Application of Sharp Boundary and Tear Zone Inversions for Optimal Interpretation of Magnetotelluric Data in North-West Iran

Document Type : Research Article


Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.


MT data inversion suffers from the non-uniqueness problem of its solution. The problem rises due to the non-linear relations between transfer functions and EM fields, employed in MT exploration, and also the limited number of imprecise data points. In most common MT inversion algorithms, this problem is solved by introducing the smoothest model constraint (Фm) to the inversion objective function: E(m)= Φd+ τ Φm
However, in situations where geological and previous geophysical data (ex. well-log and seismic data) confirm the presence of uniform subsurface structures detached by sharp boundaries, the implementation of the smoothest model constraint can lead to unrealistic geological results. In this study we investigate how the application of tear zone and sharp boundary inversions could improve the interpretation of MT data? For this purpose, four synthetic models were considered and their MT responses were calculated using a finite element forward modeling approach (Wannamaker et al 1986) and contaminated with noises. In the next step, they were employed as input data through smoothest model, tear zone and sharp boundary inversion procedures.
The results indicate that the incorporation of other geophysical data in the inversion starting model as tear zones and sharp boundaries, allows accurately measuring the space of the model parameters and obtaining more precise results. We applied a multi-site- multi-frequency approach of Mc-Neice and Jones (2001) for dimensionality and strike analysis as well as to separate and remove galvanic distortions (twist and shear angles) and contaminated impedance responses of the regional geoelectric structure. The method employs a least square approach to fit the measured data with a seven-parameter model describing strike direction and telluric distortion parameters. The results show a clear minimum in RMS for a strike angle of zero degree (figure8). Shear angles lie predominantly within the range of [-45˚, 45˚] (left column in figure 9) and the observed twist angles fall mostly within the range of [-60˚, 40˚] (right column in figure 9). Then, we applied the smoothest model, tear zone, and sharp boundary inversions for data modeling and interpretation whose results are presented in the figures (10), (11) and (12), respectively. We can effectively derive three alternative classes of models from magnetotelluric (MT) data.
The results are consistent with the conceptual model presumed for a high enthalpy geothermal region. Unaltered surface rocks and porous Basalt exhibit a high resistive overburden underlain by relatively more conductive Paleozoic sediments. In deeper parts, a common signature of hydrothermal systems appears and resistivity increases beneath a highly conductive clay cap (feature C3). An oblique conduit (feature C2) dipping to the northwest of the Moil valley connects the surficial clay cap with a deep conductor (feature C1). The conduit (feature C2) is parallel to the prevalent direction of faults and fractures of the area and shows that the linear structures constitute pathways where convective fluid flow can take place. The absence of this feature beneath the profile P03 shows that the lateral extension of the geothermal reservoir is limited to the west of profile P03.


Main Subjects

قنبری‌فرد، س. (1399). تنظیم بهینه پارامترهای مختلف الگوریتم گرادیان مزدوج غیر خطی برای وارون‌سازی داده‌های مگنتوتلوریک. پایان‌نامه کارشناسی ارشد. به‌راهنمایی منصوره منتهایی و بهروز اسکویی. تهران: دانشگاه تهران، مؤسسه ژئوفیزیک.
Bedrosian, P. A. (2007). MT+, integrating magnetotellurics to determine earth structure, physical state, and processes. Surveys in geophysics, 28, 121-167.
Bogie, I., Cartwright, A. J., Khosrawi, K., Talebi, B., & Sahabi, F. (2000). The Meshkin Shahr geothermal prospect, Iran. In Proceedings of the World Geothermal Congress 2000, Kyushu-Tohoku, Japan (pp. 997-1002).
Candansayar, M. E. (2008). Two‐dimensional inversion of magnetotelluric data with consecutive use of conjugate gradient and least‐squares solution with singular value decomposition algorithms. Geophysical Prospecting, 56(1), 141-157.
Chave, A. D., & Jones, A. G. (Eds.). (2012). The magnetotelluric method: Theory and practice. Cambridge University Press.
deGroot-Hedlin, C., & Constable, S. (1990). Occam’s inversion to generate smooth, two-dimensional models from magnetotelluric data. Geophysics, 55(12), 1613-1624.
de Groot-Hedlin, C., & Constable, S. (2004). Inversion of magnetotelluric data for 2D structure with sharp resistivity contrasts. Geophysics, 69(1), 78-86.
Favetto, A., Pomposiello, C., de Luchi, M. G. L., & Booker, J. (2008). 2D Magnetotelluric interpretation of the crust electrical resistivity across the Pampean terrane–Río de la Plata suture, in central Argentina. Tectonophysics, 459(1-4), 54-65.
Haghighi, T. L., Montahaei, M., & Oskooi, B. (2018). MT data inversion and sensitivity analysis to image electrical structure of Zagros collision zone. Journal of Applied Geophysics, 148, 23-32.
McGary, R. S., Evans, R. L., Wannamaker, P. E., Elsenbeck, J., & Rondenay, S. (2014). Pathway from subducting slab to surface for melt and fluids beneath Mount Rainier. Nature, 511(7509), 338-340.
McNeice, G. W., & Jones, A. G. (2001). Multisite, multifrequency tensor decomposition of magnetotelluric data. Geophysics, 66(1), 158-173.
Munoz, G. (2014). Exploring for geothermal resources with electromagnetic methods. Surveys in geophysics, 35, 101-122.
Muñoz, G., Ritter, O., & Moeck, I. (2010). A target-oriented magnetotelluric inversion approach for characterizing the low enthalpy Groß Schönebeck geothermal reservoir. Geophysical Journal International, 183(3), 1199-1215.
Rodi, W., & Mackie, R. L. (2001). Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion. Geophysics, 66(1), 174-187.
Schwalenberg, K., Rath, V., & Haak, V. (2002). Sensitivity studies applied to a two-dimensional resistivity model from the Central Andes. Geophysical Journal International, 150(3), 673-686.
Seyedrahimi-Niaraq, M., Doulati Ardejani, F., Noorollahi, Y., & Porkhial, S. (2017). Development of an updated geothermal reservoir conceptual model for NW Sabalan geothermal field, Iran. Geothermal Energy, 5, 1-22.
Siripunvaraporn, W., & Egbert, G. (2000). An efficient data-subspace inversion method for 2-D magnetotelluric data. Geophysics, 65(3), 791-803.
Smith, T., Hoversten, M., Gasperikova, E., & Morrison, F. (1999). Sharp boundary inversion of 2D magnetotelluric data. Geophysical Prospecting, 47(4), 469-486.
Wannamaker, P. E., Stodt, J. A., & Rijo, L. (1986). Two-dimensional topographic responses in magnetotellurics modeled using finite elements. Geophysics, 51(11), 2131-2144.