Two-dimensional inversion of magnetotelluric/radiomagnetotelluric data by using unstructured mesh

A radio magnetotelluric (MT) field data set, acquired in scalar mode, over a buried waste site has been successfully analyzed using a 3D MT inversion scheme using nonlinear conjugate gradients. The results of this analysis demonstrate the utility of the scheme where more than 4800 data points collected on multiple measurement profiles have been inverted simultaneously. The resulting image clearly detects the buried waste; when receiver profiles cross pit boundaries, the image maps the lateral extent of the pit. However, the base of the pit is poorly resolved, and depends upon the starting model used to launch the inversion. Hence, critical information on whether contamination is leaching into a resistive gravel bed lining the base of the pit, as well as the deeper geological horizons consisting of brown coal, clay, and tertiary sands, is inconclusive. Nevertheless, by incorporating within the inversion process a priori information of the background media that is host to the waste, sharper images of the base of the pit are obtained, which are in good agreement with borehole data. The 3D analysis applied in this paper overcomes previous limitations in the radio magnetotelluric (RMT) method using 2D data analysis and inversion. With 3D analysis, it is unnecessary to make assumptions regarding geological strike, and near‐surface statics can be accommodated in both source polarizations. Our findings also indicate that 2D MT interpretation can overestimate the pit's depth extent. This may lead to the erroneous conclusion that the geological horizons beneath the pit have been contaminated.

Quick clay, which is the main cause of landslides that occur in the northern countries, liquefies easily, and its presence implies an increased risk of landslide. Geophysical methods have been increasingly used in landslide investigations. Three-dimensional electric resistivity tomography, radio magnetotelluric (RMT), controlled-source RMT (CSRMT), and high-resolution reflection seismic data were acquired at a quick-clay landslide site in the southwest of Sweden. The main objectives were to evaluate the capability of each method in delineating different subsurface geologic structures that controlled a peculiar and hazardous retrogressive-type landslide in the study area. A 3D resistivity model from the inversion of CSRMT data showed the best correlation with the reflection seismic data and borehole information, thanks to the broad frequency range of the data set. It better imaged the resistive crystalline bedrock underlying the marine conductive clays and showed considerable correlations with the 3D reflection seismic data in resolving a coarse-grained layer that was interpreted to act as a conduit directing freshwater into the clays under a confined pressure, leaching their salt and forming quick clays. The 3D CSRMT resistivity model and 3D reflection seismic data showed that the coarse-grained layer has a varying thickness. At some locations, it was too thin to be resolved by the methods used here. Combination of the CSRMT model, reflection seismic data, and the borehole data suggested that a layer with thickness of approximately 5 m and resistivity between [Formula: see text] was potentially quick clay, which probably extended laterally in the entire study area. These observations suggested that future developments should focus on joint inversion of such 3D data sets incorporating sharp boundaries as constraints in the inversion and particularly when quick clays were studied.

Out-of-quadrant impedance phases (POQ) have been observed in several magnetotelluric (MT) data sets around the world, hampering the modelling and interpretation of the data. These anomalous responses are usually observed in small groups of sites and have been variously interpreted as due to electrical anisotropy, galvanic distortion, 2-D structures with large resistivity contrasts, or 3-D conductive bodies, such as L-shaped conductors. We present here what is possibly a unique land-based MT data set characterized by an exceptionally large number of sites with POQ. The MT data were collected in the southeastern margin of the Capricorn Orogen (Western Australia). This is an area with a complex geological history, which is evident in the MT responses. Given the characteristics of the study area, which includes very high resistivity Archean terrains surrounded by low resistivity Palaeoproterozoic basins, strong resistivity contrasts involving complex 3-D structures are a possible cause of this unusual behaviour. This is confirmed by 3-D forward modelling using models based on the general geology of the study area, as well as by the 3-D inversion results. A comparison of the real and synthetic data, derived from 3-D forward modelling, using the same parameters suggests that the proposed scenario is a realistic explanation for most of the anomalous phases observed in this data set. Synthetic data are not affected by galvanic distortion or electrical anisotropy, so the good match observed between the real and synthetic MT responses is likely due to 3-D inductive effects. Previous explanations for POQ responses, such as blocks with electrical anisotropy and 3-D L-shaped conductors, are not required and are considered to be less likely causes of the POQ responses on geological grounds.

A prominent TE and TM mode split is observed in magnetotelluric (MT) data below 0.5 Hz collected in the Perth Basin over the Harvey Ridge, Western Australia. We investigate the causes of mode splitting and consider implications on inversion of the MT data to subsurface electrical conductivity distribution. Twenty-five broad-band MT stations were acquired and remote reference processing was completed to arrive at a data set located midway between the Darling Fault and the Indian Ocean. We used forward modelling to test our strong suspicion that the Indian Ocean, Darling fault and architecture of the Granitic Basement were indeed the major contributors to mode splitting that we observed. Forward modelling of synthetic data was completed for comparison with the Harvey MT data. We were surprised at the match between synthetic and field data given the simplicity of the forward model and the considerable lateral distance between the MT soundings and the Indian Ocean or Darling fault. We were then able to make significant improvements to the MT inversion outcome by introducing a large scale geo-electrical architecture as the seed model for inversion. Our work demonstrates that large scale geo-electrical contrasts at considerable lateral distance from an MT transect, or the target zone need to be systematically introduced to the inversion if a quality outcome is to be achieved.

The Radio-Magnetotelluric (RMT) method is a geophysical near-surface imaging technique with a broad range of possible applications. In 2020, the GFZ Potsdam has acquired a newly developed horizontal magnetic dipole transmitter that allows the application of the RMT method even in regions with an insufficient coverage of radio transmitters which normally serve as source signal. First controlled-source RMT measurements were conducted at three different locations in Chile in 2020. Further measurements were recently conducted in Ireland.  As we are able to store the raw time series, we have full control over the subsequent data processing. The processing tools at GFZ include the modular processing suite EMERALD, which was originally designed for MT processing, but has recently been adapted to process RMT data. One main difference is that in RMT the transmitter data is considered as signal, while in natural source MT this would be regarded as electromagnetic noise that needs to be removed using automated robust statistical approaches. However, processing the entire time series in an automated manner has a large drawback: The different emitted frequencies are transmitted in a sweep implying that only a smaller fraction of the time series contains the required signal for a particular target frequency and leading to an unfavourable signal-to-noise ratio. Since it is technically impossible to have the same time base for the data logger and the transmitter with an accuracy of a few nanoseconds, an automated detection scheme is required to find time segments that contain the transmitter signal. Usually, several Gigabytes of raw time series are collected during field measurements, making manual editing and supervision of the time series virtually impossible. However, a careful selection of appropriate time segments is essential for the success of the data processing. To address the challenge, machine learning algorithms have a high potential to solve both problems. Initial experience was gained with a recurrent neural network approach in order to identify suitable time segments (Patzer & Weckmann, EMTF 2021 – conference contribution and personal communication). However, many questions remained open, e.g. if other machine learning algorithms can result in better performances, which machine learning algorithms are in principle suitable for the characteristics and properties of RMT time series and which parameters should be used as input variables (features) for the algorithms. A large number of machine learning algorithms exist, which can be divided into different groups according to their operating principle and their activity fields. We will test unsupervised methods, especially for clustering the data, to identify a set of suitable input variables. Subsequently, we will use these features to train supervised algorithms as logistic regression, support vector machine and different kinds of neural networks to find the best performing algorithm. We will mainly use the RMT data from Chile within the training process. Furthermore, we will test if the trained algorithm is applicable to other new data sets measured at different locations (e.g. Ireland) and/or with different equipment.

Comparison of stable maximum likelihood estimator with traditional robust estimator in magnetotelluric impedance estimation

We collected long-period magnetotelluric (LMT) data on the 64 stations by using a remotely controlled measurement system in northwestern Anatolia, Türkiye. The data was collected along four nearly parallel 300-km-long lines. In our previous project, we already collected broadband magnetotelluric(MT) data on 358 stations along those four lines. These lines are crossing main tectonic traverses such as North Anatolia Fault Zones, and Intra-Pontid Suture zones.  The remotely controlled MT measurement system provides us to control data quality during measurement and we can change the station location if the data quality is not good in the current stations.  This ensures the good data quality of all MT sites. After the time series analysis and main data processing procedure such as phase tensor decomposition and static shift correction, we interpreted each line's data set by using a two-dimensional inversion algorithm. We also inverted LMT data by using a three-dimensional inversion algorithm. The three-dimensional resistivity model also showed us to main tectonic units as two-dimensional resistivity models. Additionally, crust lithosphere relations were also revealed. We obtained upper and lower crust boundaries by using magnetic data and crust al depth by using gravity data. Those results also validated our resistivity models obtained from MT data inversion. We are going to give preliminary interpretation results of the lithosphere structure of northwest Anatolia in this presentation.Acknowledgement:  This study is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) Project with Grant Number 119Y197. We thanks TUBITAK.

SUMMARY Magnetotelluric (MT) data inversion seeks to recover resistivity models of the subsurface. Solving the inversion problem is a non-trivial task, as multiple plausible solutions can be recovered due to the nonlinearity of the problem. To reduce this nonlinearity, we propose a data-driven approach where a 1-D cumulative resistance model is estimated from MT data via a direct data transformation. We define the cumulative representation of layered models as the weighted sum of layer thickness divided by resistivity from surface to any depth level, which is the cumulative conductance. Its inverse, cumulative resistance, is directly related to the real part of the impedance computed from MT data. We train a neural network to transform the MT impedance into a resistance model. The corresponding 1-D resistivity model is obtained without a priori information. We validate our approach using synthetic and real data, opening the discussion for future developments of this new perspective.

Here, a new naturally-inspired stochastic nonlinear joint and individual inversion technique for integrating direct current (DC) and magnetotelluric (MT) data interpretation-based simulation of a swarm intelligence combo with specific capabilities for exploitation of the variable weight particle swarm optimizer (vPSO) and exploration of the grey wolf optimizer (GWO), vPSOGWO, is used. They are particularly notable for their capacity for information exchange while hunting for food. Through synthetic MT and DC data contaminated with various sets of random noise, the applicability of the anticipated vPSOGWO algorithm based joint and individual inversion algorithm was assessed. The field examples, collected from diversified different geological terrains, including Digha (West Bengal), India; Sundar Pahari (Jharkhand), India; Puga Valley (Ladakh), India; New Brunswick, Canada; and South Central Australia, have shown the practical application of the proposed algorithm. Further, a Bayesian probability density function (bpdf) for estimating a mean global model and uncertainty assessment in posterior; and a histogram for model resolution assessment have also been created using 1000 inverted models. We examined the inverted outcomes and compared them with results from other cutting-edge methodologies, including the GWO, PSO, genetic algorithm (GA), Levenberg–Marquardt (LM), and ridge-regression (RR). Our findings showed that the current methodology is more effective than the GWO, PSO, GA, LM, and RR techniques at consistently improving the convergence of the global minimum. In contrast to earlier approaches, the current cutting-edge strategy vPSOGWO offers an improved resolution of an additional significant crustal thickness of about 65.68 ± 1.96 km over the Puga Valley, in which the inverted crustal thickness determined by vPSOGWO agrees well with the published crustal thickness over the Puga Valley. The new technology brings simulations closer to genuine models by significantly reducing uncertainty and enhancing model resolution.

Magnetotelluric (MT) inversion algorithms are an essential tool in exploration geophysics because they provide us with resistivity models of the subsurface. Inverse problems are ill-posed and non-unique. Resistivity models produced by geologically unconstrained MT inversions are typically smooth, which means that the geological interpretations obtained based on the resistivity models will be ambiguous and uncertain. To solve the above problems, geophysicists have made many efforts over the years to introduce any prior information (such as geological structure information and physical property data) into models, which could help to better constrain the inverted model with the inversion algorithms. In the last few years, there has been renewed interest in machine learning techniques. Various machine learning methods have been applied to inversions. Using the fuzzy c-means (FCM) clustering method, geophysicists proposed new inversion algorithms that are capable of building petrophysical information into the inversion. Although the FCM has been used and advanced in previous work, geophysicists concluded that a priori information of the correct number and value of cluster centers is very important for a successful FCM inversion. In fact, it is difficult to obtain the appropriate clustering information in some geophysical survey areas. In this study, we present an effective way to build the petrophysical information for the MT inversion based on FCM clustering algorithm. When the actual petrophysical information is insufficient, considering the characteristics of MT data, we perform a one-dimensional blocky inversion that can clearly identify the interface with distinct electrical property contrast; then, we obtain the number and value of cluster centers from 1D blocky inversion for the further MT inversion with FCM clustering. The algorithm uses guided FCM clustering to improve the model within the iterative minimization during MT inversion. In our MT inversion method, we integrate the geophysical inversion and geological differentiation into a unified scheme, which interact and enhance each other. The resistivity models obtained from the inversion respect the geophysical and petrophysical data and are easy to geologically interpret. We tested the algorithm using two synthetic examples and a field data example.

Direct current resistivity prospecting is a commonly geophysical method for environmental and engineering applications. In this paper, we propose a fuzzy C-means clustering model constrained inversion algorithm for two-dimensional DC resistivity. To fit arbitrary geological structure and surface of the earth, our inversion algorithm is developed based on unstructured model mesh. To be consistent with the geological structure, the fuzzy C-means clustering model constraint is added to the inversion cost function with the minimum structure model constraint, and the Gauss-Newton optimization method is used to seek solutions of the nonlinear inverse problem. Finally, we verify the performance of our algorithm by synthetic and field data sets. The results show that the resistivity and boundary can be better restored when the correct number and value of priori cluster centers were set. By testing the field data, the inversion algorithm can obtain obvious abnormal boundaries.

WSJointInv2D-MT-DCR: An efficient joint two-dimensional magnetotelluric and direct current resistivity inversion

Magnetotelluric data modeling was used for oil and gas exploration. The parameters are apparent resistivity and phase. The magnetotelluric data of this research were line JBS4 with 71 sounding sites and line JBS8 with 66 sounding sites. The magnetotelluric data was completed by remote reference data which used for reducing the noise so that good data could be gotten. The magnetotelluric data was proceed in stage as follow: time series selection, Fourier transform, robust processing, cross power selection, and 2 dimension inversion. The result of this research showed that magnetotelluric method can figured out the sub-surface model well. From the result of interpretation 2D inversion magnetotelluric and other geophysical data (gravity anomaly map), we can conclude that magnetotelluric modeling figured out 2 sub-basins. Those sub basins had thick sediment layer (4000 meter for Bantarkalong Basin and 3000 meter for Citanduy Basin) and the dominant resistivity value was 22-119 Ohm.meter.

Originally proposed for conventional reservoirs, the dual-continuum idealization is being applied directly to simulation in ultralow-permeability reservoirs. Recent gridding techniques can mesh the geometries of large discrete natural fracture networks (NFNs) into an unstructured grid to simulate fluid flow. Insufficient literature exists to help identify the tradeoffs in selecting one approach over another. This paper not only analyzes two approaches with respect to their underlying assumptions and applicability but also proposes a hybrid approach. A commercial reservoir simulator supporting both dual-continuum and unstructured simulation grid capability was used to compare these approaches in two separate stages using CPU times and accuracy of results as metrics. First, small-scale reservoirs with simple two-dimensional (2D) fracture patterns were simulated to examine the impact of matrix permeability, fracture spacing, and fracture orientation. Second, drilling-spacing-unit-size reservoirs with networks of stochastically generated three-dimensional (3D) fracture surfaces were simulated to compare the effect of density and clustering of fractures. These models were also simulated using a hybrid approach, modeling one portion of the fracture network as discrete and the remaining as dual continuum. Results show that although the dual-continuum technique can be very fast, it is not appropriate for all simulations. For example, extremely long transient periods in ultralow-permeability reservoirs raise doubts about the applicability of steady-state transfer functions in such models, whereas this assumption is considered acceptable at higher permeabilities. Conversely, discrete-fracture techniques, where the fracture geometry is resolved accurately in structured or unstructured mesh, are applicable to a broader set of problems but can become prohibitively slow for dense NFNs. A variety of cases were explored that demonstrate the strengths and weaknesses of the two approaches. The proposed hybrid approach discussed in this paper offers a good balance between run times and the quality of results. This paper offers practical insight into technique selection over a large class of problems. Furthermore, the proposed hybrid approach of combining dual and discrete NFN simulation in the same unstructured grid is novel but provides clearly demonstrated benefits. Stochastic generation of 3D NFNs, conditioned on secondary data, is used to control the clustering of natural fractures. Two distinct types of unstructured gridding methods, both supporting multi-point flux approximation, are used to mesh discrete NFNs.

Long‐period magnetotelluric (MT) data can be used to interpret upper mantle temperature, hydrogen content, and the presence of partial melt, all of which strongly influence mantle viscosity. We have collected the first long‐period MT data in Svalbard and have combined them with preexisting broadband MT data to produce a model of the electrical resistivity of Svalbard's upper mantle. Asthenospheric resistivities are low compared to stable continental settings but more comparable to young oceanic asthenosphere, suggesting that the physical state of Svalbard's upper mantle is controlled by its proximity to the Mid‐Atlantic Ridge. Interpretation of the MT model using a petrologically constrained genetic algorithm approach shows that partial melt is present in the uppermost asthenosphere beneath Svalbard. This is the first direct evidence of partial melt in Svalbard's asthenosphere from deep geophysical soundings. Viscosities calculated from the geophysical data show a low viscosity layer (~1018 Pa s) coincident with the partial melt layer, underlain by a higher viscosity layer (~1020 Pa s) extending to the transition zone. Viscosities calculated from glacial isostatic adjustment (GIA) data in Svalbard show a considerable range due mainly to uncertainties in past ice sheet models. Improved constraints on Svalbard's mantle viscosity from geophysical data may help to improve these GIA models.