the Northern Hemisphere circulation. Journal of the Meteorological Society of Japan. Ser. II, 67(3), pp.375-383.##Reynolds, R. M., 1993, Physical Oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman--Results from the Mt Mitchell Expedition. Marine Pollution Bulletin, 27, 35-59, Printed in Great Britain.##Parvazi, K., Asgari, J., Amirisimkooei, A. R., and Tajfirooz, B., 2015, Determination of difference between datum and reference ellipsoid by using of analysis of altimetry data of Topex/Poseidon, Jason-1 and observations of coastal tide gauges, 5(1).##Picot, N., Case, K., Desai, S. and Vincent, P., 2003. AVISO and PODAAC user handbook. IGDR and. http://www.aviso.##Reynolds, R. W. and Smith, T. M., 1994, Improved global sea surface temperature analyses using optimum interpolation. J. Clim., 7, 929–948. doi:10.1175/1520-0442(1994)0072.0.CO;2##Scharffenberg, M. G. and Stammer, D., 2010, Seasonal variations of the large-scale geostrophic flow field and eddy kinetic energy inferred from the TOPEX/Poseidon and Jason-1 tandem mission data, J. Geophys. Res., 115, C02008, doi:10.1029/ 2008JC005242.1.##Shum, C. K. and Braun, A., 2004, Satellite Altimetry and Gravimetry. Lecture notes presented at Norwegian University of Science and Technology (NTNU), Trondheim, Norway, http://www.octas.statkart.##Stewart, R. H., 2009, Introduction to Physical Oceanography. Orange Grove Texts Plus, College Station, Tex.##Stommel, H., 1948, The westward intensification of wind-driven ocean currents. Transactions, American Geophysical Union, 29(2), 202–206.##Sverdrup, H. U., 1947, Wind-driven currents in a baroclinic ocean: with application to the equatorial currents of the eastern Pacific. Proceedings of the National Academy of Sciences, 33(11), 318–326.##Swift, S. A. and Bower, A. S., 2003, Formation and circulation of dense water in the Persian/Arabian Gulf. J. Geophys. Res., 108(C1), 3004, doi:10.1029/2002JC001360.##Vignudelli, S., Kostianoy, A., Cipollini, P., and Benveniste, J., 2011, Coastal Altimetry, Springer, German, Berlin.##Yelland, M. and Taylor, P. K., 1996, Wind stress measurements from the open ocean. J. Physical Oceanography, 26(4), 541–558.##Yelland, M. J., Moat, B. I. Taylor, P. K. Pascal, R. W. Hutchings, J. and Cornell, V. C., 1998, Wind stress measurements from the open ocean corrected for airflow distortion by the ship. Journal of Physical Oceanography, 28(7), 1511–1526.##Zonn, S., Kosarev, A. N., Glantz, M. and Kostianoy, A. G., 2010, The Caspian Sea Encyclopedia, 2010 edition. ed. Springer, Berlin; London.##]
0
Transforming Geocentric Cartesian Coordinates to Geodetic Coordinates by a New Initial Value Calculation Paradigm
Transforming Geocentric Cartesian Coordinates to Geodetic Coordinates by a New Initial Value Calculation Paradigm
https://jesphys.ut.ac.ir/article_65883.html
10.22059/jesphys.2018.246251.1006946
0
Transforming geocentric Cartesian coordinates (X, Y, Z) to geodetic curvilinear coordinates (φ, λ, h) on a biaxial ellipsoid is one of the problems used in satellite positioning, coordinates conversion between reference systems, astronomy and geodetic calculations. For this purpose, various methods including Closed-form, Vector method and Fixed-point method have been developed. In this paper, a new paradigm for calculation of initial values is presented. According to the new initial values, two state of the art iterative methods are modified to calculate the geodetic height and the geodetic latitude accurately and without iteration. The results show that for those points with height values between -10 to 1,000,000 km (30-fold more than the altitude of GPS satellites), the maximum error of the calculated height and geodetic latitude is less than 1.5×10-8 m and 1×10-14 rad (error lower than 0.001 mm in horizontal), respectively.
1
Transforming geocentric Cartesian coordinates (X, Y, Z) to geodetic curvilinear coordinates (φ, λ, h) on a biaxial ellipsoid is one of the problems used in satellite positioning, coordinates conversion between reference systems, astronomy and geodetic calculations. For this purpose, various methods including Closed-form, Vector method and Fixed-point method have been developed. In this paper, a new paradigm for calculation of initial values is presented. According to the new initial values, two state of the art iterative methods are modified to calculate the geodetic height and the geodetic latitude accurately and without iteration. The results show that for those points with height values between -10 to 1,000,000 km (30-fold more than the altitude of GPS satellites), the maximum error of the calculated height and geodetic latitude is less than 1.5×10-8 m and 1×10-14 rad (error lower than 0.001 mm in horizontal), respectively.
19
28
Norollah
Tatar
Norollah
Tatar
Ph.D. Student, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran
Iran
norolahtatar@gmail.com
Saeed
Farzaneh
Saeed
Farzaneh
Assistant Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran
Iran
saeed.farzaneh@gmail.com
Geodetic coordinate transformation
Cartesian geocentric coordinate
Curvilinear geodetic coordinate
[Borkowski, K. M., 1989, Accurate algorithms to transform geocentric to geodetic coordinates. Bulletin géodésique, 63, 50-56.##Bowring, B. R., 1976, Transformation from spatial to geographical coordinates. Survey review, 23, 323-327.##Civicioglu, P., 2012, Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247.##Featherstone, W. and Claessens, S., 2008, Closed-form transformation between geodetic and ellipsoidal coordinates. Studia geophysica et geodaetica, 52, 1-18.##Feltens, J., 2008, Vector methods to compute azimuth, elevation, ellipsoidal normal, and the Cartesian (X, Y, Z) to geodetic (φ, λ, h) transformation. Journal of Geodesy, 82, 493-504.##Fukushima, T., 1999, Fast transform from geocentric to geodetic coordinates. Journal of Geodesy, 73, 603-610.##Fukushima, T., 2006, Transformation from Cartesian to geodetic coordinates accelerated by Halley’s method. Journal of Geodesy, 79, 689-693.##Heiskanen, W. A. and Moritz, H., 1967, Physical Geodesy. San Francisco: Freeman.##Jones, G., 2002, New solutions for the geodetic coordinate transformation. Journal of Geodesy, 76, 437-446.##Kumi-Boateng, B. and Ziggah, Y. Y., 2016, Accuracy assessment of cartesian (X, Y, Z) to geodetic coordinates (φ, λ, h) transformation procedures in precise 3D coordinate transformation–A case study of Ghana geodetic reference network. Journal of Geosciences and Geomatics, 4, 1-7.##Laskowski, P., 1991, Is Newton's iteration faster than simple iteration for transformation between geocentric and geodetic coordinates? Bulletin Géodésique, 65, 14-17.##Ligas, M. and Banasik, P., 2011, Conversion between Cartesian and geodetic coordinates on a rotational ellipsoid by solving a system of nonlinear equations. Geodesy and Cartography, 60, 145-159.##Lin, K.-C. and Wang, J., 1995, Transformation from geocentric to geodetic coordinates using Newton's iteration. Bulletin Géodésique, 69, 300-303.##Pollard, J., 2002, Iterative vector methods for computing geodetic latitude and height from rectangular coordinates. Journal of Geodesy, 76, 36-40.##Shu, C. and Li, F., 2010, An iterative algorithm to compute geodetic coordinates. Computers & Geosciences, 36, 1145-1149.##Turner, J. D., 2009, A non-iterative and non-singular perturbation solution for transforming Cartesian to geodetic coordinates. Journal of Geodesy, 83, 139-145.##Vaniček, P. and Krakiwsky, E. J., 1986, Geodesy: The Concepts. Amsterdam: North Holland.##Vermeille, H., 2002, Direct transformation from geocentric coordinates to geodetic coordinates. Journal of Geodesy, 76, 451-454.##Vermeille, H., 2004, Computing geodetic coordinates from geocentric coordinates. Journal of Geodesy, 78, 94-95.##Vermeille, H., 2011, An analytical method to transform geocentric into geodetic coordinates. Journal of Geodesy, 85, 105-117.##Zhang, C., Hsu, H., Wu, X., Li, S., Wang, Q., Chai, H. and Du, L., 2005, An alternative algebraic algorithm to transform Cartesian to geodetic coordinates. Journal of Geodesy, 79, 413-420.##]
0
Large-scale Inversion of Magnetic Data Using Golub-Kahan Bidiagonalization with Truncated Generalized Cross Validation for Regularization Parameter Estimation
Large-scale Inversion of Magnetic Data Using Golub-Kahan Bidiagonalization with Truncated Generalized Cross Validation for Regularization Parameter Estimation
https://jesphys.ut.ac.ir/article_65881.html
10.22059/jesphys.2018.247879.1006954
0
In this paper a fast method for large-scale sparse inversion of magnetic data is considered. The L1-norm stabilizer is used to generate models with sharp and distinct interfaces. To deal with the non-linearity introduced by the L1-norm, a model-space iteratively reweighted least squares algorithm is used. The original model matrix is factorized using the Golub-Kahan bidiagonalization that projects the problem onto a Krylov subspace with a significantly reduced dimension. The model matrix of the projected system inherits the ill-conditioning of the original matrix, but the spectrum of the projected system accurately captures only a portion of the full spectrum. Equipped with the singular value decomposition of the projected system matrix, the solution of the projected problem is expressed using a filtered singular value expansion. This expansion depends on a regularization parameter which is determined using the method of Generalized Cross Validation (GCV), but here it is used for the truncated spectrum. This new technique, Truncated GCV (TGCV), is more effective compared with the standard GCV method. Numerical results using a synthetic example and real data demonstrate the efficiency of the presented algorithm.
1
In this paper a fast method for large-scale sparse inversion of magnetic data is considered. The L1-norm stabilizer is used to generate models with sharp and distinct interfaces. To deal with the non-linearity introduced by the L1-norm, a model-space iteratively reweighted least squares algorithm is used. The original model matrix is factorized using the Golub-Kahan bidiagonalization that projects the problem onto a Krylov subspace with a significantly reduced dimension. The model matrix of the projected system inherits the ill-conditioning of the original matrix, but the spectrum of the projected system accurately captures only a portion of the full spectrum. Equipped with the singular value decomposition of the projected system matrix, the solution of the projected problem is expressed using a filtered singular value expansion. This expansion depends on a regularization parameter which is determined using the method of Generalized Cross Validation (GCV), but here it is used for the truncated spectrum. This new technique, Truncated GCV (TGCV), is more effective compared with the standard GCV method. Numerical results using a synthetic example and real data demonstrate the efficiency of the presented algorithm.
29
39
Saeed
Vatankhah
Saeed
Vatankhah
Assistant Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Iran
Iran
svatan@ut.ac.ir
Magnetic survey
Sparse inversion
Golub-Kahan bidiagonalization
Regularization parameter estimation
Truncated generalized cross validation
[Boulanger, O. and Chouteau, M., 2001, Constraint in 3D gravity inversion. Geophysical prospecting, 49, 265-280.##Chung, J., Nagy, J. G. and O’Leary, D. P., 2008, A weighted GCV method for Lanczos hybrid regularization. ETNA, 28, 149-167.##Farquharson, C. G., 2008, Constructing piecewise-constant models in multidimensional minimum-structure inversions. Geophysics, 73, K1-K9.##Farquharson, C. G. and Oldenburg, D. W., 2004, A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems. Geophys. J. Int., 156, 411-425.##Gazzola, S. and Nagy, J. G., 2014, Generalized Arnoldi_Tikhonov method for sparse reconstruction. SIAM J. Sci. Comput., 36(2), B225-B247.##Golub, G. H., Heath, M. and Wahba, G., 1979, Generalized Cross Validation as a method for choosing a good ridge parameter. Technometrics, 21(2), 215-223.##Golub, G. H. and Van Loan, C., 1996, Matrix computation, 3rd edition, John Hopkins University Press, Baltimore.##Hansen, P. C., 2007, Regularization Tools: A Matlab package for analysis and solution of discrete ill-Posed problems version 4.1 for Matlab 7.3. Numerical Algorithms, 46, 189-194.##Kilmer, M. E. and O’Leary, D. P., 2001, Choosing regularization parameters in iterative methods for ill-posed problems. SIAM Journal on Matrix Analysis and Application, 22, 1204-1221.##Last, B. J. and Kubik, K., 1983, Compact gravity inversion. Geophysics, 48, 713-721.##Li, Y. and Oldenburg, D. W., 1996, 3-D inversion of magnetic data. Geophysics, 61, 394-408.##Li, Y. and Oldenburg, D. W., 2003, Fast inversion of large-scale magnetic data using wavelet transform and a logarithmic barrier method. Geophys. J. Int., 152, 251-265.##Liu, S., Hu, X., Xi, Y., Liu, T. and Xu, S., 2015, 2D sequential inversion of total magnitude and total magnetic anomaly data affected by remanent magnetization, Geophysics, 80, K1-K12.##Loke, M. H., Acworth, I. and Dahlin, T., 2003, A comparison of smooth and blocky inversion methods in 2D electrical imaging surveys. Exploration Geophysics, 34, 182-187.##Paige, C. C. and Saunders, M. A., 1982a, LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Trans. Math. Software, 8, 43-71.##Paige, C. C. and Saunders, M. A., 1982b, ALGORITHM 583 LSQR: Sparse linear equations and least squares problems. ACM Trans. Math. Software, 8, 195-209.##Pilkington, M., 1997, 3-D magnetic imaging using conjugate gradients. Geophysics, 62, 1132-1142.##Pilkington, M., 2009, 3D magnetic data-space inversion with sparseness constraint. Geophysics, 74, L7-L15.##Portniaguine, O. and Zhdanov, M. S., 1999, Focusing geophysical inversion images. Geophysics, 64, 874-887.##Portniaguine, O. and Zhdanov, M. S., 2002, 3-D magnetic inversion with data compression and image focusing. Geophysics, 67, 1532-1541.##Rao, D. B. and Babu, N. R., 1991, A rapid methods for three-dimensional modeling of magnetic anomalies. Geophysics, 56, 1729-1737.##Renaut R. A., Vatankhah, S. and Ardestani V. E., 2017, Hybrid and iteratively reweighted regularization by unbiased predictive risk and weighted GCV for projected systems. SIAM Journal on Scientific Computing, 39, 2, B221-B243.##Sun, J. and Li, Y., 2014, Adaptive Lp inversion for simultaneous recovery of both blocky and smooth features in geophysical model. Geophys. J. Int., 197, 882-899.##Vatankhah, S., Ardestani V. E. and Renaut, R. A., 2014, Automatic estimation of the regularization parameter in 2-D focusing gravity inversion: application of the method to the Safo manganese mine in northwest of Iran. Journal of Geophysics and Engineering, 11, 045001.##Vatankhah, S., Ardestani V. E. and Renaut, R. A., 2015, Application of the principle and unbiased predictive risk estimator for determining the regularization parameter in 3D focusing gravity inversion. Geophys. J. Int., 200(1), 265-277.##Vatankhah, S., Renaut, R. A. and Ardestani, V. E., 2017, 3-D Projected L1 inversion of gravity data using truncated unbiased predictive risk estimator for regularization parameter estimation. Geophys. J. Int., 210 (3), 1872-1887.##Voronin, S., 2012, Regularization of linear systems with sparsity constraints with application to large scale inverse problems. Ph.D. thesis, Princeton University, U.S.A.##Wohlberg, B. and Rodriguez, P., 2007, An iteratively reweighted norm algorithm for minimization of total variation functionals. IEEE Signal Processing Letters, 14, 948-951.##]
0
Application of Single-Frequency Time-Space Filtering Technique for Seismic Ground Roll and Random Noise Attenuation
Application of Single-Frequency Time-Space Filtering Technique for Seismic Ground Roll and Random Noise Attenuation
https://jesphys.ut.ac.ir/article_65887.html
10.22059/jesphys.2018.249021.1006959
0
Time-frequency filtering is an acceptable technique for attenuating noise in 2-D (time-space) and 3-D (time-space-space) reflection seismic data. The common approach for this purpose is transforming each seismic signal from 1-D time domain to a 2-D time-frequency domain and then denoising the signal by a designed filter and finally transforming back the filtered signal to original time domain. The technique is efficient for ground roll and also random noise attenuation. However, if we deal with a large data set and a great number of contaminated signals with ground roll noise, a much move consuming time will be required. In this paper, time-frequency filtering is formulated and carried out by a different approach. The data is transformed from original time-space domain into several single-frequency time-space domains, and the filters to reduce noise is designed in the new domains. The transform is easily and completely invertible. The employed time frequency analysis method is a high-resolution version of S-transform. Application to synthetic and real shot gathers confirms the good performance and efficiency of the method for attenuating ground roll noise and random noise.
1
Time-frequency filtering is an acceptable technique for attenuating noise in 2-D (time-space) and 3-D (time-space-space) reflection seismic data. The common approach for this purpose is transforming each seismic signal from 1-D time domain to a 2-D time-frequency domain and then denoising the signal by a designed filter and finally transforming back the filtered signal to original time domain. The technique is efficient for ground roll and also random noise attenuation. However, if we deal with a large data set and a great number of contaminated signals with ground roll noise, a much move consuming time will be required. In this paper, time-frequency filtering is formulated and carried out by a different approach. The data is transformed from original time-space domain into several single-frequency time-space domains, and the filters to reduce noise is designed in the new domains. The transform is easily and completely invertible. The employed time frequency analysis method is a high-resolution version of S-transform. Application to synthetic and real shot gathers confirms the good performance and efficiency of the method for attenuating ground roll noise and random noise.
41
51
Mohammad
Radad
Mohammad
Radad
Assistant Professor, Department of Petroleum and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
Iran
mradad@shahroodut.ac.ir
Time-frequency analysis
Single-frequency section
Filtering
Seismic noise
[Al-Husseini, M. I., Glover, J. B. and Barley, B. J., 1981, Dispersion patterns of the ground roll in eastern Saudi Arabia, Geophys., 46, 121-137.##Askari, R. and Siahkoohi, H. R., 2008, Ground roll attenuation using the S and x-f-k transforms. Geophys. Prospect, 56, 105-114.##Bekara, M. and van der Baan, M., 2009, Random and coherent noise attenuation by empirical mode decomposition. Geophys, 74, V89-V98.##Deighan, A. J. and Watts, D. R., 1997, Ground-roll suppression using the wavelet transform. Geophys, 62, 1896-1903.##Gabor, D., 1946, Theory of communication. J. Inst. Electr. Eng., 93, 429–457.##Jiao, S., Chen, Y., Bai, M., Yang, W., Wang, E. and Gan, S., 2015, Ground roll attenuation using non-stationary matching filtering. J. Geophys. Eng., 12, 922.##Knapp, R. W. and Steeples, D. W., 1986, High-resolution common-depth-point reflection profiling: field acquisition parameter design. Geophys., 51, 283-294.##Liu, G., Fomel, S. and Chen, X., 2011, Time-frequency analysis of seismic data using local attributes. Geophysics, 76, P23-P34.##Liu, X., 1999, Ground roll suppression using the Karhunen-Loeve transform. Geophys., 64, 564-566.##Liu, Y. and Fomel, S., 2013, Seismic data analysis using local time-frequency decomposition. Geophysical Prospecting, 61, 516-525.##Mallat, S., 1999, A Wavelet Tour of Signal Processing. Second ed., Academic Press, San Diego, California.##Montagne, R. and Vasconcelos, G. L., 2006, Optimized suppression of coherent noise from seismic data using the karhunen-Loeve transform. Phys. Rev. E., 74, 1-9.##Porsani, M. J., Silva, M. G., Melo, P. E. M. and Ursin, B., 2010, SVD filtering applied to ground-roll attenuation. J. Geophys. Eng., 7, 284.##Radad, M., Gholami, A. and Siahkoohi, H. R., 2015, S-transform with maximum energy concentration: application to non-stationary seismic deconvolution. J. Appl. Geophys., 118, 155-166.##Radad, M., Gholami, A. and Siahkoohi, H. R., 2016, A fast method for generating high-resolution single-frequency seismic attributes. J. Seism. Explor., 25, 11-25.##Stockwell, R. G., Mansinha, L. and Lowe, R., 1996, Localization of the complex spectrum: The S-transform. IEEE Trans. Sig. Process., 44, 998-1001.##Tartham, R. H., Keeney, J. W. and Noponen, I., 1983, Application of the tau-p transform (slant-stack) in processing seismic reflection data. Explor. Geophys., 14, 163-172.##Wang, Y., 2010, Multichannel matching pursuit for seismic trace decomposition. Geophys., 75, V61-V66.##Wiggins, R. A., 1966, W-K filter design. Geophys. Prospect., 14, 427-440.##Wu, X., and Liu, T., 2009, Spectral decomposition of seismic data with reassigned smoothed pseudo Wigner-Ville distribution. J. Appl. Geophys., 68, 386-393.##Yarham, C., Boeniger, U. and Herrmann, F. J., 2006, Curvelet-based ground roll removal. 76th Annual International Meeting, SEG, 2777-2780.##Yilmaz, O., 2001, Seismic data analysis. SEG, Tulsa, OK.##]
0
Sensitivity analysis of time lapse gravity for monitoring fluid saturation changes in a giant multi-phase gas reservoir located in south of Iran
Sensitivity analysis of time lapse gravity for monitoring fluid saturation changes in a giant multi-phase gas reservoir located in south of Iran
https://jesphys.ut.ac.ir/article_65878.html
10.22059/jesphys.2018.249232.1006961
0
The time lapse gravity method is a widely used technique to monitor the subsurface density changes in time and space. In hydrocarbon reservoirs, the density variations are due to different factors, such as: substitution of fluids with high density contrast, water influx, gas injection, and the variation in reservoir geomechanical behavior. Considering the monitoring of saturation changes in the reservoir that cannot be inferred directly by seismic survey, a forward modelling followed by a sensitivity study is performed to examine that in what conditions the saturation changes are detectable by means of 4D gravity method in the understudy reservoir. Then static and dynamic models of a giant multi-phase gas reservoir are constructed. Then, synthetic gravity data are generated after variation of production time intervals and the number of production and injection wells. In addition to detecting the gravity signal for shallower reservoirs with similar characteristics to our reservoir, a sensitivity analysis was conducted for variation in depth of the reservoir. As either the depth of the reservoir decreases or the number of the production wells and production time periods increases, the produced gravity signal is more prone to be detectable by means of modern offshore gravimeters. The gravity signal could be detected with the maximum magnitude range of 9 - in different scenarios as a consequence of gas-water substitution, which is consistent with water drive support from surrounding aquifers. Therefore, this method is applicable for providing complementary and even independent source of information about the saturation front changes in the under-study reservoir.
1
The time lapse gravity method is a widely used technique to monitor the subsurface density changes in time and space. In hydrocarbon reservoirs, the density variations are due to different factors, such as: substitution of fluids with high density contrast, water influx, gas injection, and the variation in reservoir geomechanical behavior. Considering the monitoring of saturation changes in the reservoir that cannot be inferred directly by seismic survey, a forward modelling followed by a sensitivity study is performed to examine that in what conditions the saturation changes are detectable by means of 4D gravity method in the understudy reservoir. Then static and dynamic models of a giant multi-phase gas reservoir are constructed. Then, synthetic gravity data are generated after variation of production time intervals and the number of production and injection wells. In addition to detecting the gravity signal for shallower reservoirs with similar characteristics to our reservoir, a sensitivity analysis was conducted for variation in depth of the reservoir. As either the depth of the reservoir decreases or the number of the production wells and production time periods increases, the produced gravity signal is more prone to be detectable by means of modern offshore gravimeters. The gravity signal could be detected with the maximum magnitude range of 9 - in different scenarios as a consequence of gas-water substitution, which is consistent with water drive support from surrounding aquifers. Therefore, this method is applicable for providing complementary and even independent source of information about the saturation front changes in the under-study reservoir.
53
61
Abdolmalek
Khosravi
Abdolmalek
Khosravi
M.Sc. Graduated, Department of Earth Physics, Institute of Geophysics, University of Tehran, Iran
Iran
khosravi320@ut.ac.ir
Seyed Hani
Motavalli-Anbaran
Seyed Hani
Motavalli-Anbaran
Assistant Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Iran
Iran
motavalli@ut.ac.ir
Sajad
Sarallah- Zabihi
Sajad
Sarallah- Zabihi
Expert, National Iranian Oil Company, Exploration Directorate, Tehran, Iran
Iran
sa.zabihi@gmail.com
Mohammad
Emami Niri
Mohammad
Emami Niri
Assistant Professor, Institute Of Petroleum Engineering, College of Engineering, University of Tehran, Iran
Iran
emami.m@ut.ac.ir
4D gravity
Water influx
Aquifer
Fluid saturation
[Alnes, H., Eiken, O., Nooner, S., Sasagava, G., Stenvold, T. and Zumberge, M., 2011, Results from Sliepner gravity monitoring: updated density and tempreture distribution of CO2 plume. Energy Procedia, 4, 5504-5511.##Brady, J. L., Hare, J. L., Ferguson, J. F., Seibert, J. E., Klopping, F. J., Chen, T. and Niebauer, T., 2008, Results of the worlds first 4D microgravity surveillance of a waterflood-Prudhoe Bay, Alaska. Society of Petroleum Engineers Reservoir Evaluation and Engineering, 11, 824-831.##Eiken, O., Stenvold, T., Zumberge, M., Alnes, H. and Sasagawa, G., 2008, Gravimetric monitoring of gas production from the Troll field. Geophysics, 73(6), WA149-WA145.##Eiken, O., Tollefsen, F., Aanvik, F. and Alson, T., 2005, Surface geophysical monitoring of gas field. 6th Petroleum Geology Conference, 641-650.##Eiken, O., Zumberge, M. A. and Hildebrand, J., 2003, Method for monitoring seafloor subsidence and for gravity monitoring an underground hydrocarbon reservoir. USA: University of California, 2, 377500.##Ferguson, J. F., Chen, T., Brady, J., Aiken, C. L. V. and Seibert, J., 2007, The 4D microgravity method for waterflood surveillance: part 2- Gravity measurments for the Prodhoe Ban reservoir, Alaska. Geophysics, 72(2), 33-43.##Gelderen, M. V., Haagmans, R. and Bilker, M., 1999, Gravity change and natural gas extraction in Groningen. Geophysical Prospecting, 47.##Glegola, M., Ditmar, P., Bierkens, R. and Vossepoel, F., 2009, Estimation of time-lapse gravity errors due to water table and soil moisture variations. 79th Annual International Meeting, SEG, 976-980.##Hare, J. L., Ferguson, J. F. and Brady, J. L., 2008, The 4D microgravity method for water surveillance: part 4 - Mideling and interpretation of early epoch 4D gravity surveys at Prudoe Ban, Alaska. Geophysics.##Ruiz, H., Agersborg, R., Hille, L. T., Lien, M. and Even, J., 2016, Monitoring offshore reservoirs using 4D gravity and subsidence with improved tide corrections. SEG International Exposition and 86th Annual Meeting.##Sarkowi, M., Kadir, G. and Santoso, D., 2005, Strategy of 4D Microgravity Survey for the Monitoring of Fluid Dynamics in the Subsurface. Antalya, Turkey, World Geothermal Congress, April, 2005. Siddique, M., 2011, Application of Time Lapse Gravity in History Matching a Gas/Condensate Field. EAGE/SPE Joint Workshop, 04 April 2011.##Stenvold, T., Eiken, O. and landro, M., 2008, Gravimetric monitoring of gas reservoir water influx- A combined gravity and flow modeling approach. Geophysics, 73(6).Taherniya, M., Ghazifard, A., Fatemi Aghda, M., 2013, Predicting subsidence in the South Pars gas field. EJGE, 18.##Tempone, p., Landro, M. and Fajer, E., 2012, 4D gravity response of compacting reservoir: analytical approach. Geophysics, May-June, 77(3).##Van den Beukel, A., 2014, Integrated Reservoir Monitoring of the Ormen Lange field: Time lapse seismic, Time lapse gravity and seafloor deformation monitoring. The Biennial Geophysical Seminar NPF, Extended Abstracts.##Zumberge, G., Stenvold, T., Eiken, O., Sasagawa, S. and Nooner, L., 2006, Precision of seafloor gravity and pressure measurements for reservoir monitoring Geophysics, 73(6).##Emami Niri, M. and Lumley, D. E., 2016, Probabilistic reservoir-property modeling jointly constrained by 3D-seismic data and hydraulic-unit analysis. Reservoir Evaluation and Engineering, 19(2), 253-264.##]
0
Location and dimensionality estimation of geological bodies using eigenvectors of "Computed Gravity Gradient Tensor"
Location and dimensionality estimation of geological bodies using eigenvectors of "Computed Gravity Gradient Tensor"
https://jesphys.ut.ac.ir/article_65888.html
10.22059/jesphys.2018.253742.1006984
0
One of the methodologies employed in gravimetry exploration is eigenvector analysis of Gravity Gradient Tensor (GGT) which yields a solution including an estimation of a causative body’s Center of Mass (COM), dimensionality and strike direction. The eigenvectors of GGT give very rewarding clues about COM and strike direction. Additionally, the relationships between its components provide a quantity (I), representative of a geologic body dimensions. Although this procedure directly measures derivative components of gravity vector, it is costly and demands modern gradiometers. This study intends to obtain GGT from an ordinary gravity field measurement (gz). This Tensor is called Computed GGT (CGGT). In this procedure, some information about a geologic mass COM, strike and rough geometry, just after an ordinary gravimetry survey, is gained. Because of derivative calculations, the impacts of noise existing in the main measured gravity field (gz) could be destructive in CGGT solutions. Accordingly, to adjust them, a “moving twenty-five point averaging” method, and “upward continuation” are applied. The methodology is tested on various complex isolated and binary models in noisy conditions. It is also tested on real geologic example from a salt dome, USA, and all the results are highly acceptable.
1
One of the methodologies employed in gravimetry exploration is eigenvector analysis of Gravity Gradient Tensor (GGT) which yields a solution including an estimation of a causative body’s Center of Mass (COM), dimensionality and strike direction. The eigenvectors of GGT give very rewarding clues about COM and strike direction. Additionally, the relationships between its components provide a quantity (I), representative of a geologic body dimensions. Although this procedure directly measures derivative components of gravity vector, it is costly and demands modern gradiometers. This study intends to obtain GGT from an ordinary gravity field measurement (gz). This Tensor is called Computed GGT (CGGT). In this procedure, some information about a geologic mass COM, strike and rough geometry, just after an ordinary gravimetry survey, is gained. Because of derivative calculations, the impacts of noise existing in the main measured gravity field (gz) could be destructive in CGGT solutions. Accordingly, to adjust them, a “moving twenty-five point averaging” method, and “upward continuation” are applied. The methodology is tested on various complex isolated and binary models in noisy conditions. It is also tested on real geologic example from a salt dome, USA, and all the results are highly acceptable.
63
71
Korosh
Karimi
Korosh
Karimi
M.Sc. Student, Department of Physics, faculty of Science, Razi University, Kermanshah, Iran
Iran
kuroshkarimi88@gmail.com
Mohsen
Oveisy Moakhar
Mohsen
Oveisy Moakhar
Assistant Professor, Department of Physics, faculty of Science, Razi University, Kermanshah, Iran
Iran
m_oveisy@razi.ac.ir
Farzad
Shirzaditabar
Farzad
Shirzaditabar
Assistant Professor, Department of Physics, faculty of Science, Razi University, Kermanshah, Iran
Iran
f.shirzadi@razi.ac.ir
Computed Gravity Gradient Tensor (CGGT)
Dimensionality Index (I)
Eigenvector
eigenvalue
[Abdelrahman, E. M. and El-Araby, T. M., 1996, Shape and depth solutions from moving average residual gravity anomalies. Journal of Applied Geophysics, 36, 89-95.##Aster, R. C., Borchers, B. and Thurber, C., 2003, Parameter Estimation and Inverse Problems. Elsevier.##Bell, R. E., Anderson, R. and Pratson, L., 1997, Gravity gradiometry resurfaces. The Leading Edge, 16, 55–59.##Bell, R. E. and Hansen, R. O., 1998, The rise and fall of early oil field technology: The torsion balance gradiometer. The Leading Edge, 17, 81-83.##Beiki, M. and Pedersen, L. B., 2010, Eigenvector analysis of the gravity gradient tensor to locate geologic bodies. Geophysics, 75(6), I37–I49.##Childers, V. A., Bell, R. E. and Brozena, J. M., 1999, Airborne gravimetry: An investigation of filtering. Geophysics, 64, 61–69.##Dransfield, M. H., 2007, Airborne gravity gradiometry in the research for mineral deposits. Proceedings of Exploration 07: Fifth Decennial International Conference on Mineral Exploration, edited by Milkereit, B., 341-354.##Droujinine, A., Vasilevsky, A. and Evans, R., 2007, Feasibility of using full tensor gradient FTG data for detection of local lateral density contrasts during reservoir monitoring. Geophysical Journal International, 169, 795– 820.##Edwards, A. J., J. Maki, T. and Peterson, D. G., 1997, Gravity gradiometry as a tool for underground facility detection. Journal of Environmental & Engineering Geophysics, 2(2), 137–143.##Fedi, M., Ferranti, L., Florio, G., Giori, I. and Italiano, F., 2005, Understanding the structural setting in the southern Apennines Italy: Insight from gravity gradient tensor. Tectonophysics, 397(1–2), 21–36.##Fitz Gerald, D., Argast, D., Paterson, R. and Holstein, H., 2009, Full tensor magnetic gradiometry processing and interpretation developments. 11th South African Geophysical Association SAGA.##Hatch, D., 2004, Evaluation of a full tensor gravity gradiometer for kimberlite exploration. The ASEG-PESA Airborne gravity workshop, Extended Abstracts, 73–80.##Hinojosa, J. H. and Mickus, K. L., 2002, Hilbert transform of gravity gradient profiles: Special cases of the general gravity-gradient tensor in the Fourier transform domain. Geophysics, 67(3), 766–769.##Mikhailov, V., Pajot, G., Diament, M. and Price, A., 2007, Tensor deconvolution: A method to locate equivalent sources from full tensor gravity data. Geophysics, 72(5), I61–I69.##Murphy, C. A. and Brewster, J., 2007, Target delineation using full tensor gravity gradiometry data. Extended Abstract, ASEG-PESA 19th International Geophysical Conference and Exhibition, Perth, Australia.##Nabighian, M. N., 1984, Toward a three-dimensional automatic interpretation of potential field data via generalized Hilbert transforms: Fundamental relations. Geophysics, 49(6), 780-786.##Nettleton, L. L., 1962, Gravity and magnetic for geologists and seismologists. AAPG Bulletin, 46, 1815-1838.##Nettelton, L. L., 1976, Gravity and Magnetics in oil prospecting. McGraw-Hill, New York.##Oruc, B., 2010, Depth Estimation of Simple Causative Sources from Gravity Gradient Tensor Invariants and Vertical Component. Pure. Appl. Geophys. 167, 1259–1272.##Pajot, G., de Viron, O., Diament, M., Lequentrec-Lalancette, M. F. and Mikhailov, V., 2008, Noise reduction through joint processing of gravity and gravity gradient data. Geophysics, 73(3), I23–I34.##Pedersen, L. B. and Rasmussen, T. M., 1990, The gradient tensor of potential field anomalies: Some implications on data collection and data processing of maps. Geophysics, 55, 1558–1566.##Pawlowski, B., 1998, Gravity gradiometry in resource exploration. The Leading Edge, 17, 51–52.##Routh, P., Jorgensen, G. J. and Kisabeth, J. L., 2001, Base of the salt mapping using gravity and tensor gravity data. 70th Annual International Meeting, SEG, Expanded Abstracts, 1482–1484.##Vasco, O. W. and Taylor, C., 1991, Inversion of airborne gravity gradient data, southwestern Oklahoma. Geophysics, 56, 90–91.##While, J., Biegert, E. and Jackson, A., 2009, Generalized sampling interpolation of noise gravity/gravity gradient data. Geophysical Journal International, 178, 638–650.##While, J., Jackson, A., Smit, D. and Biegert, E., 2006, Spectral analysis of gravity gradiometry profiles. Geophysics, 71(1), J11–J22.##Zhdanov, M. S., Ellis, R. and Mukherjee, S., 2004, Three-dimensional regularized focusing inversion of gravity gradient tensor component data. Geophysics, 69, 925–937.##Zhang, C., Mushayandebvu, M. F., Reid, A. B., Fairhead, J. D. and Odegard, M., 2000, Euler deconvolution of gravity tensor gradient data. Geophysics, 65, 512–520.##Zhou, W., 2016, Depth Estimation Method Based on the Ratio of Gravity and Full Tensor Gradient Invariant. Pure. Appl. Geophys, 173(2), 499-508.##]
0
Attenuation of spatial aliasing in CMP domain by non-linear interpolation of seismic data along local slopes
Attenuation of spatial aliasing in CMP domain by non-linear interpolation of seismic data along local slopes
https://jesphys.ut.ac.ir/article_67749.html
10.22059/jesphys.2018.257443.1007005
0
Spatial aliasing is an unwanted side effect that produces artifacts during seismic data processing, imaging and interpolation. It is often caused by insufficient spatial sampling of seismic data and often happens in CMP (Common Mid-Point) gather. To tackle this artifact, several techniques have been developed in time-space domain as well as frequency domain such as frequency-wavenumber, frequency-space, and frequency-time. The main advantages of seismic interpolation in time-space domain over frequency domain are: a) frequency components of the initial signals are preserved, and b) the prior knowledge that a seismic event consists of many plane wave segments, can be used. Using the later advantage, a seismic event can be predicted by pursuing the continuity of seismic events in a trace-by-trace manner. This process, which has become popular in seismic data reconstruction and imaging within the past few years, is known as predictive painting. We use predictive painting to predict the wavefronts and two-way-travel time curves in regularly sampled CMP gathers followed by increasing the number of traces by cubic interpolation. Then, the amplitude of the interpolated trace is obtained by averaging the amplitudes of the neighbouring traces. Performance of the proposed method is demonstrated on several synthetic seismic data examples as well as a field data set.
1
Spatial aliasing is an unwanted side effect that produces artifacts during seismic data processing, imaging and interpolation. It is often caused by insufficient spatial sampling of seismic data and often happens in CMP (Common Mid-Point) gather. To tackle this artifact, several techniques have been developed in time-space domain as well as frequency domain such as frequency-wavenumber, frequency-space, and frequency-time. The main advantages of seismic interpolation in time-space domain over frequency domain are: a) frequency components of the initial signals are preserved, and b) the prior knowledge that a seismic event consists of many plane wave segments, can be used. Using the later advantage, a seismic event can be predicted by pursuing the continuity of seismic events in a trace-by-trace manner. This process, which has become popular in seismic data reconstruction and imaging within the past few years, is known as predictive painting. We use predictive painting to predict the wavefronts and two-way-travel time curves in regularly sampled CMP gathers followed by increasing the number of traces by cubic interpolation. Then, the amplitude of the interpolated trace is obtained by averaging the amplitudes of the neighbouring traces. Performance of the proposed method is demonstrated on several synthetic seismic data examples as well as a field data set.
73
85
Mohammad Javad
Khoshnavaz
Mohammad Javad
Khoshnavaz
Post-Doc, Department of Earth Physics, Institute of Geophysics, University of Tehran, Iran
Iran
mj.khoshnavaz@ut.ac.ir
Hamid Reza
Siahkoohi
Hamid Reza
Siahkoohi
Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Iran
Iran
hamid@ut.ac.ir
Andrej
Bóna
Andrej
Bóna
Professor, Department of Exploration Geophysics, Curtin University of Technology, Perth, Western Australia
Iran
a.bona@curtin.edu.au
Spatial aliasing
interpolation
time-space
local slope
predictive painting
[Bóna, A., 2011, Shot-gather time migration of planar reflectors without velocity model. Geophysics, 76(2), S93–S101, doi: 10.1190/1.3549641.##Burnett, W. and Fomel, S., 2009, 3D velocity-independent elliptically anisotropic move out correction. Geophysics, 74(5), WB129–WB136, doi: 10.1190/1.3184804.##Casasanta, L. and Fomel, S., 2011, Velocity-independent τ-p move out in a horizontally layered VTI medium. Geophysics, 76(4), U45-U57, doi: 10.1190/1.3595776.##Chen, Y., Fomel, S. and Hu, J., 2014, Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization. Geophysics, 79, V183-V193, doi: 10.1190/GEO2013-0449.1.##Chen, Y., Zhang, L. and Mo, L., 2015, Seismic data interpolation using nonlinear shaping regularization. Journal of Seismic Exploration, 24(5), 327-342, http://www.geophysical-press.com/contents_jse_vol_24_4.htm.##Claerbout, J. F., 1985, Imaging the Earth’s Interior. Blackwell Scientific Publications, Inc., http://sepwww.stanford.edu/sep/prof/iei2.##Claerbout, J. F., 1992, Earth Soundings Analysis: Processing Versus Inversion. Blackwell Scientific Publications, http://sepwww.stanford.edu/sep/prof/pvi.pdf.##Crawley, S., 2000, Seismic trace interpolation with nonstationary prediction error filters: Ph.D. thesis, Stanford University.##Fomel, S., 2002, Applications of plane-wave destruction filters. Geophysics, 67, 1946–1960, doi: 10.1190/1.1527095.##Fomel, S., 2003, Seismic reflection data interpolation with differential offset and shot continuation. Geophysics, 68, 733–744, doi: 10.1190/1.1567243.##Fomel, S., 2007, Velocity-independent time-domain seismic imaging using local event slopes: Geophysics, 72(3), S139–S147, doi: 10.1190/1.2714047.##Fomel, S., 2010, Predictive painting of 3D seismic volumes. Geophysics, 75(4), A25–A30, doi: 10.1190/1.3453847.##Fomel, S., Sava, P., Vlad, I., Liu, Y. and Bashkardin, V., 2013, Madagascar: open-source software project for multidimensional data analysis and reproducible computational experiments. Journal of Open Research Software, 1(1), p. e8, doi: oftware.metajnl.com/articles/10.5334/jors.ag/##Gan, S., Wang, S., Chen, Y., Jin, Z. and Zhang, Y., 2015, Dealiased Seismic Data Interpolation Using Seislet Transform With Low-Frequency Constraint, IEEE Geoscience and Remote Sensing Letters, 12, 2150-2154, doi: 10.1109/LGRS.2015.2453119.##Gan, S., Chen, Y., Wang, S., Chen, X., Huang, W. and Chen, H., 2016, Compressive sensing for seismic data reconstruction using a fast projection onto convex sets algorithm based on the seislet transform. Journal of Applied Geophysics, 130, 194-208, doi: 10.1016/j.jappgeo.2016.03.033.##Gülünay, N., 2003, Seismic trace interpolation in the Fourier transform domain. Geophysics, 68, 355–369, doi: 10.1190/1.1543221.##Herrmann, F. J. and Hennenfent, G., 2008, Non-parametric seismic data recovery with curvelet frames: Geophysical Journal International, 173(1), 233–248, doi: 10.1111/j.1365-246X.2007.03698.x.##Ibrahim, A., Terenghi, P. and Sacchi, M. D. , 2015, Wavefield Reconstruction using a Stolt-Based Asymptote and Apex Shifted Hyperbolic Radon Transform: 55th Annual International Meeting, SEG, Expanded Abstracts, 3836-3841, doi: 10.1190/segam2015-5873567.1.##Karimi, P., 2015, Structure-constrained relative acoustic impedance using stratigraphic coordinates. Geophysics, 80(3), A63–A67, doi: 10.1190/GEO2014-0439.1.##Karimi, P., Fomel, S., Wood, L. and Dunlap, D., 2015, Predictive coherence: Interpretation, 3(4), SAE1–SAE7, doi: 10.1190/INT-2015-0030.1.##Khoshanavaz, M. J., Bóna, A., Urosevic, M., Dzunic, A. and Ung, K., 2016a, Oriented prestack time migration using local slopes and predictive painting in common-source domain for planar reflectors. Geophysics, 81(6), S409–S418, doi: 10.1190/GEO2016-0127.1.##Khoshanavaz, M. J., A. Bóna, and Urosevic, M., 2016b, Velocity-independent estimation of kinematic attributes in vertical transverse isotropy media using local slopes and predictive painting. Geophysics, 81(5), U73-U85, doi: 10.1190/GEO2015-0638.1.##Khoshnavaz, M. J., 2017, Oriented time-domain dip move out correction for planar reflectors in common-source domain. Geophysics, 82(6), U87-U97, doi: 10.1190/geo2016-0577.1##Leggott, R. J., Wombell, R., Conroy, G., Noss, T. and Williams, G., 2007, An efficient least-squares migration: 69th Conference and Exhibition, EAGE, Expanded Abstracts, P178, doi: 10.3997/2214-4609.201401856.##Liu, Y. and Fomel, S., 2010, OC-seislet: Seislet transform construction with differential offset continuation. Geophysics, 75(6), WB235–WB245, doi: 10.1190/1.3479554.##Liu, Y. and Fomel, S., 2011, Seismic data interpolation beyond aliasing using regularized nonstationary auto regression. Geophysics, 76(5), V69–V77, doi: 0.1190/GEO2010-0231.1.##Lu, L., 1985, Application of local slant-stack to trace interpolation: 55th Annual International Meeting, SEG, Expanded Abstracts, 560–562, doi: 10.1190/1.1892818.##Naghizadeh, M. and Sacchi, M. D. , 2007, Multistep autoregressive reconstruction of seismic records. Geophysics, 72(6), V111–V118, doi: 10.1190/1.2771685.##Naghizadeh, M. and Sacchi, M. D., 2010, Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data. Geophysics, 75(6), WB189–WB202, doi: 10.1190/1.3509468.##Porsani, M., 1999, Seismic trace interpolation using half-step prediction filters. Geophysics, 64, 1461–1467, doi: 10.1190/1.1444650.##Ronen, J., 1987, Wave-equation trace interpolation. Geophysics, 52, 973–984, doi: 10.1190/1.1442366.##Sacchi, M. D., Verschuur, D. J. and Zwartjes, P. M., 2004, Data reconstruction by generalized deconvolution: SEG, Expanded Abstracts, 23, 1989–1992, doi: 10.1190/1.1843303.##Shannon, C. E., 1948, A Mathematical Theory of Communication: Bell System Technical Journal, 27(3), 379–423, doi:10.1002/j.1538-7305.1948.tb01338.x.##Spitz, S., 1991, Seismic trace interpolation in the F-X domain. Geophysics, 56, 785–794, doi: 10.1190/1.1443096.##Stolt, R. H., 2002, Seismic data mapping and reconstruction. Geophysics, 67, 890–908, doi: 10.1190/1.1484532.##Trad, D., Ulrych, T. J. and Sacchi, M. D., 2002, Accurate interpolation with high-resolution time-variant Radon transforms. Geophysics, 67, 644–656, 10.1190/1.1468626.##Trickett, S. R., 2003, F-xy eigenimage noise suppression. Geophysics, 68, 751–759, doi: 10.1190/1.1567245.##Turner, G., 1990, Aliasing in the τ-p transform and the removal of spatially aliased coherent noise. Geophysics, 55, 1496–1503, doi: 10.1190/1.1442797.##Wang, J., Ng, M. and Perz, M., 2009, Fast high-resolution Radon transforms by greedy least-squares method. SEG, Expanded Abstracts, 28, 3128–3132, doi: 10.1190/1.3255506.##Yilmaz, O., 2001, Seismic data analysis. SEG, doi: 10.1190/1.9781560801580.##Yu, Z., Ferguson, J., McMechan, G. and Anno, P., 2007, Wavelet-Radon domain dealiasing and interpolation of seismic data. Geophysics, 72(2), V41–V49, 10.1190/1.2422797.##Zwartjes, P. M. and Sacchi, M. D. , 2007, Fourier reconstruction of nonuniformly sampled, aliased seismic data. Geophysics, 72(1), V21–V32, doi: 10.1190/1.2399442.##]
0
Kalman filter and Neural Network methods for detecting irregular variations of TEC around the time of powerful Mexico (Mw=8.2) earthquake of September 08, 2017
Kalman filter and Neural Network methods for detecting irregular variations of TEC around the time of powerful Mexico (Mw=8.2) earthquake of September 08, 2017
https://jesphys.ut.ac.ir/article_67738.html
10.22059/jesphys.2018.258251.1007007
0
In 98 km SW of Tres Picos in Mexico (15.022°N, 93.899°W, 47.40 km depth) a powerful earthquake of Mw=8.2 took place at 04:49:19 UTC (LT=UTC-05:00) on September 8, 2017. In this study, using three standard, classical and intelligent methods including median, Kalman filter, and Neural Network, respectively, the GPS Total Electron Content (TEC) measurements of three months were surveyed to detect the potential unusual variations around the time and location of Mexico earthquake. Every three implemented methods indicated a striking irregular variation of TEC at the earthquake time. However, on the earthquake day, the geomagnetic indices Dst and Ap have exceeded the allowed ranges and even reached maximum values during the studied time period. Besides, the solar index of F10.7 showed high activity around the earthquake day. Therefore, it is difficult to acknowledge the seismicity nature of the detected TEC unusual variations on earthquake day. Therefore, in this case, we encounter a mixed and complex behavior of ionosphere.
1
In 98 km SW of Tres Picos in Mexico (15.022°N, 93.899°W, 47.40 km depth) a powerful earthquake of Mw=8.2 took place at 04:49:19 UTC (LT=UTC-05:00) on September 8, 2017. In this study, using three standard, classical and intelligent methods including median, Kalman filter, and Neural Network, respectively, the GPS Total Electron Content (TEC) measurements of three months were surveyed to detect the potential unusual variations around the time and location of Mexico earthquake. Every three implemented methods indicated a striking irregular variation of TEC at the earthquake time. However, on the earthquake day, the geomagnetic indices Dst and Ap have exceeded the allowed ranges and even reached maximum values during the studied time period. Besides, the solar index of F10.7 showed high activity around the earthquake day. Therefore, it is difficult to acknowledge the seismicity nature of the detected TEC unusual variations on earthquake day. Therefore, in this case, we encounter a mixed and complex behavior of ionosphere.
87
97
Mehdi
Akhoondzadeh Hanzaei
Mehdi
Akhoondzadeh Hanzaei
Assistant Professor, Department of Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran
Iran
makhonz@ut.ac.ir
Earthquake Precursor
Ionosphere
Geomagnetic activity
GPS
Mexico earthquake
TEC
[Akhoondzadeh, M., 2011, Comparative study of the earthquake precursors obtained from satellite data. PhD thesis, University of Tehran, Surveying and Geomatics Engineering Department, Remote Sensing Division.##Akhoondzadeh, M., 2012, Anomalous TEC variations associated with the powerful Tohoku earthquake of 11 March 2011. Nat. Hazards Earth Syst. Sci., 12, 1453-1462, doi:10.5194/nhess-12-1453-2012.##Akhoondzadeh, M., 2013, A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies. Advances in Space Research, 51, 2048-2057.##Akhoondzadeh, M., De Santis, A., Marchetti, D., Piscini, A. and Cianchini, G., 2018, Multi precursors analysis associated with the powerful Ecuador (MW=7.8) earthquake of 16 April 2016 using Swarm satellites data in conjunction with other multi-platform satellite and ground data. Advances in Space Research, 61, 248-263.##Freund, F., 2009, Stress-activated positive hole charge carriers in rocks and the generation of pre-earthquake signals. In Electromagnetic phenomena associated with earthquakes, Ed. by M. Hayakawa, Transworld Research Network, Trivandrum, 41-96.##Hayakawa, M. and Molchanov, O. A., 2002, Seismo- Electromagnetics: Lithosphere-Atmosphere-Ionosphere Coupling. Terra Scientific Publishing Co. Tokyo, 477.##Haykin, S., 2001, Kalman filtering and Neural networks, John Wiley & Sons, Inc, McMaster University, Hamilton, Canada, 1-284.##Klimenko, M. V., Klimenko, V. V., Zakharenkova, I. E. and Pulinets, S. A., 2012, Variations of equatorial electrojet as possible seismo-ionospheric precursor at the occurrence of TEC anomalies before strong earthquake, Advances in Space Research, 49 (3), 509-517.##Liu, J. Y., Chuo, Y. J., Shan, S. J., Tsai, Y. B., Pulinets, S. A. and Yu, S. B., 2004, Pre-earthquake-ionospheric anomalies registered by continuous GPS TEC, Ann. Geophys., 22, 1585-1593.##Mannucci, A. J., Wilson, B. D., Yuan, D. N., Ho, C. H., Lindqwister, U. J. and Runge, T. F., 1998, A global mapping technique for GPS-derived ionospheric total electron content measurements, Radio Sci., 33, 565-582, doi: 10.1029/97RS02707.##Mayaud, P. N., 1980, Derivation, Meaning and use of geomagnetic indices, Geophy, 22, American Geo. Union, Washington, D. C.##Namgaladze, A. A., Zolotov, O. V. and Prokhorov, B. E., 2013, Numerical Simulation of the Variations in the Total Electron Content of the Ionosphere Observed before the Haiti Earthquake of January 12, 2010, Geomagnetism and Aeronomy, 53 (4), 553-560.##Pao, H. T, 2007, Forecasting electricity market pricing using artificial neural networks, Energy Conversion and Management, 48, 907–912.##Paoli, C., Voyant, C., Muselli, M. and Nivet, M. L., 2010, Forecasting of preprocessed daily solar radiation time series using neural networks, Solar Energy, 84, 2146–2160.##Parrot, M., 1995, Use of satellites to detect seismo-electromagnetic effects, Main phenomenological features of ionospheric precursors of strong earthquakes, Advances in Space Research, 15 (11), 1337-1347.##Pulinets, S. A. and Boyarchuk, K. A., 2004, Ionospheric Precursors of Earthquakes, Springer, Berlin.##Pulinets S. A., 2009, Physical mechanism of the vertical electric field generation over active tectonic faults, Adv. Space Res., 44, 767-773.##Pulinets S. and Ouzounov D., 2011, Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model - an unified concept for earthquake precursors validation, Journal of Asian Earth Sciences, 41, 371-382.##Qiang, Z. J., Xu, X. D. and Dian, C. G., 1991, Thermal infrared irregular variation precursor of impending earthquakes, Chinese Sciences Bulletin, 36, 319-323.##Sorokin, V. M. and Pokhotelov, O.A., 2014, Model for the VLF/LF radio signal anomalies formation associated with earthquakes, Advances in Space Research, 54 (12), 2532-2539.##Zhang, G. P., 2001, An investigation of neural networks for linear time-series forecasting, Computers & Operations Research, 28, 1183-1202.##]
0
Application of Wavelet Neural Networks for Improving of Ionospheric Tomography Reconstruction over Iran
Application of Wavelet Neural Networks for Improving of Ionospheric Tomography Reconstruction over Iran
https://jesphys.ut.ac.ir/article_67736.html
10.22059/jesphys.2018.245567.1006940
0
In this paper, a new method of ionospheric tomography is developed and evaluated based on the neural networks (NN). This new method is named ITNN. In this method, wavelet neural network (WNN) with particle swarm optimization (PSO) training algorithm is used to solve some of the ionospheric tomography problems. The results of ITNN method are compared with the residual minimization training neural network (RMTNN) and modified RMTNN (MRMTNN). In all three methods, empirical orthogonal functions (EOFs) are used as a vertical objective function. To apply the methods for constructing a 3D-image of the electron density, GPS measurements of the Iranian permanent GPS network (in three days in 2007) are used. Besides, two GPS stations from international GNSS service (IGS) are used as test stations. The ionosonde data in Tehran (φ=35.73820, λ=51.38510) has been used for validating the reliability of the proposed methods. The minimum RMSE for RMTNN, MRMTNN, ITNN are 0.5312, 0.4743, 0.3465 (1011ele./m3) and the minimum bias are 0.4682, 0.3890, and 0.3368 (1011ele./m3) respectively. The results indicate the superiority of ITNN method over the other two methods.
1
In this paper, a new method of ionospheric tomography is developed and evaluated based on the neural networks (NN). This new method is named ITNN. In this method, wavelet neural network (WNN) with particle swarm optimization (PSO) training algorithm is used to solve some of the ionospheric tomography problems. The results of ITNN method are compared with the residual minimization training neural network (RMTNN) and modified RMTNN (MRMTNN). In all three methods, empirical orthogonal functions (EOFs) are used as a vertical objective function. To apply the methods for constructing a 3D-image of the electron density, GPS measurements of the Iranian permanent GPS network (in three days in 2007) are used. Besides, two GPS stations from international GNSS service (IGS) are used as test stations. The ionosonde data in Tehran (φ=35.73820, λ=51.38510) has been used for validating the reliability of the proposed methods. The minimum RMSE for RMTNN, MRMTNN, ITNN are 0.5312, 0.4743, 0.3465 (1011ele./m3) and the minimum bias are 0.4682, 0.3890, and 0.3368 (1011ele./m3) respectively. The results indicate the superiority of ITNN method over the other two methods.
99
114
Mir Reza
Ghaffari Razin
Mir Reza
Ghaffari Razin
Assistant Professor, Department of Surveying Engineering, Arak University of Technology, Arak, Iran
Iran
mr.ghafari@arakut.ac.ir
Behzad
Voosoghi
Behzad
Voosoghi
Associate Professor, Department of Geodesy, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi Univ. of Technology, Tehran, Iran
Iran
vosoghi@kntu.ac.ir
Tomography
RMTNN
MRMTNN
ITNN
GPS
[Amerian, Y., Mashhadi Hossainali, M., Voosoghi, B. and Ghaffari razin, M. R, 2010, Tomographic reconstruction of the ionospheric electron density in term of wavelets. Journal of Aerospace Science and Technology, 7(1), 19–29.##Austen, J. R., Franke, S. J. and Liu, C. H., 1988, Ionospheric imaging using computerized tomography. Radio Sci., 23, 299–307.##Alexandridis, A. and Zapranis, A., 2013, Wavelet neural networks: A practical guide. Neural Networks, 42 1–27.##Ghaffari Razin, M. R., 2015, Development and analysis of 3D ionosphere modeling using base functions and GPS data over Iran. Acta Geod. Geophys., DOI 10.1007/s40328-015-0113-9, 51(1) , 95-111.##Ghaffari Razin, M. R. and Voosoghi, B., 2016, Regional ionosphere modeling using spherical cap harmonics and empirical orthogonal functions over Iran. Acta Geod. Geophys., 52(1), 19-33, doi: 10.1007/s40328-016-0162-8.##Ghaffari Razin, M. R., Voosoghi, B. and Mohammadzadeh, A., 2015, Efficiency of artificial neural networks in map of total electron content over Iran. Acta Geod. Geophys., DOI 10.1007/s40328-015-0143-3.##Ghaffari Razin, M. R. and Voosoghi, B., 2016, Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography. Advances in Space Research. http://dx.doi.org/10.1016/j.asr.2016.04.029.##Hirooka, S., Hattori, K. and Takeda, T., 2011, Numerical validations of neural-network-based ionospheric tomography for disturbed ionospheric conditions and sparse data. Radio Sci., 46(5), RS0F05, DOI: 10.1029/2011RS004760.##Habarulema, J. B., McKinnell, L.-A. and Opperman, B. D. L., 2009, A recurrent neural network approach##to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results, Ann. Geophys., 27, 2111–2125, doi:10.5194/angeo-27-2111-2009.##Kunitsyn, V. E., Nesterov, I. A., Padokhin, A. M. and Tumanova, U. S., 2011, Ionospheric Radio Tomography based on the GPS/GLONASS navigation systems. J. Commun. Technol. Electron., 56(11), 1269-1281, doi: 10.1134/S1064226911100147.##Kennedy, J. and Eberhart, R., 1995, Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 4(ICNN ’95), 1942–1948, Perth, Western Australia, November-December 1995.##Liaqat, A., Fukuhara, M. and Takeda, T., 2003, optimal estimation of parameter of dynamical system by neural network collocation method. Comput. Phys. Commun., 150, 215–234, doi:10.1016/S0010-4655(02) 00680-X, 2003.##Ma, X. F., Maruyama, T., Ma, G. and Takeda, T., 2005, Three dimensional ionospheric tomography using observation data of GPS ground receivers and ionosonde by neural network. J. Geophys. Res., 110, A05308, doi: 10.1029/2004JA010797.##Pokhotelov, D., Jayachandran, P., Mitchell, C. N., MacDougall, J. W. and Denton,##M. H., 2011, GPS tomography in the polar cap: comparison with ionosondes and in situ spacecraft data. GPS Solut., 15(1), 79–87. doi:10.1007/s10291-010-0170-z.##Quarteroni, A., Sacco, R. and Saleri, F., 2007, Numerical Mathematics, 37, Texts in Applied Mathematics, 2nd ed., Springer Berlin Heidelberg, Heidelberg, Germany.##Van de Kamp, M. M. J. L., 2013, Medium-scale 4-D ionospheric tomography using a dense GPS network. Ann Geophys., doi:10.5194/angeo-31-75-2013.##Wen, D. B., Wang, Y. and Norman, R., 2012, A new two-step algorithm for ionospheric tomography solution. GPS Solut., 16(1), 89–94, Jan./Feb.##Yilmaz, A., Akdogan, K. E. and Gurun, M., 2009, Regional TEC mapping using neural networks. Radio Sci., 44, RS3007, doi:10.1029/2008RS004049.##Yizengaw, E., Moldwin, M. B., Dyson, P. L. and Essex, E. A., 2007, Using Tomography of GPS TEC to Routinely Determine Ionospheric Average Electron Density Profiles. Journal of Atmospheric and Solar-Terrestrial Physics, 69, 314-321.##Zhang, Q. and Benveniste, A., 1992, Wavelet Networks. IEEE Trans. Neural Networks, 3(6), 889–898.##]
0
Prediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh
Prediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh
https://jesphys.ut.ac.ir/article_67737.html
10.22059/jesphys.2018.244511.1006941
0
Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database includes 7336 records situated in 11 features from daily brainstorm data within a twenty year period. The samples were selected based on a case study in Torbat-e Heydariyeh. 70% were chosen for learning and 30% were chosen for taking tests. From 7181 available data, 75% and 25% were used for training and evaluating, respectively. This research studied the performance of different neural networks in order to predict precipitation and then presented an algorithm for combining neural networks with linear and nonlinear methods. After modeling and comparing their results using neural networks, the root mean square error was recorded for each method. In the first modeling, the artificial neural network error was 0.05, in the second modeling, linear combination of neural networks error was 0.07, and in the third model, nonlinear combination neural networks error was 0.001. Reducing the error of forecasting precipitation has always been one of the goals of the researchers. This study, with the forecast of precipitation by neural networks, suggested that the use of a more robust method called a nonlinear combination neural network can lead to improve men is in for cast diagnostic accuracy.
1
Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database includes 7336 records situated in 11 features from daily brainstorm data within a twenty year period. The samples were selected based on a case study in Torbat-e Heydariyeh. 70% were chosen for learning and 30% were chosen for taking tests. From 7181 available data, 75% and 25% were used for training and evaluating, respectively. This research studied the performance of different neural networks in order to predict precipitation and then presented an algorithm for combining neural networks with linear and nonlinear methods. After modeling and comparing their results using neural networks, the root mean square error was recorded for each method. In the first modeling, the artificial neural network error was 0.05, in the second modeling, linear combination of neural networks error was 0.07, and in the third model, nonlinear combination neural networks error was 0.001. Reducing the error of forecasting precipitation has always been one of the goals of the researchers. This study, with the forecast of precipitation by neural networks, suggested that the use of a more robust method called a nonlinear combination neural network can lead to improve men is in for cast diagnostic accuracy.
115
126
Iman
Zabbah
Iman
Zabbah
Lecturer, Department of Computer, Torbat-e Heydariyeh branch, Islamic Azad University, Torbat-e Heydariyeh, Iran
Iran
imanzabbah@gmail.com
Ali Reza
Roshani
Ali Reza
Roshani
Assistant Professor, Department of Water Engineering, Torbat-e Heydariyeh branch, Islamic Azad University,
Torbat-e Heydariyeh, Iran
Iran
ar.roshani3380@gmail.com
Amin
Khafage
Amin
Khafage
M.Sc. Graduated, Department of Computer, Torbat-e Heydariyeh branch, Islamic Azad University,
Torbat-e Heydariyeh, Iran
Iran
amin.khafageh@gmail.com
Monthly rainfall
Artificial Neural Networks
experts’ mixture
Torbat-e Heydariyeh Precipitation
[Aksoy, H. and Dahamsheh, A., 2009, Artificial neural network models for forecasting monthly precipitation in Jordan. Stochastic Environmental Research and Risk Assessment, 23(7), 917–931.##Alizadeh, M. J. and Kavianpour, M. R., 2015, Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Mar Pollut Bull., 98, 171-178.##Bustami, R., Bessaih, N., Bong, C. and Suhaili, S., 2007, Artificial Neural Network for Precipitation and Water Level Predictions of Bedup River. IAENG International Journal of Computer Science, 34(2).##Chen, H., Guo, J., Xiong, W., Guo, S. L. and Xu, C.-Y., 2010, Downscaling GCMs sing the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin. Adv. Atmos. Sci., 27(2), 274–284.##Fallah-Ghalhary, G. A., Habibi-Nokhandan, M., Mousavi-Baygi, M., Khoshhal, J. and Barzoki, A. S., 2010, Spring rainfall prediction based on remote linkage controlling using adaptive neuro-fuzzy inference system (ANFIS). Theoretical and Applied Climatology, 101(1–2), 217–233.##Fallah Ghalhery, Gh. and KHoshhal, J., 2011, Forecasting of spring precipitation of Khorasan based on large scale climate signals using neural network. Geography Studies. 69, 115-133.##Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H. and Ramli, M. F., 2012, Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Marine pollution bulletin, 64(11), 2409-2420.##Halabiyan, A. and Darand, M., 2011, Estimation of Isfahan rainfall using artificial neural networks. Journal of Applied Geosciences Research. 12 (26): 47-63##Khalili, N., khodashenas, S. R. and Davari, K., 2007, Prediction of precipitation using artificial neural networks. In the Second Iranian Water Resources Conference.##Khalili, N., Khodashenas, S. R., Davary, K., Baygi, M. M. and Karimaldini, F., 2016, Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study. Arabian Journal of Geosciences, 9(13), 624.##Kisi, O., 2008, River flow forecasting and estimation using different artificial neural network techniques. Hydrology research, 39, 27-40##Moustris, K. P., Larissi, I. K., Nastos, P. T. and Paliatsos, A. G., 2011, Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resources Management, 25(8), 1979–1993.##Nasseri, M., Asghari, K. and Abedini, M. J., 2008, Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Systems with Applications, 35(3), 1415-1421.##Nasseri, M. and Zahraie, B., 2011, Application of simple clustering on space time mapping of mean monthly rainfall pattern. International Journal of Climatology, 31(5), 732–741.##Nourani, V., Kisi, Ö. and Komasi, M., 2011, Two hybrid Artificial Intelligence approaches for modeling rainfall– runoff process. J. Hydrol., 402, 41–59.##Okkan, U., 2012, Wavelet neural network model for reservoir inflow prediction. Scientia Iranica, 19, 1445–1455. https://doi.org/10.1016/j.scient.##Rajaee, T., Nourani, V., Zounemat-Kermani, M. and Kisi, O., 2010, River suspended sediment load prediction: application ofANN and wavelet conjunction model. J. Hydrol. Eng., 16, 613–627.##Salajegheh, A., Fathabadi, A. and Najafi Hajiva, M., 2008, Comparison of Application Time Series and Artificial Neural Network Models in Drought Forecasting (Case Study: Khorasan Razavi Provinces), 2(4), 74–77.##Sattari, M., Rezazadeh Joody, A. and Nahrin, F., 2013, Estimation of Monthly Precipitation Using Artificial Neural Networks and Model M5 (Case Study: Ahar Station). Journal of Irrigation and Water Engineering, 46 (2), 247-260.##Sirhan, H. and Koch, M., 2013, Prediction of Dynamic Groundwater Levels in the Gaza Coastal Aquifer, South Palestine, using Artificial Neural Networks. Journal of Hydrocarbons Mines and Environmental Research, 4(1), 42-56.##Sharma, M., Mathew, L. and Chatterji, S., 2014, Weather forecasting using soft computing techniques. International Journal of Advanced Technology In Engineering And Science, 2(7), 473-480.##Tryland, I., Myrmel, M., Østensvik, Ø., Wennberg, A. C. and Robertson, L. J., 2014, Impact of rainfall on the hygienic quality of blue mussels and water in urban areas in the Inner Oslofjord, Norway. Marine pollution bulletin, 85(1), 42-49.##]
0
Quantification and assessment of effective of global warming on the occurrence of heat and cold waves in some selected stations in Iran
Quantification and assessment of effective of global warming on the occurrence of heat and cold waves in some selected stations in Iran
https://jesphys.ut.ac.ir/article_64870.html
10.22059/jesphys.2018.245853.1006943
0
One of the atmospheric hazards that seriously affect human life and health is the occurrence of thermal tensions and stress in the form of heat and cold waves. Iran is one of the areas of the planet that has climate variability due to its geographical characteristics; therefore, consequently, its different regions are not immune to heat and cold waves. On the other hand, Iran's climate variability is the factor causing the difference between thresholds of heat and cold wave occurrence for its different regions. Therefore, in this study, based on three different thresholds, spatial analysis of the frequency of occurrence of heat and cold waves has been done. Thus in this work, using average daily data from 1960 to 2014, PET (Physiological Equivalent Temperature) was used to monitor heat and cold waves of four stations in Iran. Results of this study showed that in the context of global warming, although significant differences in the frequency of cold waves cannot be seen, these changes are significant and increasing for the frequency of occurrence of heat waves of selected station.
1
One of the atmospheric hazards that seriously affect human life and health is the occurrence of thermal tensions and stress in the form of heat and cold waves. Iran is one of the areas of the planet that has climate variability due to its geographical characteristics; therefore, consequently, its different regions are not immune to heat and cold waves. On the other hand, Iran's climate variability is the factor causing the difference between thresholds of heat and cold wave occurrence for its different regions. Therefore, in this study, based on three different thresholds, spatial analysis of the frequency of occurrence of heat and cold waves has been done. Thus in this work, using average daily data from 1960 to 2014, PET (Physiological Equivalent Temperature) was used to monitor heat and cold waves of four stations in Iran. Results of this study showed that in the context of global warming, although significant differences in the frequency of cold waves cannot be seen, these changes are significant and increasing for the frequency of occurrence of heat waves of selected station.
127
144
Gholam Reza
Roshan
Gholam Reza
Roshan
Assistant Professor, Department of Geography, Golestan University, Gorgan, Iran
Iran
r.rowshan@yahoo.com
Abdolazim
Ghanghermeh
Abdolazim
Ghanghermeh
Assistant Professor, Department of Geography, Golestan University, Gorgan, Iran
Iran
a_ghangherme@yahoo.com
Elham
Neyazmand
Elham
Neyazmand
M.Sc. Student, Department of Geography, Golestan University, Gorgan, Iran
Iran
elham.neyazmand1370@yahoo.com
Global warming
frequency of occurrence
Duration
thermophysiologic indices
temperature stress
Iran
[Sanaee, M., Khanmohammadi, M. and Mohammadi, H., 2013, The analysis of synoptic pattern of the hazardous event of heat wave of summer 2013 and death caused by pollution in Tehran, Journal of Knowledge of Risk, Issue 1, 67 - 83.##Darand, M., 2014, The identification and analysis of spatio-temporal heat waves of Iran, Journal of Geography and Development, number 35, 167-180.##Esmaeelinejad, M., 2012, Identification of heat waves of Iran and its relationship with synoptic pattern. Doctoral thesis of physical geography, Department of Earth Sciences, University of Sistan and Baluchestan, Iran.##Karimi, S., Negaresh, H., Tavoosi, D. and Alijani, B., 2012, Synoptic analysis of the Comprehensive cold waves of Iran, Geography and Development Journal, 29, 55-76.##Masoodian, A., 2004, Investigation of Iran’s temperature trend in the last half century, Geography and Development journal, Issue3.##Alexander, L. and Arblaster, J., 2009, Assessing trends in observed and modelled climate extremes over Australia in relation to future projections, Int. J. Climatol. 29, 417–435.##Aylin, P., Morris, S., Wakefield, J., Grossinho, A., Jaru, L. and Elliott, P., 2001, Temperature, housing, deprivation and their relationship to excess winter mortality in Great Britain 1986–1996. Int. J. Epidemiol., 30, 1100–1108. doi:10.1093/ije/30.5.1100.##Ballester, F., Michelozzi, P. and Iniguez, C., 2003, Weather, climate and public health. J Epidemiol Community Health, 57, 759–760.##Barriopedro, D., Fischer, E. M., Luterbacher, J., Ricardo M. Trigo, M. R and García-Herrera, R., 2011, The hot summer of 2010: redrawing the temperature record map of Europe. Science, 332, 220–224.##Bartholy, J. and Pongracz, R., 2007, Regional analysis of extreme temperature and precipitation indices for the Carpathian Basin from 1946 to 2001. Glob. Planet. Change, 57(1), 83–95.##Bartholy, J., Pongracz, R., Gelybo, G. and Szabo, P., 2008, Analysis of expected climate change in the Carpathian Basin using the PRUDENCE results. Idoiaras, 112(3–4), 249–264.##Basarin, B., Lukić, T. and Matzarakis, A., 2015, Quantification and assessment of heat and cold waves in Novi Sad, Northern Serbia, Int. J. Biometeorol., doi:10.1007/s00484-015-1012-z.##Basu, R. and Samet, J. M., 2002, Relation between elevated ambient temperature and mortality: a review of the epidemiologic evidence. Epidemiol. Rev., 24, 190–202.##Beniston, M., 2004, The 2003 heat wave in Europe: a shape of things to come? An analysis based on Swiss climatological data and model simulations. Geophys. Res. Lett., 31, L02202.##Beniston, M. and Diaz, H. F., 2004, The 2003 heat wave as an example of summers in a greenhouse climate? Observations and climate model simulations for Basel, Switzerland. Global and Planetary Change, 44, 73–81.##Bøkenes, L., Alexandersen, T. E., Østerud, B., Tveita, T. and Mercer, J. B., 2000, Physiological and hematological responses to cold exposure in the elderly. Int. J. Circumpolar. Health, 59, 216–221.##Christoph, S. and Gerd, J., 2004, Hot news from summer 2003. Nature, 432, 559–560.##Ding, T., Qian, W. H. and Yan, Z. W., 2009, Changes of hot days and heat waves in China during 1961–2007. Int. J. Climatol., 30, 1452–1462.##Ding, T. and Ke, Z., 2013, Characteristics and changes of regional wet and dry heat wave events in China during 1960–2013, Theor. Appl. Climatol., 122, 651–665.##Dole, R., Hoerling, M., Perlwitz, J., Eischeid, J., Pegion, P., Quan, T., Xu, T. and Murray, D., 2011, Was there a basis for anticipating the 2010 Russian heat wave? Geophys. Res. Lett., 38, L06702.##Dolney, T. J. and Sheridan, S. C., 2006, The relationship between extreme heat and ambulance response calls for the city of Toronto, Ontario, Canada. Environ. Res., 101, 94–103.##Fang, X. Q., Wang, A. Y., Fong, S. K., Lin, W. and Liu, J., 2008, Changes of reanalysis-derived Northern Hemisphere summer warm extreme indices during 1948– 2006 and links with climate variability. Glob. Planet Chang., 63, 67–78.##Farajzadeh, M. and Darand, M., 2008, Analysis of air temperature influence on mortality in Tehran. Hakim Res. J., 11, 27–34.##Fitchett, J. M., Grab, S. W., Thompson, D. I. and Roshan, G., 2014, Spatio-temporal variation in phenological response of citrus to climate change in Iran: 1960–2010. 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0
Spatiotemporal Variations of Total Cloud Cover and Cloud Optical Thickness in Iran
Spatiotemporal Variations of Total Cloud Cover and Cloud Optical Thickness in Iran
https://jesphys.ut.ac.ir/article_67759.html
10.22059/jesphys.2018.248041.1006956
0
A knowledge of cloud properties and spatiotemporal variations of clouds is especially crucial to understand the radiative forcing of climate. This research aims to study cloudiness in Iran using the most recent satellite data, powerful databases, and regional and seasonal analyses. In this study, three data series were used for the spatiotemporal variations of cloudiness in the country: A) Cloudiness data of 42 synoptic stations in the country during the statistical period from 1970 to 2005, B) Cloud Optical Thickness (COT) of Terra and Aqua MODIS sensors for 2003-2015, and C) Total Cloud Cover (TCC) of ECMWF Database, ERA-Interim version, for 1979-2015. The values obtained in the country were located via the kriging geostatistical method by RMSE. The results showed that the highest TCC occurs during the winter months. At this time of the year, the cloud cover is reduced from North to South and from West to East. Besides, COT showed that in the cold months of the year, the highest COT is observed in January and the lowest in March. The west and central Zagros highlands have the highest COT. Incorporating COT and TCC results showed that the two factors of height and approximation and access to moisture sources contribute significantly to the regional differences of cloudiness in Iran.
1
A knowledge of cloud properties and spatiotemporal variations of clouds is especially crucial to understand the radiative forcing of climate. This research aims to study cloudiness in Iran using the most recent satellite data, powerful databases, and regional and seasonal analyses. In this study, three data series were used for the spatiotemporal variations of cloudiness in the country: A) Cloudiness data of 42 synoptic stations in the country during the statistical period from 1970 to 2005, B) Cloud Optical Thickness (COT) of Terra and Aqua MODIS sensors for 2003-2015, and C) Total Cloud Cover (TCC) of ECMWF Database, ERA-Interim version, for 1979-2015. The values obtained in the country were located via the kriging geostatistical method by RMSE. The results showed that the highest TCC occurs during the winter months. At this time of the year, the cloud cover is reduced from North to South and from West to East. Besides, COT showed that in the cold months of the year, the highest COT is observed in January and the lowest in March. The west and central Zagros highlands have the highest COT. Incorporating COT and TCC results showed that the two factors of height and approximation and access to moisture sources contribute significantly to the regional differences of cloudiness in Iran.
145
164
Mahmoud
Ahmadi
Mahmoud
Ahmadi
Associate Professor, Department of physical geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
Iran
ma_ahmadi@sbu.ac.ir
Abbasali
Dadashiroudbari
Abbasali
Dadashiroudbari
Ph.D. Student, Department of physical geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
Iran
dadashiabbasali@gmail.com
Hamzeh
Ahmadi
Hamzeh
Ahmadi
Ph.D. Student, Department of physical geography, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Iran
Iran
iran_alasht@yahoo.com
COT
TCC
ECMWF database
MODIS Sensor
Iran
[Ackerman, S., 2015, MODIS atmosphere L2 cloud mask product. NASA MODIS adaptive processing system, Goddard Space Flight Center, USA.##Ackerman, S. A., Holz, R. E., Frey, R., Eloranta, E. W., Maddux, B. C. and McGill, M., 2008, Cloud detection with MODIS. Part II: validation. Journal of Atmospheric and Oceanic Technology, 25(7), 1073-1086.##Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C. and Gumley, L. E., 1998, Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research, 103(D24), 32-141.##Ahmadi, M., Ahmadi, H. and Dadashiroudbari, A., 2018, Assessment of trends and spatial pattern seasonal and annual cloudiness in Iran. Natural Environmental Hazards, 7(15), 239-256. doi: 10.22111/jneh.2017.3200##Alijani, B., 2006, Iran climate, Payame Noor University Press, p. 221 (in Persian).##Barnes, M. L., Miura, T. and Giambelluca, T. 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A., 2003, The MODIS cloud products: Algorithms and examples from Terra. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 459-473.##Rasooli, A. A., Jahanbakhsh, S. and Ghasemi, A. R., 2013, Investigation of Spatial and Temporal Variations of Cloud Cover in Iran. Geores, 28(3), 87-104.##Rasouli, A., Jahanbakhsh, S. and Ghasemi, A., 2013, Investigation of temporal and spatial variations of cloud cover in Iran. Geographical Survey Quarterly, 3, 85-102.##Sanchez-Lorenzo, A., Calbó, J. and Wild, M., 2012, Increasing cloud cover in the 20th century: review and new findings in Spain. Climate of the Past, 8(4), 1199-1212.##Sato, T. and Kimura, F., 2005, Diurnal cycle of convective instability around the central mountains in Japan during the warm season. Journal of the Atmospheric Sciences, 62(5), 1626-1636.##Sato, T., Kimura, F. and Hasegawa, A. S., 2007, Vegetation and topographic control of cloud activity over arid/semiarid Asia. 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0
Periodicity of Downward Longwave Radiation at an Equatorial Location
Periodicity of Downward Longwave Radiation at an Equatorial Location
https://jesphys.ut.ac.ir/article_67740.html
10.22059/jesphys.2018.253506.1006981
0
A good understanding of the diverse mechanisms in the atmosphere is required in modelling the climate. In this study, the diurnal and seasonal patterns of all-sky downward longwave radiation (DLR) at Ilorin (8o 32l N, 4o 34l E), Nigeria were investigated alongside relative humidity (RH) and temperature. The average diurnal pattern of DLR gives an arc that begins by increasing gradually with some inherent fluctuations from 01:00 hour to a maximum at 14:00 hour local time, and then gradually decreases to a minimum at 00:00 hour. However, sometimes erratic and double peak arc diurnal DLR patterns occur around the mid and the end of the year periods respectively. The seasonal, diurnal peak of temperature and the minimum of relative humidity (RH) occur approximately two hours after the peak of DLR. Besides, the seasonal trends of both DLR and RH match each other, except sometimes in June, which could be due to the midyear DLR erratic diurnal effect. Possibly, the mechanisms of the inter-tropical discontinuity (ITD) influence the particular diurnal patterns of DLR at the midyear and year-end periods. Moreover, monthly dispersion of DLR is high during known months of high atmospheric aerosols, and annual maximum temperature occurs after the Harmattan season. Hence, variations in DLR are influenced mostly by mineral dust in the atmosphere, mechanisms of ITD and changes in the sun-earth distance, which reflects on the different seasons in Ilorin.
1
A good understanding of the diverse mechanisms in the atmosphere is required in modelling the climate. In this study, the diurnal and seasonal patterns of all-sky downward longwave radiation (DLR) at Ilorin (8o 32l N, 4o 34l E), Nigeria were investigated alongside relative humidity (RH) and temperature. The average diurnal pattern of DLR gives an arc that begins by increasing gradually with some inherent fluctuations from 01:00 hour to a maximum at 14:00 hour local time, and then gradually decreases to a minimum at 00:00 hour. However, sometimes erratic and double peak arc diurnal DLR patterns occur around the mid and the end of the year periods respectively. The seasonal, diurnal peak of temperature and the minimum of relative humidity (RH) occur approximately two hours after the peak of DLR. Besides, the seasonal trends of both DLR and RH match each other, except sometimes in June, which could be due to the midyear DLR erratic diurnal effect. Possibly, the mechanisms of the inter-tropical discontinuity (ITD) influence the particular diurnal patterns of DLR at the midyear and year-end periods. Moreover, monthly dispersion of DLR is high during known months of high atmospheric aerosols, and annual maximum temperature occurs after the Harmattan season. Hence, variations in DLR are influenced mostly by mineral dust in the atmosphere, mechanisms of ITD and changes in the sun-earth distance, which reflects on the different seasons in Ilorin.
165
177
Nsikan
Obot
Nsikan
Obot
Ph.D. Graduated, Department of Physics, University of Lagos, Akoka, Lagos State, Nigeria
Iran
obot4ever@yahoo.com
Ibifubara
Humphrey
Ibifubara
Humphrey
Ph.D. Graduated, Department of Physics, University of Lagos, Akoka, Lagos State, Nigeria
Iran
ihumphrey@unilag.edu.ng
Michael
Chendo
Michael
Chendo
Professor, Department of Physics, University of Lagos, Akoka, Lagos State, Nigeria
Iran
mchendo@unilag.edu.ng
Elijah
Oyeyemi
Elijah
Oyeyemi
Professor, Department of Physics, University of Lagos, Akoka, Lagos State, Nigeria
Iran
eoyeyemi@unilag.edu.ng
Sunday
Udo
Sunday
Udo
Professor, Department of Physics, University of Calabar, Calabar, Cross River State, Nigeria
Iran
soudo95@yahoo.com
Downward longwave radiation (DLR)
temperature
relative humidity
Inter-tropical discontinuity (ITD)
Sun-earth distance
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