Inversion Of Magnetotelluric Data Research Articles

Deep basin conductor characterization using machine learning-assisted magnetotelluric Bayesian inversion in the SW Barents Sea

Summary In this article, we use a new workflow to substantiate the characterization of a prominent, deep sediment conductor in the hyper-extended Bjørnøya Basin (SW Barents Sea) previously identified in smooth resistivity models from 3D deterministic inversion of magnetotelluric data. In low dimensionality environments like layered sedimentary basin, 1D Bayesian inversion can be advantageous for a thorough exploration of the solution space but the violation of the 1D assumption has to be efficiently handled. The primary geological objectives of this work is therefore preceded by a secondary task: the application of a new machine learning approach for handling the 1D violation assumption for 21 MT field stations in the Barents Sea. We find that a decision tree can adequately learn the relationship between MT dimensionality parameters and the 1D-3D residual response for a training set of synthetic models, mimicking typical resistivity structures of the SW Barents Sea. The machine learning model is then used to predict the dimensionality compensation error for MT signal periods ranging of 1 to 3000 s for 21 receivers located over the Bjørnøya Basin and Veslemøy High. After running 1D Bayesian inversion, we generated a posterior resistivity distribution for an ensemble of 6000 1D models fitting the compensated MT data for each 21 field stations. The proportion of 1D models showing ρ < 1 Ω.m is consistently beyond 80% and systemically reaches a maximum of 100% in the Early Aptian - Albian interval in the Bjørnøya Basin. In hyper-extended basins of the SW Barents Sea, the dimensionality compensation workflow has permitted to refine the characterization of the deep basin conductor by leveraging the increased vertical resolution and optimal used of MT data. In comparison, the smooth 3D deterministic models only poorly constrained depth and lateral extent of the basin anomaly. The highest probability of finding ρ < 1 Ω.m is robustly assigned to the syn-tectonic Early Aptian - Albian marine shales, now buried at 6 to 8 km depth. Based on a theoretical two phase fluid-rock model, we show that the pore fluid of these marine shales must have a higher salinity than seawater to explain the anomaly ρ < 1 Ω.m. Therefore, the primary pore fluid underwent mixing with a secondary brine during rifting. Using analogue rift systems in paleomargins, we argue that two possible secondary brine reservoir may contribute to deep saline fluid circulation in the hyper-extended basin: (1) Permian salt-derived fluid and, (2) mantle-reacted fluid from serpentinization.

Lithospheric electrical structure of the Alxa Block, NW China, southern central Asian Orogenic Belt, revealed by 2D inversion of magnetotelluric data

The Alxa Block, situated between the Tarim and North China cratons, is essential to understand the tectonics of northwest China. It has undergone several tectonic cycles, including rifting from northern Gondwana in Neoproterozoic, subduction-accretion of the Paleo-Asian Ocean during Paleozoic, and intracontinental deformation in Mesozoic. Broadband magnetotelluric data covering a frequency range of 320 Hz to ∼ 10000 s was collected along two NW-SE trending lines across major tectonic units of the Alxa region to investigate the deep structures of the Alxa Block. By applying the nonlinear conjugate gradient (NLCG) inversion approach, two-dimensional (2D) electrical resistivity models were obtained at a 100 km depth. These findings indicate that the Zhusileng-Hangwula and Zongnaishan-Shalazhashan tectonic zones have high resistivity in their lower crust and upper mantle, with an overall layered high-low–high resistivity pattern. The upper crust of the Nuru-Langshan tectonic zone is primarily characterized by high resistivity values, whereas the lower crust and upper mantle contain a large-scale low-resistivity (or conductive) zone that is likely the result of partial melting. The large-scale Early–Middle Jurassic nappe structures may be connected to sub-horizontal low-resistivity anomalies in the shallow strata, which are located up to 20 km below the Yagan and Zhusileng–Hangwula tectonic zones. Below the Engger Us fault, two low-resistivity bands are interpreted as the product of bidirectional subduction and final closure of the Paleo-Asian Ocean. The Alxa Block may have subducted southwards beneath the Ordos Block, as suggested by a clear low-resistivity anomaly between the two blocks. Furthermore, partial melting may have happened for high-conductivity bodies in the upper mantle based on the modified Archie formula’s evaluation of their melting degree.

Constraints on Growth and Stabilization of the Western Superior Craton From Inversion of Magnetotelluric Data

AbstractA data set consisting of 376 broadband and long‐period MT measurements was used to generate the first ever 3D resistivity model of the Archean western Superior Craton. The modeled resistivity structure is compared to coincident seismic reflection data. The observed geophysical signatures are interpreted within the context of the late stages of crustal growth and cratonization of the region via the progressive accretion of terranes against the initial cratonic core. The northern‐most terranes comprising the cratonic core exhibit a nearly homogenous highly resistive crust. The lower crust of the southern terranes contains largely continuous low resistivity bands which run subparallel to major terrane boundaries and corresponding fault systems. In some cases, low resistivity features are coincident with dense packages of sub‐horizontal to listric reflections within the mid‐ to lower crust. These resistivity structures are inferred to represent preserved geoelectric signatures of late to post‐orogenic magmatic pulses likely related to delamination of locally overthickened crust. Increased mantle heat flow resulted in partial melting of the lower crust and upper mantle and upward migration of CO2‐rich melts and fluids through crustal weak zones corresponding to shear and/or suture zones formed during terrane amalgamation. Thermal softening of the mid‐ to lower crust led to orogenic collapse and reactivation of the crustal shear zones, resulting in formation and interconnection of graphitic films which were preserved within the stable craton. These results have implications for the tectono‐magmatic history of the western Superior Craton, as well toward the understanding of the geodynamic regime of the Archean Earth.

2-D probabilistic inversion of MT data and uncertainty quantification using the Hamiltonian Monte Carlo method

SUMMARY Bayesian methods provide a valuable framework for rigorously quantifying the model uncertainty arising from the inherent non-uniqueness in the magnetotelluric (MT) inversion. However, widely used Markov chain Monte Carlo (MCMC) sampling approaches usually require a significant number of model samples for accurate uncertainty estimates, making their applications computationally challenging for 2-D or 3-D MT problems. In this study, we explore the applicability of the Hamiltonian Monte Carlo (HMC) method for 2-D probabilistic MT inversion. The HMC provides a mechanism for efficient exploration in high-dimensional model space by making use of gradient information of the posterior probability distribution, resulting in a substantial reduction in the number of samples needed for reliable uncertainty quantification compared to the conventional MCMC methods. Numerical examples with synthetic data demonstrate that the HMC method achieves rapid convergence to the posterior probability distribution of model parameters with a limited number of model samples, indicating the computational advantages of the HMC in high-dimensional model space. Finally, we applied the developed approach to the COPROD2 field data set. The statistical models derived from the HMC approach agree well with previous results obtained by 2-D deterministic inversions. Most importantly, the probabilistic inversion provides valuable quantitative model uncertainty information associated with the resistivity structures derived from the observed data, which facilitates model interpretation.

On the stability of the lithospheric geoelectrical structure resolution in Tarim – Tien Shan junction zone: application of various approaches to the magnetotelluric data inversion

For several decades, the intracontinental Tien Shan orogeny has served as a testing polygon for the study of modern geodynamic and seismic processes, carried out by Russian and foreign scientists at the Research Station RAS (Bishkek). The investigations have big practical significance for developing seismic hazzard zoning strategies. Magnetotelluric sounding, providing unique information on the lithosphere structures and rheology, plays an important role in the complex of applied geological and geophysical methods.
 The article describes the experience of studying the deep geoelectric structure of one of the most seismically active regions of the Tien Shan, the junction zone of the orogen and the Tarim plate. This experience was obtained during the interpretation of materials from the regional profile of MT soundings that cross this zone. Comparing the results of applying three different approaches to the inversion of magnetotelluric data, which differ not only in software implementations and algorithmic foundations, but also in model dimension, allows us to assess the reliability of the allocation and revealed properties of the main structural elements in the deep conductivity section, which help to clarify the seismotectonic pattern of the studied crustal zone.
 The objectives of the article also included support for new SinoProbe MT experiments.

Deep Structure of the Santos Basin, Offshore Brazil From 3D Inversion of magnetotelluric Data

AbstractThe relationship between deep crustal structure and the deformation in the overlying sedimentary wedge in Santos basin, Brazil is not well understood, and the origin and evolution of the salt‐related “Albian Gap (AG)” remain a topic of debate. We investigate the deep structure using three‐dimensional inversion of full tensor marine magnetotelluric (MT) data (of 10−1 to 104 s period bandwidth) from 92 stations along three NW–SE lines in 50–1,700 m water depth, one crossing the Cretaceous hinge line (CHL), AG, and Cabo Frio Fault (CFF). The geological validity of the resulting MT resistivity models was determined using resistivity logs from 11 wells and seismic data. The model shows two regionally persistent electrically conductive layers (C2 and C3) related to key Cenozoic and Cretaceous unconformities in the upper part of the sedimentary wedge. Beneath this wedge, the resistive continental crust is ∼35 km thick across the CHL until the 200 m isobath and thereafter thins rapidly seaward to ∼21 km over a lateral distance of ∼80 km defining a domain of highly extended and faulted crust. Our models show a mantle‐associated basement high and evidence of significant uplift of the lower part of the sedimentary wedge at 100–150 km distance along our central profile which spatially coincides with the AG and a previously proposed Moho high. This implies a mantle‐driven deformation of the crust and basin fill. We propose that mantle flow and magmatism may have played a significant role in the inferred displacement at the AG.

Geoelectric characterisation of the junction of seismically active Delhi Hardwar Ridge and Delhi Sargodha Ridge

A magnetotelluric (MT) geophysical survey for the first time has been conducted for the geoelectric characterization of the junction of the contact zone of NNE-SSW striking Delhi Hardwar Ridge (DHR) and NW–SE trending Delhi Sargodha Ridge (DSR) in the Rohtak area, Haryana which has experienced 15 earthquakes of M2.0–M4.4 from April to August 2020. A total of 08 MT sites are acquired along a NW–SE profile of length 50 km. From the 2D MT data inversion, the DHR and DSR are for the first time characterized by equal values of moderate resistivity of 100 Ohm m at two depths. The resistivity variation for DHR corresponds to 100 Ohm m from the surface to the depth of 20 km, whilst DSR is found associated with the same value of resistivity extending in the NW direction. The DHR has been found striking NE-SW with a very shallow central axis (less than 400 m) having a width of 12–15 km forming half grabens on both limbs supported by shallow faults. The DSR has been found bifurcated from DHR at a depth of 12–13 km and extended in the NW direction. The DSR has been generated due to flexure bulging caused by collision and anticlockwise rotation of the Indian plate in the Eocene period. A NE striking steep dipping reverse fault (F1) has also been identified about 15 km west of the DHR. It is inferred that the DSR got upthrusted along this fault and became shallower in the NW region. The seismicity in the Rohtak and surroundings is located at the bifurcation points of DHR and DSR and the contact zone of DSR and reverse fault F1. The reverse fault F1 is also active and has generated microseismicity in the past.

Feature-based magnetotelluric inversion by variational autoencoder using a subdomain encoding scheme

Magnetotelluric (MT) data inversion aims to reconstruct a subsurface resistivity model that minimizes the discrepancy between inverted and measured electromagnetic data. Conventional pixel-based minimum-structure inversion often yields a smoothed-out reconstruction with a relatively low resolution. A priori geophysical knowledge can be embedded into inversion and improve the reconstruction resolution through proper reparameterization. However, existing reparameterization approaches, such as model-based and parametric transform-based inversion, have limited ability to incorporate various a priori information. The effectiveness of existing deep generative model-based inversion algorithms is still debatable when applied to scenarios with complex backgrounds. We develop a feature-based MT data inversion method based on a variational autoencoder (VAE) with a subdomain encoding scheme. Instead of encoding the entire domain of an investigation, we adopt a 1D subdomain encoding scheme to encode the 1D resistivity-depth models using a single VAE. The latent variables for the 2D model are a combination of the latent variables for 1D models, and the encoded region of interest (ROI) can be flexibly determined. The latent variables of ROI and the pixels outside the ROI are simultaneously inverted using the gradient-descent method. Our 1D subdomain encoding scheme reduces the complexity and diversity of the data set, and it can flexibly embed a priori knowledge with various uncertainties. Synthetic data inversion and inversion of the Southern African Magnetotelluric Experiment field data validate our method’s ability to effectively improve inversion accuracy and resolution.

A Novel Fruit Fly Optimization Algorithm with Evolution Strategy for Magnetotelluric Data Inversion

As a novel metaheuristic algorithm, fruit fly optimization algorithm (FOA) can effectively deal with the inversion problem of one-dimensional magnetotelluric data. However, FOA still has the disadvantage of premature convergence and falling into local extreme value. Therefore, based on standard FOA, we improve the FOA algorithm by introducing evolutionary strategies. Firstly, crossover and mutation strategies are introduced to improve the updating process of FOA population individuals. Secondly, by improving the variation scale factor, the global search and local search capabilities of the algorithm are balanced, and these improvements can accelerate the algorithm convergence. The improved algorithm is compared with other algorithms. After the benchmark function test, the improved algorithm has better optimization ability. Finally, the MT theoretical model and field data are used to test that the evolutionary strategy can effectively improve the convergence speed of the algorithm, and the inversion accuracy of the new algorithm is greatly improved.

2D magnetotelluric inversion based on ResNet

In this study, a deep learning algorithm was applied to two-dimensional magnetotelluric (MT) data inversion. Compared with the traditional linear iterative inversion methods, the MT inversion method based on convolutional neural networks (CNN) does not rely on the selection of the initial model parameters and does not fall into the local optima. Although the CNN inversion models can provide a clear electrical interface division, their inversion results may remain prone to abrupt electrical interfaces as opposed to the actual underground electrical situation. To solve this issue, a neural network with a residual network architecture (ResNet-50) was constructed in this study. With the apparent resistivity and phase pseudo-section data as the inputs and with the resistivity parameters of the geoelectric model as the training labels, the modified ResNet-50 model was trained end-to-end for producing samples according to the corresponding production strategy of the study area. Through experiments, the training of the ResNet-50 with the dice loss function effectively solved the issue of over-segmentation of the electrical interface by the cross-entropy function, avoided its abrupt inversion, and overcame the computational inefficiency of the traditional iterative methods. The proposed algorithm was validated against MT data measured from a geothermal field prospect in Huanggang, Hubei Province, which showed that the deep learning method has opened up broad prospects in the field of MT data inversion.

Electromagnetic prediction of rock thermal properties beyond boreholes: Soultz-sous-Forets (France) case study

Estimation of deep temperature, thermo-hydro-dynamic and basin simulation, heat exchange and geothermal resource assessment require knowledge of the rock thermal properties beyond boreholes. At the absence of suitable methods and instruments for such estimates we propose a new approach to prediction of thermal conductivity, specific heat capacity and heat flow density based on results of electromagnetic sounding of the study area. Artificial neural network used to this end is taught by correspondence between laboratory measurements on cores from the drilled boreholes and adjacent electrical conductivity profiles determined by inversion of the electromagnetic sounding data collected in their vicinity. This approach is used to estimate rock thermal properties along NW-SE magnetotelluric profile crossing Soultz-sous-Forêts geothermal area, Alsace, France. We demonstrate that accuracy of such prediction both in depths below the boreholes and in the interwell space is comparable with that usually achievable in the borehole scale. In particular, it is shown that relative errors of thermal conductivity prediction at depths twice as large as the borehole depth are as small as 2%, and the accuracy of cross-well thermal conductivity prediction is 6% and 10% for dry and wet rocks, respectively. On the other hand, the prediction accuracy for matrix thermal conductivity of a rock from the known profile of dry thermal conductivity is about 8%. Based on the results of our studies, we constructed dry and wet thermal conductivity sections from the electrical conductivity model determined earlier by 2-D inversion of magnetotelluric data. The temperature model of the study area built along the same profile by means of electromagnetic geothermometer and the obtained thermal conductivity sections enabled estimating the profiles of the vertical component of heat flow density at the surface from dry and wet thermal conductivity sections. They could be considered as minimum and maximum constraints, respectively, bounding the area of its possible values, which, in turn, depend on in situ thermal conductivity of fluids filling the rock pores. The range of possible vertical heat flow density variations (100–150 ± 10% mW·m− 2) agrees well with heat flow estimates in the Upper Rhine graben and, in particular, in Soultz-sous-Forêts geothermal area roughly estimated earlier from borehole measurements. Using the artificial neural network trained on the correspondence between the specific heat capacity determined in the borehole and adjacent electrical conductivity profile, we built the model of specific heat capacity. Against the background, a zone of enhanced specific heat capacity ranging between 2.05 and 2.15 MJ·m− 3·K− 1 was observed in the domain located at the base of the sedimentary cover and upper part of the granitic basement.

Crustal structure of the Lazufre volcanic complex and the Southern Puna from 3-D inversion of magnetotelluric data: Implications for surface uplift and evidence for melt storage and hydrothermal fluids

Abstract The Central Andes are unique in the global system of subduction zones in that a significant, high-altitude plateau has formed above a subduction zone. In this region, both subduction and the associated magmatism have been shown to vary in both space and time. Geophysical data have been invaluable in determining the subsurface structure of this region. Extensive seismic studies have determined the regional-scale distribution of partial melt in the crust and upper mantle. Magnetotelluric studies have been effective in providing independent constraints on the quantity and composition of partial melt in the crust and upper mantle. Geodetic studies have shown that a small number of volcanic centers exhibit persistent, long-term uplift that may indicate the formation of plutons or future eruptions. This paper describes a detailed study of the Southern Puna using magnetotelluric (MT) data. This region is located at the southern limit of the Central Andes in a region where a recent transition from flat-slab subduction to normal subduction has caused an increase in magmatism, in addition to hypothesized lithospheric delamination. It is also a region where an extensive zone adjacent to the volcanic arc is undergoing surface uplift, located near Volcán Lastarria and Cordon del Azufre (collectively called Lazufre). The main goals of the work are to define the crustal structure and to investigate processes that may cause surface uplift of relatively large regions not associated with active volcanism. As part of the PLUTONS project, MT data were collected on an east-west transect (approximately along 25°S) that extended across the Southern Puna, from Lazufre to north of Cerro Galan. The data were combined with previously collected MT data around Lazufre and inverted to give a 3-D resistivity model of the crust. The low resistivity of the crust resulted in limited sensitivity to mantle structure. A number of major crustal conductors were detected and included (1) a mid-crustal conductor extending eastward from the volcanic arc as far as the Salar de Antofalla; (2) an upper- to mid-crustal conductor located north of Cerro Galan; and (3) a conductor that rises westward from (1) and terminates directly beneath the region of surface uplift at Lazufre. These conductors are broadly coincident with the location of crustal low shear-wave anomalies. The conductive features were interpreted to be due to zones of partial melt stored in the crust, and petrological data were used to estimate melt fractions. Below Lazufre, it is likely that aqueous fluids contribute to the high conductivity, which is observed within the depth range of the inflation source, giving evidence that the surface uplift may be associated with both magmatic and hydrothermal processes.

Seismogenic Structure of the 2014 M6.5 Ludian Earthquake From Three‐Dimensional Joint Inversion of Magnetotelluric Data and Seismic Arrival Times

AbstractOn 3 August 2014, a destructive earthquake with a magnitude of 6.5 occurred in the Ludian region in Yunan, China, causing heavy casualties. However, the seismogenic structure of the Ludian earthquake is still unclear. To address this issue, we have developed a three‐dimensional joint inversion algorithm for magnetotelluric data and seismic body wave arrival times based on the cross gradient structural similarity constraint, which utilizes the complementary advantages of the two different datasets. We use arrival times of P‐ and S‐waves from 6,369 local earthquakes recorded by 30 seismic stations and magnetotelluric impedances from 127 MT stations to perform the joint inversion to construct a combined 3‐D P‐wave velocity (Vp), S‐wave velocity (Vs), and resistivity models and the locations of relocated aftershocks in the source region of the Ludian earthquake. Our models show significant low Vp/Vs ratios and low resistivity in the upper crust of the main shock zone, and the relocated aftershocks form an L‐shaped zone. Based on petrophysical studies and analysis of the cross‐plots of different physical properties, we infer the existence of cracks/fractures and fluids as well as high quartz contents in the source region. These fluids may play a key role in promoting the Ludian earthquake by reducing the effective normal stress of the fault zone, and the existence of high quartz contents further makes the crustal rocks in the source region more brittle. Our study provides a new evidence how the properties of the crust in the focal area have some control on the genesis of moderate to large earthquakes.

Investigation of deep-seated heat source through 3-D Magnetotelluric inversion in Arjuno-Welirang volcanic complex (East Java)

Arjuno-Welirang is a volcanic complex with quaternary ages in East Java Province, Indonesia. Various geothermal systems in the area might exist, from inactive to still-active hydrothermal systems and even volcanic (magmatic) hydrothermal systems. To differentiate the status of geothermal systems to be useful for further exploration programs, it is necessary to investigate the deep-seated heat source responsible for supporting heat to the existing geothermal systems. To achieve the objective, Magnetotelluric (MT) is used to image the subsurface resistivity variation down to about 20 km. A total of 117 MT stations have been included to fill the coverage area, especially in the eastern and southern regions of the research area. A series of data processing has been conducted to improve the quality of MT data, and 3-D inverse modeling has been applied after processing. In addition, the exploration is also focused on identifying the extension of the high-temperature reservoir with benign fluid. Based on the 3-D inversion of MT data, the updome of resistivity structure at the deeper level below Mt. Welirang is identified as a deep-seated heat source indication supporting the main upflow reservoir zone. A possible reservoir extension to the western side of Mt. Welirang–Mt. Kembar complex is more prominent as indicated by the resistivity pattern, supported by the Cangar and Coban hot springs occurrence as an indication of an outflow zone. This extension zone could be one of the focused areas for further exploration drilling to avoid magmatic fluid that probably exists at the main upflow zone.

The Western Segment of the Precambrian Suture Between the Yangtze and Cathaysia Blocks: Constraints From Magnetotelluric Data in Southwest China

AbstractThe western segment of the suture zone between the Yangtze and Cathaysia blocks, which is the most important tectonic boundary related to the formation and rifting of south China, is enigmatic and not fully understood due to the sporadic exposure of Precambrian strata and ophiolites. Three‐dimensional electrical resistivity models derived from inversion of magnetotelluric data identified a lithospheric‐scale conductive zone extending northeastwards beneath the Youjiang basin, which was interpreted as the western segment of the suture zone. The high conductivity and coincident high magnetic anomalies closely match the location of Carlin‐type gold deposits, which can be explained by fluids and gold‐bearing sulfide minerals in a fossil suture zone. Inconsistent with the southeast‐dip resolved at the eastern segment of the suture zone (the Jiangshan‐Shaoxing fault) in the earlier study, the slightly north‐dipping geometry at the western suture zone implies the reactivation by northward subduction and closure of the Paleo‐Tethys Ocean.

Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application

Abstract. We propose, test and apply a methodology integrating 1D magnetotelluric (MT) and magnetic data inversion, with a focus on the characterisation of the cover–basement interface. It consists of a cooperative inversion workflow relying on standalone inversion codes. Probabilistic information about the presence of rock units is derived from MT and passed on to magnetic inversion through constraints combining structural constraints with petrophysical prior information. First, we perform the 1D probabilistic inversion of MT data for all sites and recover the respective probabilities of observing the cover–basement interface, which we interpolate to the rest of the study area. We then calculate the probabilities of observing the different rock units and partition the model into domains defined by combinations of rock units with non-zero probabilities. Third, we combine these domains with petrophysical information to apply spatially varying, disjoint interval bound constraints (DIBC) to least-squares magnetic data inversion using the alternating direction method of multipliers (or ADMM). We demonstrate the proof-of-concept using a realistic synthetic model reproducing features from the Mansfield area (Victoria, Australia) using a series of uncertainty indicators. We then apply the workflow to field data from the prospective mining region of Cloncurry (Queensland, Australia). Results indicate that our integration methodology efficiently leverages the complementarity between separate MT and magnetic data modelling approaches and can improve our capability to image the cover–basement interface. In the field application case, our findings also suggest that the proposed workflow may be useful to refine existing geological interpretations and to infer lateral variations within the basement.

Deep heat source detection using the magnetotelluric method and geothermal assessment of the Farafra Oasis, Western Desert, Egypt

Nonrenewable hydrocarbon energy sources provide the bulk of Egypt's energy. However, the nation needs to diversify its energy portfolio and use conventional and renewable energy in tandem to ensure its long-term economic growth; therefore, harvesting untapped geothermal resources might help Egypt meet its energy needs in a sustainable and effective manner. The Farafra Oasis is located in the midst of Egypt's Western Desert and has been selected as the focal point for sustainable long-term development in the Western Desert.Using a magnetotelluric (MT) survey, a deep exploration of the Farafra region has been conducted to explore the underlying geological state and, as a result, explain the origin of several thermal wells around the oasis. None of the prior studies reached the necessary depths to offer information on the surface of deep igneous rocks, which may be the source of heat in the study area. Two field excursions were conducted to measure 19 stations along a line crossing the Farafra Oasis and extending more than 130 km. The MT instrument measured periods greater than 1000 s to gather low-frequency data with great efficiency.On the basis of 2D inversion of MT data, a mathematical model of the Farafra region's geothermal system was built to visualize and characterize the heat sources underneath the Farafra Oasis. It was shown that the existence of a high-temperature pluton at considerable depth is the primary cause of the temperature rise in the aquifer under the Farafra Oasis, as the water mass builds up on top of plutonic rocks and flows upwards via fractures and faults.

Flexible and Accurate Prior Model Construction Based on Deep Learning for 2-D Magnetotelluric Data Inversion

The conventional magnetotelluric (MT) data inversion methods, such as the nonlinear conjugate gradient method, quasi-Newton method, and Gauss–Newton method and so on, can converge robustly, but their results are easily affected by the initial model and regularization term. Although supervised learning can break through the resolution limitation by directly learning the nonlinear relationship between the model and the data, it cannot guarantee the data fitting without considering physical constraints. Here, we propose a novel prior model generation method using deep learning for conventional inversion to jointly take advantages of the two techniques. We first combine Gaussian random rough surface scheme and random polygon generation algorithm to construct practical 2-D geoelectric models, in which the prior information on the geoelectric structure can be flexibly integrated. Then, a fast 2-D MT forward modeling method is applied to calculate the forward responses and establish the training set. Finally, we use the training set to complete the parameter optimization of U-shaped network (U-NET) and run the conventional inversion with prior model generated by the trained U-NET. Numerical experiments with synthetic data show that the proposed method can effectively integrate the advantages of conventional inversion and supervised learning, and remarkably improve the resolution in the inversion if proper training sets are used. The inversions of the USArray data also prove that our method can retain high-resolution structures predicted by the U-NET in the final inversion results with a data fitting as good as the traditional Gauss–Newton method.

An Intelligent MT Data Inversion Method With Seismic Attribute Enhancement

Magnetotelluric data inversion reconstructs a electrical resistivity structure most compatible with the observed magnetotelluric data, and static correction can remove the undesired static shift effect in magnetotelluric data. Conventional magnetotelluric data static shift correction often face the challenge of demanding requirements, such as large data amount, additional types of data, or a deep understanding of the research area. Magnetotelluric inversion constrained by seismic data often has better resolution and model consistency compared with independent magnetotelluric inversion. However, valuable inversion knowledge contained in geophysicists’ expertise is not effectively incorporated. In this work, we present an intelligent magnetotelluric data inversion method leveraging data-driven and physics-driven techniques based on deep learning. A novel magnetotelluric data static shift correction method is introduced based on a neural network. A magnetotelluric data inversion method is formulated with the constraint of the extracted seismic reflection image based on two different neural networks. Experiments on synthetic and field data verify the effectiveness of the proposed method.

A fully finite-element based model-space algorithm for three-dimensional inversion of magnetotelluric data

SUMMARY We present a fully finite-element based inversion methodology for imaging 3-D magnetotelluric impedance data on unstructured meshes. The inverse problem is formulated using a minimum-structure Gauss–Newton type optimization scheme that minimizes an objective function with respect to the model perturbation. By introducing a rigorous regularization scheme, we derived a Ritz-type variational formulation of the model objective function and designed a face-based finite-element basis function to discretize the model gradient across tetrahedron’s inter-element boundaries. The forward modelling engine of our optimization scheme is based on a finite-element solution of the E-field Helmholtz equation that is enforced for the magnetotelluric simulation problem using the appropriate edge-based basis functions and 3D boundary conditions. The optimization algorithm developed here utilizes a message passing interface scheme and uses a direct solver to factorize and store both the regularization matrix and the forward modelling coefficient matrix on the processes working in parallel. Having to do this only once within each Gauss–Newton optimization cycle facilitates both the calculation of the dot product of the model regularization terms with the evolving model perturbation, and computing implicitly the sensitivity-vector products. We validated the methodology and the correctness of the developed algorithm for two test examples (COMMEMI 3Ds) from the literature. Also, by comparing the performance between classes of iterative solvers we demonstrated the superior performance of generalized minimum residual solver in reducing the residual norm of the iterative solver during model updates. Using the algorithm in a geologically realistic scenario, we imaged the anticipated geometry of the Lalor volcanogenic massive sulphide deposit in Canada. The feasibility of the imaging methodology is further evaluated with the survey data, for which, again the algorithm converged to the anticipated model solution reproducing the lithostratigraphic sequence of the ore deposit.