فیلترکردن داده‌های خرد لرزه‌ای با تکنیک کاهش مصنوعی فرکانس نمونه‌برداری برای تخمین بسامد آبرفت‌های بنیادی و عمق سنگ بستر مهندسی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی نفت و معدن، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران.

2 گروه مهندسی برق، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران.

چکیده

کیفیت اندازه‌گیری و روش پردازش خردلرزه‌ها تأثیر قابل‌توجهی بر دقت تخمین پارامترهای دینامیکی ساختگاه دارد. در این مقاله، به‌منظور افزایش دقت تخمین فرکانس غالب آبرفت، از تکنیک کاهش مصنوعی فرکانس نمونه‌برداری و روش واریانس پیش‌رونده استفاده شده است. این روش‌ها به‌ویژه در مناطق شهری که تحت‌تأثیر اختلالات محلی قرار دارند، می‌توانند به‌طور مؤثری نویزهای اضافی را حذف کرده و دقت تخمین را افزایش دهند. با استفاده از پنجره لغزان، واریانس پیش‌رونده سیگنال محاسبه و از آن به‌عنوان پایه‌ای برای حذف محدوده داده‌هایی که تحت‌تأثیر اختلالات گذرا قرار گرفته‌اند، استفاده می‌شود. نتایج حاصل از اعمال این روش‌ها بر روی داده‌های خردلرزه‌ای ثبت شده در شهر میبد، نشان‌دهنده عملکرد مؤثر این تکنیک‌ها در حذف اثر اختلالات محلی و بهبود دقت تخمین فرکانس غالب آبرفت است. این مطالعه نشان می‌دهد که با تغییراتی در روش پردازش داده‌ها، می‌توان به‌طور قابل‌توجهی از اثرات مخرب اختلالات محلی کاست و فرکانس غالب زمین را با دقت بیشتری تعیین کرد. با توجه به اقتصادی بودن این روش، تحلیل خردلرزه‌ها می‌تواند به‌عنوان یک روش پایه‌ای در مطالعات اولیه ژئوتکنیکی در مناطق مختلف مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Filtering of microtremor data with the technique of artificial reduction of sampling frequency to estimate the frequency of fundamental alluvium and the depth of engineering bedrock

نویسندگان [English]

  • Ahmad Adib 1
  • Mohammad Mousavi Anzehaee 2
  • pooria Kianoush 1
1 Department of Ptrolum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

The phenomenon of resonance caused by the behavior of alluvium during an earthquake affects the occurrence of damage. This phenomenon occurs when the dominant period of alluvion is equal to the dominant period of structures (Mukhopadhyay and Bormann, 2004; Kanli et al., 2006; Kvasnička et al., 2011). Therefore, determining the dominant frequency of the soil with high certainty is particularly important and is the primary goal of this paper. The method of using microtremors to determine the site's response to earthquakes and determine the dominant frequency of the soil is of interest, and by measuring them at the ground level, information about the vibration characteristics of the soil can be obtained. (Kerh & Chu, 2002; Kianoush et al., 2023a,b; Jamshidi et al., 2024; Khoshmagham et al., 2024).
Adib et al. (2015) conducted the site classification of Ardakan City in Yazd province based on the earth's natural frequency by the microtremor data. While identifying the dynamic characteristics of the earth in this area, the level of compatibility of the land classification is suitable for the studies of the site effect based on the geotechnical, geophysical, and microtremor data with the regulations of dynamic design of buildings. Similarly, Bagheri et al. (2017) emphasized the importance of denoising seismic data to improve the quality of records, which is essential for accurate interpretation. They proposed a novel method combining frequency-offset deconvolution (FXD) and decision-based median (DBM) filtering to enhance the signal-to-noise ratio (S/N) and effectively suppress random noise.
Adib and others (Adib et al., 2015) classified the site effects using a fractal model based on the analysis of microtremor data, frequency amplification index, and vulnerability (k-g)) in the city of Meybod. Nogoshi and Igarashi (1970, 1971) improved, this classification and found four types of soil, including (1) hard soil and weak rock with a frequency of 6.2 to 8 Hz, (2) hard soil with a frequency of about 4.9 to 6.2 Hz, (3) relatively soft soil with a frequency of 2.4 to 4.9 Hz and (4) soft soil Soft with a frequency of less than 2.4 Hz that were separated in this city (Wahba et al., 2024; Labuta et al., 2025). Bagheri and Riahi (2016, 2018) also highlighted the effectiveness of DBM filtering in seismic data processing, demonstrating its superiority in suppressing random noise and improving S/N. This classification aligns with findings by KhodAgholi and Bagheri (2020), who introduced local least squares polynomial (LLSP) smoothing to diminish seismic random noise, thereby enhancing data quality and interpretability.
The Quality of microtremor data and processing methods considerably affects the accuracy of estimating dynamic soil parameters. In this paper, an artificial sampling frequency reduction technique has been proposed for removing high-frequency perturbations from microtremor data, and a running variance method has been used to improve the automatic detection of data sections infected by local perturbations. In this method, the running variance of the signal was calculated using a sliding window. Then, the resulting variance signal was used to remove the portions of data affected by transient perturbations. The proposed methods have been applied to the data recorded for the Meybod city in the North of Yazd Province. By comparing the results of the proposed techniques and the standard methods on microtremor data in urban areas, it is clear that the proposed methods have successfully removed the effects of local transients and their redundant fluctuations such that there is no sharp amplitude variation in the residual signal. The results of the simulation confirm this claim.
Additionally, the ease of parameter setting in the running variance approach makes it superior to the ratio method. Discarding the fewer amounts of contaminated data is an effective method, especially in regions with extensive noise. This paper shows that some variations in microtremor data processes could reduce the destructive effects of local transients on them and consequently improve fundamental site frequency estimation based on the spectral ratio of microtremors. Therefore, considering the low cost of this approach, microtremor analysis can be used as a primary method for initial geotechnical studies in various regions. As a result, it increases the accuracy and degree of confidence in estimating the dominant frequency based on the spectral ratio based on the microtremors data.

کلیدواژه‌ها [English]

  • Microtremors
  • Fundamental Alluvium Frequency
  • Artificial Sampling Frequency Reduction Technique
  • Running Variance Method
  • Disturbance Removal
ادیب، ا. (1395). طبقه‌بندی ساختگاه بر مبنای فرکانس طبیعی مبتنی بر داده‌های خردلرزه‌ای برای طراحی ساختمان‌ها، مطالعه موردی شهر اردکان، مجله فیزیک زمین و فضا، 42(1)، 88-75.
Abbas, A., Aimar, M., Cox, B. R., & Foti, S. (2025). A frequency-domain beamforming procedure for extracting Rayleigh wave attenuation coefficients and small-strain damping ratio from 2D ambient noise array measurements. Earthquake Spectra, 87552930241304914.
Adib, A., Afzal, P., & Heydarzadeh, K. (2015). Site effect classification based on microtremor data analysis using a concentration–area fractal model, Nonlin Processes Geophys, 22, 53–63.
Adib, A., & Kianoush, P. (2025). Geotechnical and geological characterization of the Meskani Mine Complex, Yazd Block, Central Iran: A Multidisciplinary study. Results in Earth Sciences, 3, 100072.
Bagheri, M. & Riahi, M. A. (2018). Using a novel method for random noise reduction of seismic records. Iranian Journal of Oil and Gas Science and Technology7(3), 65-72.
Bagheri, M., & Riahi, M. A. (2016). Seismic data random noise attenuation using DBM filtering. Bollettino di Geofisica Teorica ed Applicata, 57, 1-11.
Bagheri, M., Riahi, M.A. & Hashemi, H. (2017). Denoising and improving the quality of seismic data using combination of DBM filter and FX deconvolution. Arabian Journal of Geosciences, 10, 440.
Mousavi Anzehaee, M., Heydarzadeh, K., & Adib, A. (2018). Employing the Bayesian data fusion to estimate the fundamental frequency of site by means of microtremors data, Acta Geod Geophys, 53, 523–541.
Ait Laasri, E. H., Akhouayri, E. S., Agliz, D., & Atmani, A. (2014). Automatic detection and picking of P-wave arrival in locally stationary noise using cross-correlation, Digital Signal Processing, 26, 87-100.
Akazawa, T. (2004). A technique for automatic detection of onset time of P and S-phases in strong motion records, Proc. of 13th World Conference on Earthquake Engineering, Vancouver, B.C., Canada, Paper No. 786.
Apostolidis, P. I., Raptakis D. G., Pandi, K. K., Manakou, M.V., & Pitilakis, K.D. (2006). Definition of subsoil structure and preliminary ground response in Aigion city (Greece) using microtremor and earthquakes, Soil Dynamics and Earthquake Engineering, 26 (10), 922-940.
Bommer, J. J., van Elk, J., & Zoback, M. D. (2024). Estimating the Maximum Magnitude of Induced Earthquakes in the Groningen Gas Field, the Netherlands. Bulletin of the Seismological Society of America, 114(6), 2804-2822.
Boore, D. M. (2003). Simulation of Ground Motion Using the Stochastic Method. In: Seismic Ground Motion, pp. 1-20.
Chatelain, J., Guillier, B., Cara, F., Duval, A., Atakan, K., Bard, P., & the WP02 SESAME team. (2008). Evaluation of the influence of experimental conditions on H/V results from ambient noise recordings, Springer, Bull. Earthq, Eng, 6 (1), 33-74.
Delgado, J., Galiana-Merino, J. J., García-Tortosa, F. J., Garrido, J., Lenti, L., Martino, S. & Soler-Llorens, J. L. (2021). Ambient noise measurements to constrain the geological structure of the Güevéjar landslide (S Spain). Applied Sciences, 11(4), 1454.
Eftekhari, S. H., Memariani, M., Maleki, Z., Aleali, M., & Kianoush, P. (2024). Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods. Scientific Reports, 14(1), 5198.
EL Hilali, M., Bounab, A., Timoulali, Y., El Messari, J. E. S., & Ahniche, M. (2023). Seismic site-effects assessment in a fluvial sedimentary environment: case of Oued Martil floodplain, Northern Morocco. Natural Hazards, 118(2), 1235-1257.
Gentili, S., & Michelin, A. (2006). Automatic picking of P and S phases using a neural tree”, J. Seismol. 10 (1), 39–63.
Gosar, A. (2007). Microtremor HVSR study for assessing site effects in the Bovec basin (NW Slovenia) related to 1998 Mw 5.6 and 2004 Mw 5.2 earthquakes, Engineering Geology, 91 (2-4), 178–193.
Havenith, H. B. (2004). Guidelines for the implementation of the H/V spectral ratio technique on ambient vibrations measurements, processing and interpretation. European Commission, ORBi, the institutional repository of the University of Liège, 1-62.
Hloupis G., Vallianatos F., & Stonham, J. (2004). A wavelet representation of HVSR technique, Bull. Geol. Soc. Greece, Proceedings of the 10th International Congress, Thessaloniki XXXVI, pp. 1269-1278.
Hosseini, S. A., Khah, N. K. F., Kianoush, P., Arjmand, Y., Ebrahimabadi, A., & Jamshidi, E. (2023a). Tilt angle filter effect on noise cancelation and structural edges detection in hydrocarbon sources in a gravitational potential field. Results in Geophysical Sciences, 14, 100061.
Hosseini, S. A., Khah, N. K. F., Kianoush, P., Afzal, P., Shakiba, S., & Jamshidi, E. (2023b). Boundaries determination in potential field anomaly utilizing analytical signal filtering and its vertical derivative in Qeshm Island SE Iran. Results in Geophysical Sciences, 14, 100053.
Jamshidi, E., Kianoush, P., Hosseini, N., & Adib, A. (2024). Scaling-up dynamic elastic logs to pseudo-static elastic moduli of rocks using a wellbore stability analysis approach in the Marun oilfield, SW Iran. Scientific Reports,14(1):19094.
Kanlı, A. I., Tildy, P., Prónay, Z., Pınar, A., & Hermann, L. (2006). VS30 mapping and soil classification for seismic site effect evaluation in Dinar region, SW Turkey, Geophys. J. Int, 165 (1), 223–235.
Kassaras, I., Kalantoni, D., Benetatos, C., Kaviris, G., Michalaki, K., Sakellariou, N., & Makropoulos, K. (2015). Seismic damage scenarios in Lefkas old town (W. Greece). Bulletin of Earthquake Engineering, 13, 3669-3711.
Kerh, T., & Chu, D. (2002). Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion, Advances in Engineering Software, 33 (11-12), 733-742.
Keshavarz Faraj Khah, N., Salehi, B., Kianoush, P., & Varkouhi, S. (2024). Estimating elastic properties of sediments by pseudo-wells generation utilizing simulated annealing optimization method. Results in Earth Sciences, 2, 100024.
KhodAgholi, M. A., & BAgheri, M. (2020). Seismic data random noise attenuation using LLSP smoothing. Bollettino di Geofisica Teorica ed Applicata, 61(2).
Khoshmagham, A., Hosseini, N., Shirinabadi, R., Bangian Tabrizi, AH., Gholinejad, M., & Kianoush, P. (2024). Investigating the Time-Dependent Behavior of Intact Rocks and Fractured Rocks Using Unconfined Relaxation Testing in Underground Coal Mines. Geotechnical and Geological Engineering, 42, 6889-6922.
Kianoush, P., Mohammadi, G., Hosseini, S.A., Keshavarz Faraj Khah, N., & Afzal, P. (2023a). Inversion of seismic data to modeling the Interval Velocity in an Oilfield of SW Iran. Results in Geophysical Sciences, 13:100051.
Kianoush, P., Mohammadi, G., Hosseini, S. A., Keshavarz Faraj Khah, N., & Afzal, P. (2023b). ANN-based estimation of pore pressure of hydrocarbon reservoirs—a case study. Arabian Journal of Geosciences, 16(5), 302.
Kianoush, P., Khah, N. K. F., Hosseini, S. A., Jamshidi, E., Afzal, P., & Ebrahimabadi, A. (2023c). Geobody estimation by Bhattacharyya method utilizing nonlinear inverse modeling of magnetic data in Baba-Ali iron deposit, NW Iran. Heliyon, 9(11).
Kianoush, P. , Afzal, P. , Mohammadi, G. , Keshavarz Faraj Khah, N. , & Hosseini, S. A. (2023d). Application of Geostatistical and Velocity-Volume Fractal Models to Determine Interval Velocity and Formation Pressures in an Oilfield of SW Iran. Journal of Petroleum Research33(1402-1), 146-170. 
Kianoush, P., Mahvi, M.R., Keshavarz Faraj Khah, N., Kadkhodaie, A., Jodeiri Shokri, B., & Varkouhi, S. (2024a). Hydrogeological studies of the Sepidan basin to supply required water from exploiting water wells of the Chadormalu mine utilizing reverse osmosis (RO) method. Results in Earth Sciences 2, 100012.
Kianoush, P. , Keshavarz Faraj Khah, N. , Afzal, P. , Jamshidi, E. , Bangian Tabrizi, A. H. & Kadkhodaie, A. (2024b). Formation Pressures Determination Utilizing the Integration of Fractal and Geostatistical Modelling in a Hydrocarbon Formation of SW Iran. Journal of Analytical and Numerical Methods in Mining Engineering14(40), 11-34. 
Kvasnička, P., Matešić, L., & Ivandić, K. (2011). Geotechnical site classification and Croatian National Annex for Eurocode 8, GEOFIZIKA, 28 (1), 83–97.
Labuta, M., Oprsal, I., Landa, D. A., & Burjánek, J. (2025). Ambient vibrations of a deep maar resonator. Soil Dynamics and Earthquake Engineering, 188, 109072.
Lombardo, G., Langer, H., Gresta, S., Rigano, R., Monaco, C., & De Guidi, G. (2006). On the importance of geolithological features for the estimate of the site response: the case of Catania metropolitan area (Italy), Natural Hazards, 38 (3), 339-354.
Mukhopadhyay, M., & Bormann, P. (2004). Low cost seismic microzonation using microtremor data: an example from Delhi, India, Journal of Asian Earth Sciences, 24 (3), 271-280.
Nakamura, Y. (1989). A method for dynamic characteristics estimation of subsurface using microtremors on the ground surface, Quarterly Report of RTRI, 30 (1), 25-33.
Okada, H. (2003). The microseismic survey method, Society of Exploration Geophysicists of Japan, Translated by Koya Suto as Geophysical Monograph Series Society of Exploration Geophysicists, Tulsa, Geophysical Monograph Series, No. 12.
Parolai, S., & Galiana-Merino, J. J. (2006). Effect of transient seismic noise on estimates of H/V spectral ratios, Bull. Seismol, Soc. Am, 96 (1), 228-236.
Parolai, S., Richwalski, S. M., Milkereit, C., & Bormann, P. (2004). Assessment of the stability of H/V spectral ratios from ambient noise and comparison with earthquake data in the Cologne area (Germany), Tectonophysics, 390 (1-4), 57- 73.
Rajput, A., Ajay Pratap Singh, A.P., Kumar Pal, S., & Singh, P. (2025). Determining shallow S‐Wave velocity structure and site response parameters in Gwalior basin, Central India, through microtremor measurements. Near Surface Geophysics, 23(1), 49 – 68.
Riepl, J., Bard, P. Y., Hatzfeld, D., & Papaioannou, C. (1998). Nechtschein S. Detailed evolution of site response estimation methods across and along the sedimentary valley of Volvi (EURO-SEISTEST), Bull Seismol Soc Am, 88 (2), 488–502.
Saragiotis, C. D., Hadjileontiadis, L. J., & Panas, S. M. (2002). PAI-S/K: a robust automatic seismic P phase arrival identification scheme, IEEE Trans. Geosci. Remote Sens, 40 (6), 1395-1404.
Tezcan, S. S., Kaya, I.E., BAL, E., & Ozdemir, Z. (2002). Seismic amplification at Avcilar, Istanbul, Eng. Struct. 24 (5), 661-667.
Song, C., Yu, C., Li, Z., Pazzi, V., Del Soldato, M., Cruz, A., & Utili, S. (2021). Landslide geometry and activity in Villa de la Independencia (Bolivia) revealed by InSAR and seismic noise measurements. Landslides, 18(8), 2721-2737.
Trnkoczy, A. (2012). Understanding and parameter setting of STA/LTA trigger algorithm”, In: Bormann, P. (Ed.) New Manual of Seismological Observatory Practice 2 (NMSOP-2), Potsdam: Deutsches GeoForschungsZentrum (GFZ), p. 1-20.
Tuladhar, R. (2002). Seismic Microzonation of the Greater Bangkok with Microtremor Observations, Thesis for degree of Master of Engineering, Asian Institute of Technology, School of Civil Engineering.
Vera Rodriguez, I. (2011). Automatic Time-picking of Microseismic Data Combining STA/LTA and the Stationary Discrete Wavelet Transform, Recovery 2011, CSPG CSEG CWLS Joint Annual Convention, Verarodr, Canada.
Varkouhi, S., Tosca, N.J., Cartwright, J.A., Guo, Z., Kianoush, P., & Behl, R.J. (2024). Pervasive accumulations of chert in the equatorial Pacific during the early eocene climatic optimum. Marine and Petroleum Geology, 167, 106940.
Vera Rodriguez, I., Bonar, D., & Sacchi, M. (2011). Improvements in microseismic data processing using sparsity and non-linear inversion constraints, Recorder, Official Publication of the Canadian Society of Exploration Geophysicists, 36 (9), 24-28.
Wahba, D., Omran, A. A., Adly, A., Gad, A., Arman, H., & El-Bagoury, H. (2024). Optimizing Site Selection for Construction: Integrating GIS Modeling, Geophysical, Geotechnical, and Geomorphological Data Using the Analytic Hierarchy Process. ISPRS International Journal of Geo-Information, 14(1), 3.
Wong, J., Han, L., Bancroft, J. C., & Stewart, R. R. (2009). Automatic time-picking of first arrivals on noisy microseismic data, CSPG CSEG CWLS Convention”, Calgary, Canada.
Xiantai, G., Zhimin, L., Na, Q., & Weidong, J. (2011). Adaptive picking of microseismic event arrival using a power spectrum envelope. Computers & geosciences, 37(2), 158-164.
Xu, W., Wu, J., & Gao, M. (2023). Seismic Hazard Analysis of China’s Mainland Based on a New Seismicity Model. International Journal of Disaster Risk Science, 14(2), 280-297.
Zaharia, B., Radulian, M., Popa, M., Grecu, B., & Tataru, D. (2008). Estimation of the Local Response Using the Nakamora Method for Bucharest area, Romanian Reports in Physics, 60 (1), 131-144.
Zhang, H. J., Thurber, C., & Rowe, C. (2003). Automatic P-wave arrival detection and picking with multi scale wavelet analysis for single-component recordings, Bull. Seismol. Soc. Am. 93 (5), 1904-1912.