Magnetotelluric (MT) data smoothing based on B-Spline algorithm and qualitative spectral analysis

In Magnetotelluric (MT) data processing, poor signal-to-noire ratio usually biaces the impedance tensor Z elements estimates. Strong electromagnetic (EM) noise may thus render the MT method impracticable in industrial areas. Noise due to periodic sources (i.e. power line harmonies) is easily removed by analog filtering in the field; but non-periodic EM noise (see e.g. Szarka, 1988, for an exhaustive list) should be handled more accurately in MT data processing.

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.

Summary The Simulated Annealing (SA) algorithm has been widely used in geophysical data processing. However, the seri al SA algorithm is limited by low computing efficiency. In this paper, the annealing schedule has been improved in the SA algorithm, and a constrained inversion of magnetotelluric (MT) data using the parallel SA algorithm has been applied. The test of synthetic data shows that the improved Very Fast Simulated Annealing (VFSA) method is significantly better than the traditional method. Additionally, the computing efficiency has been improved using the parallel SA inversion algorithm. This method has also been applied in MT data processing for geothermal exploration and good geological results have been achieved.

While much of the workflow for geophysical data processing and analysis is automated, final quality assurance typically requires the decisions of skilled human analysts and interpreters. The quality and reliability of geophysical inverse models depends directly on the effectiveness of input data. The influence of spurious data on derived data products used in the inversion has been reduced by this effectiveness. Failure to identify and mitigate bias in data products can lead to costly errors. By applying supervised machine learning (ML), a neural network can be trained to recognize features in data that a human domain expert would identify as characteristic of poor data quality. In this study, we use magnetotelluric (MT) data as an example of a geophysical data set appropriate for such a training exercise. While MT data are used to estimate the resistivity structure of the subsurface, the concepts we discuss are universal to seismic, potential fields, and other geophysical data sets. We train a neural network, pyMAGIQ (Python-based magnetotelluric impedance qualifier), with multiple hidden layers and demonstrate that it successfully generates the nonlinear mapping function required to assess the quality of MT data. The training set is a large database of frequency-domain MT impedance tensors from the National Science Foundation-funded EarthScope MT project. A human-assigned quality index is associated with each impedance. We apply pyMAGIQ to unrated MT data from the United States and Canada and confirm that the ML-assigned quality factors are consistent with those assigned by trained human operators. We also apply sensitivity analysis to the trained neural network. This reveals that the human- and ML-assigned data quality index depends on the magnitude of the confidence limits on (1) the phases and (2) the continuity of the apparent resistivities and phases with respect to frequency.

The effects of the ocean coast on magnetotelluric (MT) data have been studied and results are described in this article. MT soundings from various sites along coastal plains of Kachchh were acquired where volcanic rocks overlie Mesozoic sediments having potential resource prospects. The region of our study lies in the proximity of Gulf of Kachchh and Arabian Sea. This article describes the effects the presence of a conductive body on the acquired MT data. With the help of synthetic MT modelling and induction arrows we demonstrate the influence of shallow conductive body on the MT data. The modelling results when compared to the field observations show that much of the high frequencies are unaffected by the coast, however low frequencies of the data do seem to be distorted by onshore–offshore resistivity contrasts. The least affect on high frequencies can be attributed to very shallow water depth as well as to the fact that the onshore–offshore resistivity contrasts are not large enough to influence the high frequency data badly. The results presented here report for the first time the effect of the coast on the analysis on MT data from Kachchh. Due to several academic as well as industrial onshore–offshore MT experiments presently being carried out to explore its hydrocarbon potential, our results have an important bearing on designing and acquisition of future MT surveys in this region.

Power-line interference is one of the most common noises in magnetotelluric (MT) data. It usually causes distortion at the fundamental frequency and its odd harmonics, and may also affect other frequency bands. Although trap circuits are designed to suppress such noise in most of the modern acquisition devices, strong interferences are still found in MT data, and the power-line interference will fluctuate with the changing of load current. The fixed trap circuits often fail to deal with it. This paper proposes an alternative scheme for power-line interference removal based on frequency-domain sparse decomposition. Firstly, the fast Fourier transform of the acquired MT signal is performed. Subsequently, a redundant dictionary is designed to match with the power-line interference which is insensitive to the useful signal. Power-line interference is separated by using the dictionary and a signal reconstruction algorithm of compressive sensing called improved orthogonal matching pursuit (IOMP). Finally, the frequency domain data are switched back to the time domain by the inverse fast Fourier transform. Simulation experiments and real data examples from Lu-Zong ore district illustrate that this scheme can effectively suppress the power-line interference and significantly improve data quality. Compared with time domain sparse decomposition, this scheme takes less time consumption and acquires better results.

A method is described for filtering magnetotelluric (MT) data in the wavelet domain that requires a minimum of human intervention and leaves good data sections unchanged. Good data sections are preserved because data in the wavelet domain is analyzed through hierarchies, or scale levels, allowing separation of noise from signals. This is done without any assumption on the data distribution on the MT transfer function. Noisy portions of the data are discarded through thresholding wavelet coefficients. The procedure can recognize and filter out point defects that appear as a fraction of unusual observations of impulsive nature either in time domain or frequency domain. Two examples of real MT data are presented, with noise caused by both meteorological activity and power-line contribution. In the examples given in this paper, noise is better seen in time and frequency domains, respectively. Point defects are filtered out to eliminate their deleterious influence on the MT transfer function estimates. After the filtering stage, data is processed in the frequency domain, using a robust algorithm to yield two sets of reliable MT transfer functions.

SUMMARYSimulation-based probabilistic inversions of 3-D magnetotelluric (MT) data are arguably the best option to deal with the nonlinearity and non-uniqueness of the MT problem. However, the computational cost associated with the modelling of 3-D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT data sets. In this contribution, we present a novel and general inversion framework, driven by Markov Chain Monte Carlo (MCMC) algorithms, which combines (i) an efficient parallel-in-parallel structure to solve the 3-D forward problem, (ii) a reduced order technique to create fast and accurate surrogate models of the forward problem and (iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method. We demonstrate the feasibility and performance of our approach to invert for large-scale conductivity structures with two numerical examples using different parametrizations and dimensionalities. In both cases, we report staggering gains in computational efficiency compared to traditional MCMC implementations. Our method finally removes the main bottleneck of probabilistic inversions of 3-D MT data and opens up new opportunities for both stand-alone MT inversions and multi-observable joint inversions for the physical state of the Earth’s interior.

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.

Inversion is a fundamental step in magnetotelluric (MT) data routine analysis to retrieve a subsurface geoelectrical model that can be used to inform geological interpretations. To reduce the effect of non-uniqueness and local minimum trapping problems and improve calculation speeds, a data-driven mathematical method with a deep neural network was developed to estimate the subsurface resistivity. In this study, a deep learning (DL) inversion technique using a revised multi-head convolutional neural network (CNN) architecture was investigated for MT data analysis. We created synthetic datasets consisting of 100,000 random samples of resistivity layers to train the network’s parameters. The trained model was validated with independent noised datasets, and the predicted results displayed reasonable accuracy and reliability, which demonstrates the potential application of DL inversion for real-world MT data. The trained model was used to analyze MT data collected in the southwestern Athabasca Basin, Canada. The calculated results from the DL method displayed a detailed subsurface resistivity distribution compared to traditional iterative inversion. Since this approach can predict a resistivity model without multiple forward modeling operations after the CNN model is created, this framework is suitable to speed up the computation of multidimensional MT inversion for subsurface resistivity.

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.

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.

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.

SUMMARY Anewmethodologytoestimatemagnetotelluric(MT)tensorrelationships,calledEstimationof Local transfer-functIons by Combining Interstation Transfer-functions (ELICIT), is proposed wherebytheMTtensorrelationshipsofthelocalsitearederivedusingonlyinterstationtransfer functions. The MT impedance tensor and the geomagnetic transfer function at the local site are characterised by combining interstation tensor relationships between electric and magnetic fields at the local site with the horizontal magnetic fields acquired at a neighbouring site. The main property of the proposed method is that the employed interstation transfer functions are independently constrained, without the need to acquire the electric and the magnetic fields at the local site simultaneously to recover the local MT tensor relationships. Due to this property, the ELICIT method offers new possibilities for MT data acquisition and processing, providing significant improvements when the magnetic time-series at the local site are affected by local noise or are truncated. Error analysis shows that, even when magnetic fields are truncated, the quality of the results obtained following the ELICIT method are similar to those we would obtainifthemagneticfieldshadnotbeentruncated.Anotherimportantpropertyisthatdifferent neighbouring sites can be used to recover the tensor relationships at the local site. Averaging of results obtained using different neighbouring sites can be performed to improve the statistics. For our example data, when the ELICIT method is used to improve the statistics, errors of the estimates for periods between 1000 and 20000 s periodicities are clearly reduced. All interstation transfer functions are calculated doing remote reference and the bootstrap method is used to compute the errors, when necessary. Long period magnetotelluric data acquired in the Pyrenees and in the Atlas Mountains in Morocco, and magnetic data provided by ∗ ∗

Summary The Geological Survey of Sweden (SGU) conducted a few geophysical measurements in the frame of Barents bedrock mapping project to better understand the bedrock geology as well as study some key mineralisation zones in northern Norrbotten area. In this context magnetotelluric (MT), reflection seismic and ground gravity data were collected. The gravity, airborne magnetic and MT data were inverted in 3D and the resulting models were used for a combined interpretation. Most of the high resistivity features coincide with the crystalline intrusive rocks such as granites and basalts where the high susceptibility and high density zones coincide with the basaltic intrusions. The moderately low resistivity zones occur at locations mapped as meta-sedimentary rocks hosting known conductive mineralisations for example graphite. Most of these low resistivity zones show very low magnetic susceptibilities and at some locations low densities. To the east of the study area a very distinct low resistivity zone matches the occurrence of copper sulphide and graphite mineralisations. The zone becomes very conductive (< 10 Ohmm) and non magnetic. Compared to the other models the MT resistivity models lack resolution at the surface suggesting more detailed study using electromagnetic methods such as controlled source MT or time domain EM.