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

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

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.

Stochastic joint inversion of hydrogeophysical data for salt tracer test monitoring and hydraulic conductivity imaging

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.

Many geophysical studies have highlighted the limitations of relying on a single geophysical data set to understand subsurface conditions. Often, individual inversions yield inconsistent geologic models. To address this issue, this study integrates direct current (DC) resistivity and gravity data within a joint inversion (JI) framework. The developed algorithm, implemented in MATLAB, features adaptive fuzzy c‐means‐based model coupling and quantitatively measures the similarity between inverted physical property models. The proposed JI approach incorporates two key parameters: the number of geologic units and the average resistivity and density values for each unit. A comparative analysis was performed between the results of JI and those obtained from separate interpretations of DC resistivity and gravity data sets. The results indicate that the developed approach significantly improves the understanding of the subsurface by producing consistent resistivity and density models—an outcome rarely achieved through individual inversions in complex geological environments. Furthermore, the framework provides a quantitative metric of similarity between the inverted resistivity and density models at each iteration. To validate the proposed approach, tests were conducted on synthetic and real‐field data sets using a triangular mesh.

SUMMARYBecause the magnetotelluric (MT) method uses natural sources, the electric and magnetic fields recorded in the field acquisition are not directly used but usually converted into other MT response functions for interpretation such as inversion. Considering that inversion results are dependent on types of input data, it can be helpful to analyse different characteristics of MT response functions for inversion. In this study, we examine sensitivity patterns of MT response functions used commonly in MT inversion, which are the impedance tensor, apparent resistivity, phase, tipper, effective impedance and phase tensor; and investigate how their sensitivity patterns affect inversion results. We first describe overall tendencies of 3-D sensitivity patterns of the MT response functions, and then classify the MT response functions into six groups based on 2-D sensitivity patterns computed at the surface, which are briefly called ‘surface-sensitivity patterns’ in this study. The ’diagonal components of the impedance’ and ‘off-diagonal components of the phase tensor’, which have four petals-shaped surface-sensitivity patterns along the diagonal directions, belong to Group 1, and contribute to imaging 3-D subsurface structures from receivers installed evenly at the surface. Group 2 contains the ‘xy-components of the impedance, apparent resistivity and phase’ and ‘yy-component of the phase tensor’ whose surface-sensitivity patterns are linear in the y-axis. The ‘yx-components of the impedance, apparent resistivity and phase’ and ‘xx-component of the phase tensor’ that have strong linear surface-sensitivity patterns along the x-axis are classified into Group 3. The MT response functions of Groups 2 and 3 are useful for inversion of structures close to 2-D, whose strike extends along the y- and x-axes, respectively. Groups 4 and 5 include the ‘x- and y-components of tipper’ that possess linearly aligned two petals-shaped surface-sensitivity patterns in the x- and y-axes, respectively. The tipper can be helpful in imaging both 2-D and 3-D structures. The ‘effective impedance’ belongs to Group 6, whose surface-sensitivity patterns appear as a small circle. The surface-sensitivity patterns allow the effective impedance to have an advantage in interpretation of 1-D structures. By using several MT response functions for specific cases of 1-D, 2-D and 3-D interpretation of MT data, we investigate whether characteristics of the sensitivity patterns are reflected in modelling (simulating field data) and inversion results, and then suggest optimal MT response functions for those cases. In doing so, we show how to utilize the characteristics of the sensitivity patterns in inversion, and recommend the input MT response functions for inversion according to MT exploration situations. Our study provides basic information on similarities and differences of major MT response functions for inversion and insights on which MT response functions are suitable to increase the feasibility of MT inversion for different field situations based on the sensitivity patterns.

2D magnetotelluric inversion based on ResNet

An extensive magnetotelluric (MT) survey comprised of 85 sites has been acquired over the Darajat geothermal field in Indonesia to map the geothermal reservoir and the overlying clay cap. The rouged topography and the geometry of the margin of the clay cap makes the resistivity structure 3D at reservoir depth. Although 3D MT inversion is now commonly used in geothermal interpretations 1D and 2D MT inversions are still effective tools for a variety of tasks such as quality assurance. Lower dimensional inversion can also play two critical roles in determining and assessing the resistivity model derived by 3D inversion: 1) by providing a good starting model to reduce the computational cost of the 3D inversion, and 2) by providing a computationally feasible path to stochastic inversion of the data that provides realistic parameter’s standard deviations for use in assessing reliability of the resistivity model. Using a spatially constrained 1D stochastic inversion of the MT data, we investigate the common claim that 1D inversion can provide a pseudo 3D model which closely matches the 3D inversion for the overburden and clay cap layers. The discrepancy between the pseudo 3D and true 3D inverse models increases with depth, however the presence of the core resistive feature of the field is still indicated at approximately the same depth as found in the true 3D model. Analysis of the 1D model parameter probability density functions shows that layer thicknesses are better determined than layer resistivities.

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.

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.

To improve our understanding of the NICO deposit and Iron Oxide-Copper-Gold (IOCG) deposits in general we conducted both a regional-scale (10's km) and mine-scale (a few km) magnetotelluric (MT) study at the NICO deposit in the summer of 2010. An MT study involves the measurement, along a profile or on a grid, of variations in the natural magnetic field and correlated changes in voltages in the ground due to so-called "telluric" currents. The data collected enable a view of the electrical resistivity structure from the surface to the depth of the mantle. The survey itself is relatively easy to conduct, requiring a crew of only 2 or 3 persons due to the simplicity of installing the receiver equipment and the fact that no artificial or man-made energy source needs to be installed. The purpose of the MT survey is twofold. The first is to test the IOCG exploration model proposed by Corriveau et al (2010). The Corriveau et al alteration vector-to-mineralisation model enables effective exploration in a geologically complex region by providing a framework to understand the spatial and temporal relationships amongst the alteration products associated with the mineralization event (Corriveau et al., 2010). The model can be treated as a scientific hypothesis and tested. For example, Potter et al. (2013) have successfully tested the validity of the model to vector towards ore at the Fab lake showing. A fundamental result of Potter et al. (2012) is their demonstration that the model provides sufficient detail and tools that can be used by others to explore for ore. In this paper we test the spatial associations predicted by the model using geophysical methods due to the unique geophysical signatures alteration products in the model suggests. For example, sulphides should be conductive, but not magnetic. Magnetite should be magnetic, but not conductive. The second purpose of the MT work is more regional in nature. Regional MT studies (Heinson et al, 2006) and isotope studies at the Olympic Dam deposit (Johnson and McCulloch, 1995) have suggested a mantle source for mineralizing fluids. Seismic surveys (Drummond et al., 2006) support this notion by suggesting that deep faults or conduits beneath Olympic Dam permit mantle fluids access to the uppermost portions of the lithosphere. In addition, at Olympic Dam, the lithospheric architecture includes tapering out of an Archean lithospheric root, the extremity of which is directly under Olympic Dam (cf. Drummond et al., 2006; Cook et al., 1998; Snyder, 2008; Spratt et al., 2009). Whether similar roots and deep-seated conduits exist beneath the NICO deposit, and if there is evidence for large-scale fluid movement along them are key questions a regional MT survey can address.

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.

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.

ABSTRACTZ‐axis tipper electromagnetic and broadband magnetotelluric data were used to determine three‐dimensional electrical resistivity models of the Morrison porphyry Cu–Au–Mo deposit in British Columbia. Z‐axis tipper electromagnetic data are collected with a helicopter, thus allowing rapid surveys with uniform spatial sampling. Ground‐based magnetotelluric surveys can achieve a greater exploration depth than Z‐axis tipper electromagnetic surveys, but data collection is slower and can be limited by difficult terrain. The airborne Z‐axis tipper electromagnetic tipper data and the ground magnetotelluric tipper data show good agreement at the Morrison deposit despite differences in the data collection method, spatial sampling, and collection date. Resistivity models derived from individual inversions of the Z‐axis tipper electromagnetic tipper data and magnetotelluric impedance data contain some similar features, but the Z‐axis tipper electromagnetic model appears to lack resolution below a depth of 1 km, and the magnetotelluric model suffers from non‐uniform and relatively sparse spatial sampling. The joint Z‐axis tipper electromagnetic inversion solves these issues by combining the dense spatial sampling of the airborne Z‐axis tipper electromagnetic technique and the deeper penetration of the lower frequency magnetotelluric data. The resulting joint resistivity model correlates well with the known geology and distribution of alteration at the Morrison deposit. Higher resistivity is associated with the potassic alteration zone and volcanic country rocks, whereas areas of lower resistivity agree with known faults and sedimentary units. The pyrite halo and ≥0.3% Cu zone have the moderate resistivity that is expected of disseminated sulphides. The joint Z‐axis tipper electromagnetic inversion provides an improved resistivity model by enhancing the lateral and depth resolution of resistivity features compared with the individual Z‐axis tipper electromagnetic and magnetotelluric inversions. This case study shows that a joint Z‐axis tipper electromagnetic–magnetotelluric approach effectively images the interpreted mineralised zone at the Morrison deposit and could be beneficial in exploration for disseminated sulphides at other porphyry deposits.

A three-dimensional (3D) magnetotelluric (MT) survey has been carried out to delineate subsurface structures and possible fractures, for development of low-temperature geothermal resources in Pohang, Korea. Quite good quality MT data could be obtained throughout the survey region by locating the remote reference in Kyushu, Japan, which is ~480 km from the centre of the field site.3D modelling and inversion are performed taking into account the sea effect in MT measurements near the seashore. The nearby sea in the Pohang area affects MT data at frequencies below 1 Hz ~0.2 Hz, depending on the distance from the seashore. The most severe sea effects were observed in the south-east parts of the survey area, closer to Youngil Bay. 3D inversion with and without the seawater constraint showed very similar results at shallow depths, roughly down to 2 km. At greater depths, however, a strong sea effect seems to form a fictitious conductive structure in ordinary 3D inversion, especially in the south-eastern part of the survey region.Comparison between drilling results and the resistivity profiles from inversions showed that five layered structures can be distinguished the subsurface beneath the target area. They are: (a) semi-consolidated mudstones with resistivity less than 10 Ω m, which are ~300 m thick in the northern part and ~600 m thick in the southern part of the survey area; (b) occasional occurrence of trachybasalt and lapilli tuff within the mudstone layer has resistivity of a few tens of Ω m, (c) intrusive rhyolite ~400 m thick has resistivity of several hundreds of Ω m, (d) alternating sandstone and mudstone down to 1.5 km depth shows resistivity of ~100 Ω m, (e) a conductive structure was found at a depth of ~3 km, but more geological and geophysical study should be carried out to identify this structure.