References#
The development of pyCSAMT v2 draws on the following literature. Papers marked with ‡ are directly cited in the codebase docstrings.
pyCSAMT — software and field applications#
Kouadio, K.L., Liu, R., Mi, B., Liu, C. (2022). ‡ pyCSAMT: An alternative Python toolbox for groundwater exploration using controlled source audio-frequency magnetotelluric. Journal of Applied Geophysics, 201, 104647. https://doi.org/10.1016/j.jappgeo.2022.104647
Kouadio, L.K., Liu, R., Malory, A.O., Liu, W., Liu, C. (2023). A novel approach for water reservoir mapping using controlled source audio-frequency magnetotelluric in Xingning area, Hunan Province, China. Geophysical Prospecting, 71(4), 668–683. https://doi.org/10.1111/1365-2478.13385
Kouadio, K.L., Liu, J., Liu, W., Liu, R., Boukhalfa, Z. (2024). An integrated approach for sewage diversion: Case of the Huayuan mine, Hunan Province, China. Geophysics, 89(4), B241–B256. https://doi.org/10.1190/geo2023-0332.1
Kouadio, K.L. (2025). k-diagram: Rethinking forecasting uncertainty via polar-based visualization. Journal of Open Source Software, 10(116), 8661. https://doi.org/10.21105/joss.08661
CSAMT / MT methodology and source effects#
Yan, S., Fu, J. (2004). ‡ An analytical method to estimate shadow and source overprint effects in CSAMT sounding. Geophysics, 69(1), 161–163. https://doi.org/10.1190/1.1649384
Chen, M., Yan, S. (2005). Analytical study on field zones, record rules, shadow and source overprint effects in CSAMT exploration. Chinese Journal of Geophysics, 48(4), 1022–1031.
Da, L., Wu, X., Di, Q., Wang, G., Lv, X., Wang, R., Yang, J., Yue, M. (2016). Modeling and analysis of CSAMT field source effect and its characteristics. Journal of Geophysics and Engineering, 13(1), 49–58. https://doi.org/10.1088/1742-2132/13/1/49
Lei, D., Fayemi, B., Yang, L., Meng, X. (2017). The non-static effect of near-surface inhomogeneity on CSAMT data. Journal of Applied Geophysics, 139, 306–315. https://doi.org/10.1016/j.jappgeo.2017.03.003
Wang, K., Tan, H. (2017). Research on the forward modeling of controlled-source audio-frequency magnetotellurics in three-dimensional axial anisotropic media. Journal of Applied Geophysics, 146, 27–36. https://doi.org/10.1016/j.jappgeo.2017.08.007
Wang, S., Lin, C. (2023). A novel approach to address source overprint and shadow effects in controlled-source audio-frequency magnetotelluric exploration. Geophysics, 88(6), E215–E230. https://doi.org/10.1190/GEO2023-0003.1
Zhang, M., Farquharson, C.G., Liu, C. (2021). Improved CSAMT apparent resistivity pseudo sections based on the frequency and frequency-spatial gradients of electromagnetic fields. Geophysical Prospecting, 70(1), 211–229. https://doi.org/10.1111/1365-2478.13059
Fan, H., Zhang, Y., Wang, X. (2022). A novel phased-array transmitting source in controlled-source audio-frequency magnetotellurics. Journal of Geophysics and Engineering, 19, 595–614. https://doi.org/10.1093/jge/gxac023
Zhang, K., Zhang, R., Wang, M., Lin, Z., Zhang, Q., Jing, J., Li, F., Yang, S. (2025). Controlled source ultra-audio frequency magnetotellurics transmitter for high-resolution detection of urban shallow underground space. Measurement, 256, 118144. https://doi.org/10.1016/j.measurement.2025.118144
Airborne and passive EM methods#
Ward, S.H. (1959). ‡ AFMAG – airborne and ground. Geophysics, 24(4), 761–787. https://doi.org/10.1190/1.1438652
Liu, F., Huang, L., Pang, Y., Shi, Z., Xiao, P., Fang, G. (2018). ‡ Airborne AFMAG method motion-induced noise simulation and suppression. Journal of Applied Geophysics, 158, 129–138. https://doi.org/10.1016/j.jappgeo.2018.09.006
Lo, B., Zang, M. (2008). ‡ Numerical modeling of Z-TEM (airborne AFMAG) responses to guide exploration strategies. SEG Technical Program Expanded Abstracts, 27, 1098–1101.
Legault, J.M., Zhao, S., Fitch, R. (2012). ‡ ZTEM airborne AFMAG survey results over low sulphidation epithermal gold-silver vein systems at Gold Springs, south eastern Nevada. 22nd International Geophysical Conference and Exhibition (ASEG), Brisbane.
Sattel, D., Witherly, K. (2012). ‡ An overview of ZTEM data interpretation tools. 2012 NFEM Forum.
Pedersen, L.B., Qian, W., Dynesius, L., Zhang, P. (1994). ‡ An airborne tensor VLF system. From concept to realization. Geophysical Prospecting, 42(8), 863–883.
Wang, Y., Qu, J., Chen, T., Zhou, S., Li, Y. (2025). ‡ Studies of three dimensional staggered-grid finite difference for Z-axis tipper electromagnetic numerical simulation. Frontiers in Earth Science, 13, 1496312. https://doi.org/10.3389/feart.2025.1496312
Prikhodko, A., Bagrianski, A., Kuzmin, P., Sirohey, A. (2022). ‡ Natural field airborne electromagnetics – history of development and current exploration capabilities. Minerals, 12(5), 583. https://doi.org/10.3390/min12050583
Sattel, D., Witherly, K., Kaminski, V. (2019). ‡ A brief analysis of MobileMT data. SEG International Exposition and Annual Meeting, D043S102R007.
Zhdanov, M.S., Gribenko, A., Prikhodko, A., Sabra, H.E., Jorgensen, M., Cox, L.H. (2024). ‡ Three-dimensional MobileMT and TMI data inversions for mineral exploration. 1st ASEG DISCOVER Symposium.
Swift, C.M. (1967). ‡ A magnetotelluric investigation of an electrical conductivity anomaly in the southwestern United States. PhD thesis, MIT.
Tensor decomposition and dimensionality#
Caldwell, T.G., Bibby, H.M., Brown, C. (2004). ‡ The magnetotelluric phase tensor. Geophysical Journal International, 158(2), 457–469. https://doi.org/10.1111/j.1365-246X.2004.02281.x
Groom, R.W., Bailey, R.C. (1989). ‡ Decomposition of magnetotelluric impedance tensors in the presence of local three-dimensional galvanic distortion. Journal of Geophysical Research: Solid Earth, 94(B2), 1913–1925. https://doi.org/10.1029/JB094iB02p01913
Deep learning and AI-assisted inversion#
Liu, Z., Chen, H., Ren, Z., Tang, J., Xu, Z., Chen, Y., Liu, X. (2021). ‡ Deep learning audio magnetotellurics inversion using residual-based deep convolution neural network. Journal of Applied Geophysics, 188, 104309. https://doi.org/10.1016/j.jappgeo.2021.104309
Guo, R., Yao, H.M., Li, M., Ng, M.K.P., Jiang, L., Abubakar, A. (2021). ‡ Joint inversion of audio-magnetotelluric and seismic travel time data with deep learning constraint. IEEE Transactions on Geoscience and Remote Sensing, 59(9), 7982–7995. https://doi.org/10.1109/TGRS.2020.3032743
Oh, S., Noh, K., Yoon, D., Seol, S.J., Byun, J. (2019). Salt delineation from electromagnetic data using convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 16(8), 1202–1206. https://doi.org/10.1109/LGRS.2018.2877155
Oh, S., Noh, K., Seol, S.J., Byun, J. (2020). ‡ Cooperative deep learning inversion of controlled-source electromagnetic data for salt delineation. Geophysics, 85(4), E121–E137. https://doi.org/10.1190/GEO2019-0532.1
Moghadas, D. (2020). One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network. Geophysical Journal International, 223(1), 198–212. https://doi.org/10.1093/gji/ggaa202
Puzyrev, V., Meka, S., Swidinsky, A. (2019). Deep convolutional neural networks for 1D inversion of electromagnetic data. 81st EAGE Conference & Exhibition, London. https://doi.org/10.3997/2214-4609.201900685
Puzyrev, V., Swidinsky, A. (2021). ‡ Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks. Computers & Geosciences, 149, 104681. https://doi.org/10.1016/j.cageo.2021.104681
Inversion algorithms and rock physics#
deGroot-Hedlin, C., Constable, S. (1990). ‡ Occam’s inversion to generate smooth, two-dimensional models from magnetotelluric data. Geophysics, 55(12), 1613–1624. https://doi.org/10.1190/1.1442303
Kelbert, A., Meqbel, N., Egbert, G.D., Tandon, K. (2014). ‡ ModEM: A modular system for inversion of electromagnetic geophysical data. Computers & Geosciences, 66, 40–53. https://doi.org/10.1016/j.cageo.2014.01.010
Palacky, G.J. (1988). ‡ Resistivity characteristics of geologic targets. In: Nabighian, M.N. (Ed.), Electromagnetic Methods in Applied Geophysics, Vol. 1. SEG, Tulsa, pp. 53–129.
Data standards#
Society of Exploration Geophysicists (1991). MT/EMAP Data Interchange Standard (revised edition). SEG Technical Standards Committee, Tulsa, Oklahoma. (Original standard adopted 14 December 1987.)
Ward, S.H., Hohmann, G.W. (1988). ‡ Electromagnetic theory for geophysical applications. In: Nabighian, M.N. (Ed.), Electromagnetic Methods in Applied Geophysics, Vol. 1. SEG, Tulsa, pp. 131–311.
Nabighian, M.N., Macnae, J.C. (1991). ‡ Time domain electromagnetic prospecting methods. In: Nabighian, M.N. (Ed.), Electromagnetic Methods in Applied Geophysics, Vol. 2. SEG, Tulsa, pp. 427–520.
Christiansen, A.V., Auken, E., Sorensen, K. (2009). ‡ The transient electromagnetic method. In: Kirsch, R. (Ed.), Groundwater Geophysics: A Tool for Hydrogeology, 2nd ed. Springer, pp. 179–226.
Bundled example datasets#
The real field surveys bundled with pyCSAMT for docstrings, tests, and documentation examples come from published field studies. Please cite the relevant paper below if you reuse this data beyond exploring pyCSAMT itself.
Kouabena, K.A.W., Zhou, J., Chen, R., Yin, L., Cai, H., Lu, Z., Gu, J., Yu, W. (2025). Enhanced prediction of deep-seated Cu-Mo porphyry mineralization: A comprehensive interpretation based on 2D inversion of audio-magnetotelluric data. Ore Geology Reviews, 185, 106798. https://doi.org/10.1016/j.oregeorev.2025.106798
(Field dataset: data/AMT/WILLY_DATA/ — see the README there for
which lines are bundled.)
Kouadio, K.L., Xu, Y., Liu, C., Boukhalfa, Z. (2020). Two-dimensional inversion of CSAMT data and three-dimensional geological mapping for groundwater exploration in Tongkeng Area, Hunan Province, China. Journal of Applied Geophysics, 104204. https://doi.org/10.1016/j.jappgeo.2020.104204
(Field dataset: data/CSAMT/ — see the README there for survey
details.)