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Статті в журналах з теми "Magnetotelluric inversion"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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1

Matsuno, Tetsuo, Alan D. Chave, Alan G. Jones, Mark R. Muller, and Rob L. Evans. "Robust magnetotelluric inversion." Geophysical Journal International 196, no. 3 (January 2, 2014): 1365–74. http://dx.doi.org/10.1093/gji/ggt484.

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2

Schaa, Ralf, Brett Harris, and Andrew Pethick. "Magnetotelluric inversion strategies." ASEG Extended Abstracts 2019, no. 1 (November 11, 2019): 1–6. http://dx.doi.org/10.1080/22020586.2019.12073167.

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3

Liao, Chen, Xiangyun Hu, Shihui Zhang, Xuewen Li, Quanzeng Yin, Zhao Zhang, and Longfei Zhang. "Joint inversion of gravity, magnetotelluric and seismic data using the alternating direction method of multipliers." Geophysical Journal International 229, no. 1 (November 11, 2021): 203–18. http://dx.doi.org/10.1093/gji/ggab463.

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SUMMARY Joint inversion for the same or different geophysical parameters is proved to be an effective technique for obtaining high-resolution solutions. Thus, comprehensive geophysical interpretation based on joint inversion has been widely concerned and applied in recent years. To realize joint inversion conveniently and efficiently, we proposed a new inversion strategy based on the alternating direction method of multipliers. In this regard, three optimization algorithms were presented respectively to attain the joint inversion of body wave traveltime and surface wave dispersion data, to obtain the joint inversion of magnetotelluric and seismic data with cross-gradient constraints, and to acquire gravity constrained inversion. A complex model with inconsistent structures in terms of resistivity, velocity and density was designed to evaluate the accuracy and effectiveness of the multiparameter joint inversion algorithms. In our joint inversion processes, each method was optimized independently and the jointly inverted results were significantly more accurate than those of separate inversions. Finally, we applied the algorithms to the field data involving gravity anomaly data, magnetotelluric data and Rayleigh wave dispersion data. The reliable underground structure was achieved by the joint interpretation of density, resistivity and velocity profiles, which verified the practicality of the inversion strategy in the actual data.

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4

Srnka, L. J., and W. Y. CrutchfieldII. "Riccati inversion of magnetotelluric data." Geophysical Journal International 91, no. 1 (October 1987): 211–28. http://dx.doi.org/10.1111/j.1365-246x.1987.tb05221.x.

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5

Smith, J. Torquil, and John R. Booker. "Magnetotelluric inversion for minimum structure." GEOPHYSICS 53, no. 12 (December 1988): 1565–76. http://dx.doi.org/10.1190/1.1442438.

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Анотація:

Structure can be measured in terms of a norm of the derivative of a model with respect to a function of depth f(z), where the model m(z) is either the conductivity σ or log σ. An iterative linearized algorithm can find models that minimize norms of this form for chosen levels of chi‐squared misfit. The models found may very well be global minima of these norms, since they are not observed to depend on the starting model. Overfitting data causes extraneous structure. Some choices of the depth function result in systematic overfitting of high frequencies, a “blue” fit, and extraneous shallow structure. Others result in systematic overfitting of low frequencies, a “red” fit, and extraneous deep structure. A robust statistic is used to test for whiteness; the fit can be made acceptably white by varying the depth function f(z) which defines the norm. An optimum norm produces an inversion which does not introduce false structure and which approaches the true structure in a reasonable way as data errors decrease. Linearization errors are often so small that models of σ (but not log σ) may be reasonably interpreted as the true conductivity averaged through known resolution functions.

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6

Bawahab, Nabil, Udi Harmoko, Tony Yulianto, and Irvan Ramadhan. "Identification of low resistivity layers in the “N” geothermal field using 2D magnetotelluric inversion modelling." Journal of Physics and Its Applications 2, no. 2 (May 11, 2020): 85–89. http://dx.doi.org/10.14710/jpa.v2i2.7532.

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Magnetotelluric research in the “N” geothermal field has been carried out to see the subsurface detail in the “N” geothermal field. 2D inversion model is generated by secondary data from magnetotelluric data collection in the form of time series data to become 2D models. Magnetotellurics method is used to identify geothermal system components, especially identifying layers with low resistivity values (2 Ω.m - 10 Ω.m) or also called as the cap rock which is seen with a very contrasting color difference compared to the surrounding layers. There are manifestations on the “N” geothermal field which reinforce the assumption that there is a geothermal system in this area. This research begins by processing time series data to become apparent resistivity and phase data. Time series data processing in this study uses several processing methods to produce better apparent resistivity and phase data. The final result of this study is a 2D model that illustrates the contour of the resistivity value of rocks laterally or vertically. 2D model interpretation in this study identified the cap rock layer with low resistivity distribution (2 Ω.m - 10 Ω.m), the medium resistivity zone identified as the reservoir layer (11 Ω.m - 70 Ω.m), and the resistive zone which has high resistivity value (more than 70 Ω.m).

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7

Wang, Shunguo, Mehrdad Bastani, Steven Constable, Thomas Kalscheuer, and Alireza Malehmir. "Boat-towed radio-magnetotelluric and controlled source audio-magnetotelluric study to resolve fracture zones at Äspö Hard Rock Laboratory site, Sweden." Geophysical Journal International 218, no. 2 (April 23, 2019): 1008–31. http://dx.doi.org/10.1093/gji/ggz162.

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SUMMARY Boat-towed radio-magnetotelluric (RMT) measurements using signals between 14 and 250 kHz have attracted increasing attention in the near-surface applications for shallow water and archipelago areas. A few large-scale underground infrastructure projects, such as the Stockholm bypass in Sweden, are planned to pass underneath such water zones. However, in cases with high water salinity, RMT signals have a penetration depth of a few metres and do not reach the geological structures of interest in the underlying sediments and bedrock. To overcome this problem, controlled source signals at lower frequencies of 1.25 to 12.5 kHz can be utilized to improve the penetration depth and to enhance the resolution for modelling deeper underwater structures. Joint utilization of boat-towed RMT and controlled source audio-magnetotellurics (CSAMT) was tested for the first time at the Äspö Hard Rock Laboratory (HRL) site in south-eastern Sweden to demonstrate acquisition efficiency and improved resolution to model fracture zones along a 600-m long profile. Pronounced galvanic distortion effects observed in 1-D inversion models of the CSAMT data as well as the predominantly 2-D geological structures at this site motivated usage of 2-D inversion. Two standard academic inversion codes, EMILIA and MARE2DEM, were used to invert the RMT and CSAMT data. EMILIA, an object-oriented Gauss–Newton inversion code with modules for 2-D finite difference and 1-D semi-analytical solutions, was used to invert the RMT and CSAMT data separately and jointly under the plane-wave approximation for 2-D models. MARE2DEM, a Gauss–Newton inversion code for controlled source electromagnetic 2.5-D finite element solution, was modified to allow for inversions of RMT and CSAMT data accounting for source effects. Results of EMILIA and MARE2DEM reveal the previously known fracture zones in the models. The 2-D joint inversions of RMT and CSAMT data carried out with EMILIA and MARE2DEM show clear improvement compared with 2-D single inversions, especially in imaging uncertain fracture zones analysed in a previous study. Our results show that boat-towed RMT and CSAMT data acquisition systems can be utilized for detailed 2-D or 3-D surveys to characterize near-surface structures underneath shallow water areas. Potential future applications may include geo-engineering, geohazard investigations and mineral exploration.

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8

Wittke, J., and B. Tezkan. "Two-dimensional meshless modelling and TE-mode inversion of magnetotelluric data." Geophysical Journal International 226, no. 2 (April 14, 2021): 1250–61. http://dx.doi.org/10.1093/gji/ggab147.

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SUMMARY We present a new 2-D forward modelling and inversion scheme to interpret magnetotelluric/radio-magnetotelluric data by utilizing a novel meshless forward operator. We use this discretization technique within an inverse scheme to recover conductivity structures from given magnetotelluric data. To approximate solutions of the partial differential equations that describe the magnetotelluric experiment, we discretize the subsurface only in terms of nodes. These node sets, which are simple to generate, are used to derive the differential operators’ approximations in a generalized meshless framework. First, we study and compare forward modelling calculations to an analytical and known solution from the literature. Several example calculations are given, which validate the proposed meshless forward operator. We then formulate our inverse scheme for TE-mode data, which uses only subsets of the nodal subsurface parametrization to generate conductivity structures from this given data. The inverse scheme consists of a Gauss–Newton algorithm combined with the generalized meshless framework. To validate the algorithm, we present inversion results from synthetic and field data. We compare our results to conductivity models calculated by established, well-known inversion schemes and literature results. We report that our algorithm can accurately model magnetotelluric responses and recover meaningful conductivity models, explaining given magnetotelluric data.

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9

Buland, Arild, and Odd Kolbjørnsen. "Bayesian inversion of CSEM and magnetotelluric data." GEOPHYSICS 77, no. 1 (January 2012): E33—E42. http://dx.doi.org/10.1190/geo2010-0298.1.

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We have developed a Bayesian methodology for inversion of controlled source electromagnetic (CSEM) data and magnetotelluric (MT) data. The inversion method provided optimal solutions and also the associated uncertainty for any sets of electric and magnetic components and frequencies from CSEM and MT data. The method is based on a 1D forward modeling method for the electromagnetic (EM) response for a plane-layered anisotropic earth model. The inversion method was also designed to invert common midpoint (CMP)-sorted data along a 2D earth profile assuming locally horizontal models in each CMP position. The inversion procedure simulates from the posterior distribution using a Markov chain Monte Carlo (McMC) approach based on the Metropolis-Hastings algorithm. The method that we use integrates available geologic prior knowledge with the information in the electromagnetic data such that the prior model stabilizes and constrains the inversion according to the described knowledge. The synthetic examples demonstrated that inclusion of more data generally improves the inversion results. Compared to inversion of the inline electric component only, inclusion of broadside and magnetic components and an extended set of frequency components moderately decreased the uncertainty of the inversion. The results were strongly dependent on the prior knowledge imposed by the prior distribution. The prior knowledge about the background resistivity model surrounding the target was highly important for a successful and reliable inversion result.

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10

Wiik, Torgeir, Ketil Hokstad, Bjørn Ursin, and Lutz Mütschard. "Joint contrast source inversion of marine magnetotelluric and controlled-source electromagnetic data." GEOPHYSICS 78, no. 6 (November 1, 2013): E315—E327. http://dx.doi.org/10.1190/geo2012-0477.1.

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We evaluated a joint contrast source inversion scheme for marine controlled-source electromagnetic (mCSEM) and magnetotelluric (MT) data based on a scattered field formulation. The scheme considered only contrasts in electric conductivity, and it allowed the medium to be transversely isotropic with a vertical symmetry axis. The method was based on the integral equation formulation of electromagnetic field propagation, and we demonstrated how the method solved the inverse problem of determining the conductivity structure of the subsurface. The method did not consider MT impedances as data input to inversion, but instead explicitly the field components, and the consequences of this approach, were discussed. Although there are challenges associated with source estimation and data noise, we found it easier to make connections to CSEM and it simplified some computational issues. Three synthetic examples were considered to demonstrate the method: a reservoir below an anisotropic overburden, a salt diapir, and a reservoir near a salt diapir. MT and CSEM data were first treated sequentially, first inverting the MT data and using the result as the initial model and in the regularization in CSEM inversion. The result of this approach was then compared to a joint inversion. The same approach was finally applied to a real data set. We found that sequential inversions in some situations produced similar results as joint inversions, and hence, joint inversion may not be necessary in all situations. Nonetheless, joint inversion could be useful for imaging salt diapirs and eventually hydrocarbons near salt. In particular, it was useful to map the spatial extent of the salt diapirs. It was, moreover, a useful tool for checking data consistency in different models with respect to several data types.

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11

Hidalgo-Silva, Hugo, and Enrique Gómez-Treviño. "Impulse noise treatment in magnetotelluric inversion." Open Geosciences 13, no. 1 (January 1, 2021): 130–37. http://dx.doi.org/10.1515/geo-2020-0225.

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Abstract The problem of model recovering in the presence of impulse noise on the data is considered for the magnetotelluric (MT) inverse problem. The application of total variation regularization along with L1-norm penalized data fitting (TVL1) is the usual approach for the impulse noise treatment in image recovery. This combination works poorly when a high level of impulse noise is present on the data. A nonconvex operator named smoothly clipped absolute deviation (TVSCAD) was recently applied to the image recovery problem. This operator is solved using a sequence of TVL1 equivalent problems, providing a significant improvement over TVL1. In practice, TVSCAD requires the selection of several parameters, a task that can be very difficult to attain. A more simple approach to the presence of impulse noise in data is presented here. A nonconvex function is also considered in the data fitness operator, along with the total variation regularization operator. The nonconvex operator is solved by following a half-quadratic procedure of minimization. Results are presented for synthetic and also for field data, assessing the proposed algorithm’s capacity in model recovering under the influence of impulse noise on data for the MT problem.

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12

Avdeev, Dmitry B., and Anna D. Avdeeva. "A RIGOROUS THREE-DIMENSIONAL MAGNETOTELLURIC INVERSION." Progress In Electromagnetics Research 62 (2006): 41–48. http://dx.doi.org/10.2528/pier06041205.

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13

Cao, Hui, Yun Liu, and Kun Peng Wang. "Improved Magnetotelluric Zohdy-Oldenburg Direct Inversion." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/174586.

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Based on researches of Baiyao (1995) and Zhang and Xu (2001), this paper proposes an improved 2D MT Zohdy-Oldenburg direct inversion method, in the least-square sense, embodying the features of Zohdy’s ratio method and Oldenburg’s difference method, in the condition of rugged topography, with phase information. It bypasses large calculations of the Jacobian matrix and large sparse linear systems of equations and enables direct modifications and comparisons of the model parameters. According to the calculation and analysis of examples, it shows faster convergence and higher precision. In contrast with the conventional linear inversion, the calculation speed of this new method can be increased by more than 10 times.

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14

Mandolesi, E., and A. G. Jones. "Magnetotelluric inversion based on mutual information." Geophysical Journal International 199, no. 1 (August 5, 2014): 242–52. http://dx.doi.org/10.1093/gji/ggu258.

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15

Dosso, S. E., and D. W. Oldenburg. "The similitude equation in magnetotelluric inversion." Geophysical Journal International 106, no. 2 (August 1991): 507–9. http://dx.doi.org/10.1111/j.1365-246x.1991.tb03910.x.

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16

Plotkin, V. V. "Inversion of heterogeneous anisotropic magnetotelluric responses." Russian Geology and Geophysics 53, no. 8 (August 2012): 829–36. http://dx.doi.org/10.1016/j.rgg.2012.06.010.

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17

MacBain, J. A. "Inversion theory for the magnetotelluric problem." Inverse Problems 3, no. 3 (August 1, 1987): 453–61. http://dx.doi.org/10.1088/0266-5611/3/3/013.

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18

Simpson, Janelle, and Graham Heinson. "Estimating interpretation uncertainty from magnetotelluric inversion." ASEG Extended Abstracts 2019, no. 1 (November 11, 2019): 1–5. http://dx.doi.org/10.1080/22020586.2019.12073138.

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19

Madden, T. M., and R. L. Mackie. "Three-dimensional magnetotelluric modelling and inversion." Proceedings of the IEEE 77, no. 2 (1989): 318–33. http://dx.doi.org/10.1109/5.18628.

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20

Limswipin, Elen Novia, Syamsurijal Rasimeng, Karyanto Karyanto, and Noor Muhammad Indragiri. "INVERSI 2D DATA MAGNETOTELURIK UNTUK MENGETAHUI KEBERADAAN HIDROKARBON DAERAH BULA, MALUKU." Jurnal Geofisika Eksplorasi 4, no. 3 (January 17, 2020): 15–27. http://dx.doi.org/10.23960/jge.v4i3.38.

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There had been done a regional research which tittle is “2D inversion magnetotelluric data for understanding the hidrocarbon presence in Bula, Maluku”. This study aims to determine the resistivity distribution area of research based on data Magnetotelluric, identifying the presence of hydrocarbons based on the value of the resistivity of the results of 2D inversion of data Magnetotelluric. Methods of data processing done are (i) transform raw data from the time domain into the frequency domain, (ii) reduce noise by robust processing, (iii) process combine, (iv) Selection cross power, (v) inversion 1D and 2D. 2D inversion results is sectional subsurface resistivity distribution, layer having resistivity values 7-16 Ωm along MT1 and MT7 point at a depth of 1000 meters is a clay stone which is indicated as cap rock. Layer with resistivity values 34-120 Ωm, which is between the point MT6 and MT7 at a depth of 1500 meters is indicated as the sandstone reservoir. Based on geologic information and sectional 2D inversion seen their fault based on the resistivity contrast is between the point MT2 and MT3, MT3 and MT4 and MT6 and MT7.

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21

Moorkamp, M., A. Avdeeva, Ahmet T. Basokur, and Erhan Erdogan. "Inverting magnetotelluric data with distortion correction—stability, uniqueness and trade-off with model structure." Geophysical Journal International 222, no. 3 (June 4, 2020): 1620–38. http://dx.doi.org/10.1093/gji/ggaa278.

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SUMMARY Galvanic distortion of magnetotelluric (MT) data is a common effect that can impede the reliable imaging of subsurface structures. Recently, we presented an inversion approach that includes a mathematical description of the effect of galvanic distortion as inversion parameters and demonstrated its efficiency with real data. We now systematically investigate the stability of this inversion approach with respect to different inversion strategies, starting models and model parametrizations. We utilize a data set of 310 MT sites that were acquired for geothermal exploration. In addition to impedance tensor estimates over a broad frequency range, the data set also comprises transient electromagnetic measurements to determine near surface conductivity and estimates of distortion at each site. We therefore can compare our inversion approach to these distortion estimates and the resulting inversion models. Our experiments show that inversion with distortion correction produces stable results for various inversion strategies and for different starting models. Compared to inversions without distortion correction, we can reproduce the observed data better and reduce subsurface artefacts. In contrast, shifting the impedance curves at high frequencies to match the transient electromagnetic measurements reduces the misfit of the starting model, but does not have a strong impact on the final results. Thus our results suggest that including a description of distortion in the inversion is more efficient and should become a standard approach for MT inversion.

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22

Feng, Xuan, Enhedelihai Nilot, Cai Liu, Minghe Zhang, Hailong Yu, Jianyu Zhao, and Chengcheng Sun. "Joint Inversion of Seismic and Audio Magnetotelluric Data with Structural Constraint for Metallic Deposit." Journal of Environmental and Engineering Geophysics 23, no. 2 (June 2018): 159–69. http://dx.doi.org/10.2113/jeeg23.2.159.

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Audio magnetotelluric (AMT) and seismic methods are widely used to detect metallic deposits. However, each geophysical method only provides partial information of the underground target. Besides, individual methods have inherent limitations and ambiguity which leads to non-uniqueness when solving the inverse problem. To obtain a more robust and consistent ore deposit model, it is best to integrate different geophysical methods and data types. Towards this effort, we propose a joint inversion algorithm using cross-gradient constraint to build a connection between seismic and AMT data, and simultaneously invert for a resistivity and P-wave velocity model. Compared with separate AMT Gauss–Newton inversion and seismic Full waveform inversion (FWI) method, we can get more detailed and robust inversion results. In addition, frequency domain FWI with the Limited-Memory-Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm provides an effective way to reduce computer memory usage and improve convergence speed. This joint inversion algorithm has been tested using simple synthetic models with two cross targets. The results obtained with separate inversions were compared with those obtained with joint inversion. Then, we applied the algorithm to geophysical models of the Jinchuan sulfide deposit. The AMT results obtained with joint inversion of seismic data were better than those obtained with separate AMT inversion. The joint inversion approach appears more robust than the traditional separate FWI inversion and it is recommended that the proposed algorithm be considered in future projects of real field data.

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23

Maier, R., G. Heinson, M. Tingay, and S. Greenhalgh. "Joint inversion of gravity and magnetotelluric data." ASEG Extended Abstracts 2009, no. 1 (2009): 1. http://dx.doi.org/10.1071/aseg2009ab045.

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24

Conway, Dennis, and Graham Heinson. "Magnetotelluric modelling: towards a 4-D inversion." ASEG Extended Abstracts 2016, no. 1 (December 2016): 1–2. http://dx.doi.org/10.1071/aseg2016ab244.

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25

Siripunvaraporn, Weerachai, Makoto Uyeshima, and Gary Egbert. "Three-dimensional inversion for Network-Magnetotelluric data." Earth, Planets and Space 56, no. 9 (September 2004): 893–902. http://dx.doi.org/10.1186/bf03352536.

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26

Mackie, Randall L., and Theodore R. Madden. "Three-dimensional magnetotelluric inversion using conjugate gradients." Geophysical Journal International 115, no. 1 (October 1993): 215–29. http://dx.doi.org/10.1111/j.1365-246x.1993.tb05600.x.

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27

Zhang, Yuanchou, and K. V. Paulson. "Magnetotelluric inversion using regularized Hopfield neural networks." Geophysical Prospecting 45, no. 5 (September 1997): 725–43. http://dx.doi.org/10.1046/j.1365-2478.1997.660299.x.

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28

Smith, Torquil, Michael Hoversten, Erika Gasperikova, and Frank Morrison. "Sharp boundary inversion of 2D magnetotelluric data." Geophysical Prospecting 47, no. 4 (July 1999): 469–86. http://dx.doi.org/10.1046/j.1365-2478.1999.00145.x.

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29

Siripunvaraporn, Weerachai, Gary Egbert, Yongwimon Lenbury, and Makoto Uyeshima. "Three-dimensional magnetotelluric inversion: data-space method." Physics of the Earth and Planetary Interiors 150, no. 1-3 (May 2005): 3–14. http://dx.doi.org/10.1016/j.pepi.2004.08.023.

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30

Lu, Libin, Kunpeng Wang, Handong Tan, and Qingkun Li. "Three-dimensional magnetotelluric inversion using L-BFGS." Acta Geophysica 68, no. 4 (June 30, 2020): 1049–66. http://dx.doi.org/10.1007/s11600-020-00456-7.

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31

Song, Wei-qi, and Shan Sun. "Magnetotelluric data inversion with seismic data constraint." Acta Seismologica Sinica 18, no. 6 (November 2005): 678–85. http://dx.doi.org/10.1007/s11589-005-0095-8.

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32

Feng, Deshan, Xuan Su, Xun Wang, Siyuan Ding, Cen Cao, Shuo Liu, and Yi Lei. "Magnetotelluric Regularized Inversion Based on the Multiplier Method." Minerals 12, no. 10 (September 28, 2022): 1230. http://dx.doi.org/10.3390/min12101230.

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Анотація:

Magnetotellurics (MT) is an important geophysical method for resource exploration and mineral evaluation. As a direct and effective form of data interpretation, MT inversion is usually considered to be a penalty-function constraint-based optimization strategy. However, conventional MT inversion involves a large number of calculations in penalty terms and causes difficulties in selecting exact regularization factors. For this reason, we propose a multiplier-based MT inversion scheme, which is implemented by introducing the incremental Lagrangian function. In this case, it can avoid the exact solution of the primal-dual subproblem in the penalty function and further reduce the sensitivity of the regularization factors, thus achieving the goal of improving the convergence efficiency and accelerating the optimization calculation of the inverse algorithm. In this study, two models were used to verify the performance of the multiplier method in the regularized MT inversion. The first experiment, with an undulating two-layer model of metal ore, verified that the multiplier method could effectively avoid the MT inversion falling into local minimal. The second experiment, with a wedge model, showed that the multiplier method has strong robustness, due to which it can expand the selection range and reduce the difficulty of the regularization factors. We tested the feasibility of the multiplier method in field data. We compared the results of the multiplier method with those of conventional inversion methods in order to verify the accuracy of the multiplier method.

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33

Zhdanov, Michael S., Sheng Fang, and Gábor Hursán. "Electromagnetic inversion using quasi‐linear approximation." GEOPHYSICS 65, no. 5 (September 2000): 1501–13. http://dx.doi.org/10.1190/1.1444839.

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Three‐dimensional electromagnetic inversion continues to be a challenging problem in electrical exploration. We have recently developed a new approach to the solution of this problem based on quasi‐linear approximation of a forward modeling operator. It generates a linear equation with respect to the modified conductivity tensor, which is proportional to the reflectivity tensor and the complex anomalous conductivity. We solved this linear equation by using the regularized conjugate gradient method. After determining a modified conductivity tensor, we used the electrical reflectivity tensor to evaluate the anomalous conductivity. Thus, the developed inversion scheme reduces the original nonlinear inverse problem to a set of linear inverse problems. The developed algorithm has been realized in computer code and tested on synthetic 3-D EM data. The case histories include interpretation of a 3-D magnetotelluric survey conducted in Hokkaido, Japan, and the 3-D inversion of the tensor controlled‐source audio magnetotelluric data over the Sulphur Springs thermal area, Valles Caldera, New Mexico, U.S.A.

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34

Wang, Han, Yunhe Liu, Changchun Yin, Jinfeng Li, Yang Su, and Bin Xiong. "Stochastic inversion of magnetotelluric data using deep reinforcement learning." GEOPHYSICS 87, no. 1 (December 21, 2021): E49—E61. http://dx.doi.org/10.1190/geo2020-0425.1.

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We have adopted a new tool to invert magnetotelluric data for the 1D model based on deep Q-networks (DQN), which works as a stochastic optimization method. By transforming the inversion problem into a Markov decision process, the tool learns by trial and error to find the optimal path for updating the model to fit the observed data. The DQN method converges to the target through different paths (e.g., Bayesian or other stochastic methods) and can partially provide the probability distribution of the inversion results, which can be used for uncertainty estimation. The DQN search space gradually decreases as the learning experience progresses, accelerating the single inversion and approximating the optimal result. To check the effectiveness of the DQN inversion, the five- and eight-layer models were separately designed to test the robustness of the DQN for the initial model and the noise level. A further comparison with Occam’s method and the Bayesian method indicated that our DQN obtained more robust inversion results for data contaminated by different noise levels. The inversion results with the survey data from Zhagaitunuoergong area, Inner Mongolia, China, well reveals the shape of the interface basement.

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35

Kang, Seogi, Lindsey J. Heagy, Rowan Cockett, and Douglas W. Oldenburg. "Exploring nonlinear inversions: A 1D magnetotelluric example." Leading Edge 36, no. 8 (August 2017): 696–99. http://dx.doi.org/10.1190/tle36080696.1.

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At some point in many geophysical workflows, an inversion is a necessary step for answering the geoscientific question at hand, whether it is recovering a reflectivity series from a seismic trace in a deconvolution problem, finding a susceptibility model from magnetic data, or recovering conductivity from an electromagnetic survey. This is particularly true when working with data sets where it may not even be clear how to plot the data: 3D direct current resistivity and induced polarization surveys (it is not necessarily clear how to organize data into a pseudosection) or multicomponent data, such as electromagnetic data (we can measure three spatial components of electric and/or magnetic fields through time over a range of frequencies). Inversion is a tool for translating these data into a model we can interpret. The goal of the inversion is to find a “model” — some description of the earth's physical properties — that is consistent with both the data and geologic knowledge.

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36

Moorkamp, Max, and Anna Avdeeva. "Using non-diagonal data covariances in geophysical inversion." Geophysical Journal International 222, no. 2 (May 13, 2020): 1023–33. http://dx.doi.org/10.1093/gji/ggaa235.

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SUMMARY We present a new approach that allows for the inversion of quantities derived from the observed data using non-diagonal data covariance matrices. For example, we can invert approximations of apparent resistivity and phase instead of magnetotelluric impedance using this methodology. Compared to the direct inversion of these derived quantities, the proposed methodology has two advantages: (i) If an inversion algorithm allows for the specification of a full data covariance matrix, users can invert for arbitrary derived quantities by specifying the appropriate covariance matrix instead of having to rely on the inversion code to have implemented this feature. (ii) It is fully compatible with the assumptions of least-squares optimization and thus avoids potential issues with bias when inverting quantities that are nonlinear functions of the original data, We discuss the theory of this approach and show an example using magnetotelluric data. However, the same method can be applied to other types of geophysical data, for example gravity gradient measurements.

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37

Gallardo, L. A., S. L. Fontes, M. A. Meju, M. P. Buonora, and P. P. de Lugao. "Robust geophysical integration through structure-coupled joint inversion and multispectral fusion of seismic reflection, magnetotelluric, magnetic, and gravity images: Example from Santos Basin, offshore Brazil." GEOPHYSICS 77, no. 5 (September 1, 2012): B237—B251. http://dx.doi.org/10.1190/geo2011-0394.1.

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We have applied a crossgradient joint inversion and geospectral visualization method to marine seismic reflection, magnetotelluric, gravity, and magnetic data sets acquired along a 162 km profile across a segment of Santos Basin oil province in the continental margin of southeast Brazil. The main exploration targets are the top of the fractured Precambrian crystalline basement and any concealed basement grabens, the overlying presalt and salt/carbonate deposits, and the postsalt cover deposits. The results of joint inversion clearly mapped the various units and are a significant improvement over previous models derived from separate 2D seismic reflection processing and 2D magnetotelluric imaging. Additionally, multispectral fusion of these models resulted in a single image that permits highly constrained geologic interpretations enabling a better understanding of basin architecture. We suggest that joint inversion and image fusion is the way forward for effective geophysical integration.

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38

Mitsuhata, Yuji, Toshihiro Uchida, and Hiroshi Amano. "2.5‐D inversion of frequency‐domain electromagnetic data generated by a grounded‐wire source." GEOPHYSICS 67, no. 6 (November 2002): 1753–68. http://dx.doi.org/10.1190/1.1527076.

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Interpretation of controlled‐source electromagnetic (CSEM) data is usually based on 1‐D inversions, whereas data of direct current (dc) resistivity and magnetotelluric (MT) measurements are commonly interpreted by 2‐D inversions. We have developed an algorithm to invert frequency‐Domain vertical magnetic data generated by a grounded‐wire source for a 2‐D model of the earth—a so‐called 2.5‐D inversion. To stabilize the inversion, we adopt a smoothness constraint for the model parameters and adjust the regularization parameter objectively using a statistical criterion. A test using synthetic data from a realistic model reveals the insufficiency of only one source to recover an acceptable result. In contrast, the joint use of data generated by a left‐side source and a right‐side source dramatically improves the inversion result. We applied our inversion algorithm to a field data set, which was transformed from long‐offset transient electromagnetic (LOTEM) data acquired in a Japanese oil and gas field. As demonstrated by the synthetic data set, the inversion of the joint data set automatically converged and provided a better resultant model than that of the data generated by each source. In addition, our 2.5‐D inversion accounted for the reversals in the LOTEM measurements, which is impossible using 1‐D inversions. The shallow parts (above about 1 km depth) of the final model obtained by our 2.5‐D inversion agree well with those of a 2‐D inversion of MT data.

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39

Mütschard, Lutz, Ketil Hokstad, Torgeir Wiik, and Bjørn Ursin. "3D marine magnetotelluric inversion: A hybrid impedance and direct-field approach." GEOPHYSICS 82, no. 6 (November 1, 2017): E335—E346. http://dx.doi.org/10.1190/geo2015-0394.1.

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The measured electromagnetic field in magnetotellurics (MT) is composed of the natural source field and its subsurface response. Commonly, the data are represented as impedances, the complex ratio between the horizontal electric and magnetic fields. This measure is independent of the source distribution because the impedance-tensor estimation contains a deconvolution operator. We have used a Gauss-Newton-type 3D MT inversion scheme to compare impedance-data inversion with an inversion using the recorded electric field directly. The use of the observed electric field is beneficial to the inversion algorithm because it simplifies the estimation of the sensitivities. The direct-field approach permits the use of the observed data without processing, but it presumes knowledge of the source distribution. A method to estimate the time-variable strength and polarization of the incoming plane-wave source is presented and tested on synthetic and real-data examples. The direct-field inversion is successfully applied to a synthetic and a real data set within marine settings. A comparison with the conventional impedance inversion is conducted. The results of the synthetic data example are very similar, with a slightly more accurate reconstruction of the model in the impedance case, whereas the direct-field inversion produces a smoother inversion result when compared with the impedance case. The mapping of a resistive salt structure in the real-data example indicates deviations in the final conductivity models. The impedance inversion suggests a deeper rooted resistive structure, whereas the direct-field inversion predicts a more compact structure limited to the overburden. We have evaluated the advantages of the new approach like the simplification of the sensitivity calculation, limitations, and disadvantages like knowledge of the source distribution.

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40

Zabinyakova, O. B. "APPLICATION OF THE L-CURVE METHOD IN DETERMINING THE QUASI-OPTIMAL REGULARIZATION PARAMETER FOR TWO-DIMENSIONAL INVERSION OF MAGNETOTELLURIC DATA AT THE BISHKEK GEODYNAMIC TEST SITE." Bulletin of Kamchatka Regional Association «Educational-Scientific Center». Earth Sciences, no. 2(50) (June 30, 2021): 95–105. http://dx.doi.org/10.31431/1816-5524-2021-2-50-95-105.

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41

Siripunvaraporn, Weerachai, Gary Egbert, and Mokoto Uyeshima. "Data Space Occam’s Inversion: Three-Dimensional Magnetotelluric Case." ASEG Extended Abstracts 2003, no. 1 (April 2003): 1–3. http://dx.doi.org/10.1071/aseg2003_3demab017.

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42

Van Anh Le, Cuong, Brett Harris, Eric Takam Takougang, and Andrew Pethick. "Application of seismic attributes for constraining Magnetotelluric Inversion." ASEG Extended Abstracts 2015, no. 1 (December 2015): 1–4. http://dx.doi.org/10.1071/aseg2015ab308.

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43

Sasaki, Yutaka. "Three-dimensional inversion of static-shifted magnetotelluric data." Earth, Planets and Space 56, no. 2 (February 2004): 239–48. http://dx.doi.org/10.1186/bf03353406.

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44

Usui, Yoshiya, Takafumi Kasaya, Yasuo Ogawa, and Hisanori Iwamoto. "Marine magnetotelluric inversion with an unstructured tetrahedral mesh." Geophysical Journal International 214, no. 2 (May 5, 2018): 952–74. http://dx.doi.org/10.1093/gji/ggy171.

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45

Patro, P. K., M. Uyeshima, and W. Siripunvaraporn. "Three-dimensional inversion of magnetotelluric phase tensor data." Geophysical Journal International 192, no. 1 (November 9, 2012): 58–66. http://dx.doi.org/10.1093/gji/ggs014.

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46

Guo, Rongwen, Stan E. Dosso, Jianxin Liu, Jan Dettmer, and Xiaozhong Tong. "Non-linearity in Bayesian 1-D magnetotelluric inversion." Geophysical Journal International 185, no. 2 (March 29, 2011): 663–75. http://dx.doi.org/10.1111/j.1365-246x.2011.04996.x.

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47

Meju, M. A., and V. R. S. Hutton. "Iterative most-squares inversion: application to magnetotelluric data." Geophysical Journal International 108, no. 3 (March 1992): 758–66. http://dx.doi.org/10.1111/j.1365-246x.1992.tb03467.x.

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48

Seillé, Hoël, and Gerhard Visser. "Bayesian inversion of magnetotelluric data considering dimensionality discrepancies." Geophysical Journal International 223, no. 3 (August 21, 2020): 1565–83. http://dx.doi.org/10.1093/gji/ggaa391.

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SUMMARY Bayesian inversion of magnetotelluric (MT) data is a powerful but computationally expensive approach to estimate the subsurface electrical conductivity distribution and associated uncertainty. Approximating the Earth subsurface with 1-D physics considerably speeds-up calculation of the forward problem, making the Bayesian approach tractable, but can lead to biased results when the assumption is violated. We propose a methodology to quantitatively compensate for the bias caused by the 1-D Earth assumption within a 1-D trans-dimensional Markov chain Monte Carlo sampler. Our approach determines site-specific likelihood functions which are calculated using a dimensionality discrepancy error model derived by a machine learning algorithm trained on a set of synthetic 3-D conductivity training images. This is achieved by exploiting known geometrical dimensional properties of the MT phase tensor. A complex synthetic model which mimics a sedimentary basin environment is used to illustrate the ability of our workflow to reliably estimate uncertainty in the inversion results, even in presence of strong 2-D and 3-D effects. Using this dimensionality discrepancy error model we demonstrate that on this synthetic data set the use of our workflow performs better in 80 per cent of the cases compared to the existing practice of using constant errors. Finally, our workflow is benchmarked against real data acquired in Queensland, Australia, and shows its ability to detect the depth to basement accurately.

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49

Whittall, Kenneth P. "Inversion of magnetotelluric data using localized conductivity constraints." GEOPHYSICS 51, no. 8 (August 1986): 1603–7. http://dx.doi.org/10.1190/1.1442211.

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I present an algorithm for the one‐dimensional magnetotelluric inverse problem of finding conductivity as a function of depth in the earth. The algorithm uses linear programming to solve an integral form of a nonlinear Riccati equation. This iterative scheme sacrifices the efficiency of direct inversion for the overwhelming advantages of incorporating localized conductivity constraints. I use localized conductivity constraints in two ways to combat the nonuniqueness of the nonlinear inverse problem. First, I impose physical constraints derived from external sources to restrict the nonuniqueness and construct conductivity models that are closer to reality. Second, I impose arbitrary constraints in an effort to assess the extent of nonuniqueness and explore the range of acceptable profiles. The first technique enhances the reliability of an interpretation, and the second measures the plausibility of particular conductivity features.

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50

Pek, Josef, and Fernando A. M. Santos. "Magnetotelluric inversion for anisotropic conductivities in layered media." Physics of the Earth and Planetary Interiors 158, no. 2-4 (October 2006): 139–58. http://dx.doi.org/10.1016/j.pepi.2006.03.023.

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