Underground Resistivity Research Articles

Physics-guided deep learning-based inversion for airborne electromagnetic data

SUMMARY The Earth's subsurface structure provides critical insights into sustainable resource management and geologic evolution. The airborne electromagnetic (AEM) method is an efficient data acquisition technique and can be used to image the underground resistivity structure with high spatial resolution. However, inversion of the increasingly huge volume of AEM data poses a heavy computational burden. In this study, we develop a hybrid deep learning-based approach by using the physics-guided neural network (PGNN) which incorporates the governing physical laws into the loss function to solve the AEM inverse problem. The PGNN integrates the strength of data-driven method for representation learning with electromagnetic laws and allows for the underlying physical constraints to be strictly satisfied. We validate the effectiveness of our approach using both synthetic and field datasets. Compared with the classic Gauss–Newton method, our PGNN inversion system shows strong robustness against multiple noise sources and reduces the risk of being trapped in local extrema. Moreover, the PGNN-inverted results are physically more consistent with the AEM observations compared to the purely data-driven approach. Application to the field AEM data from Northern Australia demonstrates that the PGNN-based inversion framework effectively estimates the subsurface electrical properties with considerable lateral continuity and significantly higher efficiency, completing the inversion of more than 2734000 AEM soundings taking only minutes on a common PC. Our proposed PGNN-based method shows great promise for large-scale underground resistivity imaging, and the well-identified subsurface resistivity structure can effectively improve our understanding of resource distributions and geological hazards.

MT2DInv-Unet: A 2D magnetotelluric inversion method based on deep-learning technology

Traditional gradient-based inversion methods usually suffer from the problems of falling into local minima and relying heavily on initial guesses. Deep-learning methods have received increasing attention due to their excellent nonlinear fitting ability. However, given the recent application of deep-learning methods in the field of magnetotelluric (MT) inversion, there are currently challenges associated with achieving high inversion resolution and extracting sufficient features. We develop a neural network model (called MT2DInv-Unet) based on the deformable convolution for 2D MT inversion to approximate the nonlinear mapping from the MT response data to the resistivity model. The deformable convolution is achieved by adding an offset to each sample point of the conventional convolution operation, which extracts hidden relationships and allows the flexible adjustment of the size and shape of the feature region. Meanwhile, we design the network structure with multiscale residual blocks, which effectively extract the multiscale features of the MT response data. This design not only enhances the network performance but also alleviates issues such as vanishing gradients and network degradation. The results of synthetic models indicate that our network inversion method has stable convergence, good robustness, and generalization performance, and it performs better than the fully convolutional neural network and U-Net network. Finally, the inversion results of field data show that MT2DInv-Unet can effectively obtain a reliable underground resistivity structure and has a good application prospect in MT inversion.

Electrical Resistivity Tomography for Urban Underground Space by COMSOL Multiphysics

The development and utilization of urban underground space are of great significance to urban sustainable development. Geophysical methods have the characteristics of non-destructive detection, and electrical resistivity tomography has an obvious response to underground resistivity structure and can be used in urban underground space exploration. In this paper, COMSOL Multiphysics is used to realize the finite-element simulation of geoelectric models, and electrical resistivity tomography is used to explore resistivity anomalies of karst and fault models in urban underground environments. The results show that electrical resistivity tomography can identify karst and fault distribution effectively, and COMSOL Multiphysics promises to be a powerful tool for exploring urban underground space.

MeterGPX: A Smart Multimeter Embedded with Multigene Genetic Programming Model for Multiarray Antenna Transmitter

Genetic programming (GP) is an evolutionary algorithm used to produce high-quality solutions to various problems. It has seen few claims in circuitry and the development of antenna designs. The application of GP in the model of embedded digital systems on multi-channel antenna arrays of subsurface imaging equipment has not yet been investigated. This study focuses on designing and developing a digital multimeter embedded with a multigene genetic programming (MGGP) model for multi-array transmitter antenna used for subsurface imaging operating at a low frequency of 3.5 kHz to 18.5 kHz using Arduino microcontroller for prototyping. The electrical outputs of a transmitter antenna system employed in a subsurface imaging device require live measurement and monitoring during operation for data logging purposes. The amount of transmitted voltage, produced current, and operating frequency are significant parameters for mapping the underground resistivity, thus the produced GP models are functions of the three parameters. GP fitness function was determined through MATLAB software. The output current signal from the transmitter were imitated in Proteus simulation software using a current source in the designed current measuring circuit. This produced linear and nonlinear relationships of the electrical outputs where GP modeling was beneficially applied. The application of GP in with the microcontroller provided an accurate reading of frequency, current and voltage produced by the multi-array transmitter antenna. These measurements were displayed using LM016L LCD display. Moreover, this embedded digital multimeter on transmitter antenna avoids utilizing costly high voltage measuring devices.

Computation of Analytical Derivatives for Airborne TEM Inversion Using a Cole–Cole Parameterization Based on the Current Waveform of the Transmitter

Airborne transient electromagnetic (ATEM) technology is a technique often used in mineral exploration and geological mapping. Due to inductive polarization (IP) phenomena, the ATEM response curve often shows a negative response or declines rapidly to the attenuation curve. Traditional resistivity inversion techniques do not explain the IP response of a signal well, so the negative response is usually removed during data processing, resulting in a reduced correctness and authenticity of the findings. In this paper, in the parameter inversion based on the Cole-Cole model, the Jacobian matrix chain analysis method is used to calculate, and the current waveform calculation is also considered in the inversion. The results show that compared with the perturbation method, the analysis technique can greatly reduce the calculation time and improve the inversion efficiency. In the single-point one-dimensional inversion and lateral constraint quasi-two-dimensional inversion, the Cole-Cole four-parameter forward response has strong inversion accuracy, which can successfully invert the actual exploration content and the Cole-Cole four-parameter response. Some measured sounding data in the Qingchengzi survey area of Liaoning Province, China have a negative response to IP, and the resistivity scheme cannot be used alone for inversion, but the real underground resistivity structure can be obtained through the method studied in this paper, and good exploration results can be obtained.

Mineralization Based on CSAMT and SIP Sounding Data: A Case Study on the Hadamengou Gold Deposit in Inner Mongolia

The Hadamengou deposit is the largest gold deposit in Inner Mongolia. However, given that the sources of ore-forming alkaline magmatic hydrothermal solutions and ore-controlling structures are still controversial, the theories behind the genesis of the deposit have been controversial. In this study, four controlled-source audio magnetotellurics (CSAMT) and spectral induced polarization (SIP) profiles in the mining area were used to obtain the underground resistivity model and the pseudo section map of the apparent frequency dispersivity based on fine inversion. In the resistivity model, there are two high-resistivity blocks with resistivity greater than 3000 Ω m and three low-resistivity channels with resistivity less than 50 Ω m. Combined with the regional geological and drilling data, it is inferred that the high-resistance bodies, R4 and R5, may be alkaline magmatic intrusions related to multiple stages of magmatic hydrothermal activities, ranging from the Precambrian to Yanshanian periods. The highly conductive channels, C3, C5, and C4, may represent the Baotou-Hohhot fault, secondary faults, and ductile shear zone, respectively, which were formed in the Precambrian era and underwent multiple activations during the Hercynian to Yanshanian period. According to the spatial relationship, it is inferred that the ductile shear zone is an important ore-controlling and ore-hosting structure. However, the Baotou–Hohhot fault may be a pre-metallogenic fault rather than an ore-controlling fault. By comparing the resistivity model with the pseudo section of the apparent frequency dispersivity, it was found that all the known gold veins are located in the superimposed area of low resistivity and high-frequency dispersivity. It is speculated that the ductile shear zone outside the alkaline magmatic rock with the superimposed characteristics of low resistivity and high-frequency dispersivity is the favorable area for mineralization.

Imaging of karst cave using ground penetrating radar and electrical resistivity tomography: a case study in Shandong, China

A denudation-dissolution hilly area about 20km southeast of Jinan city, Shandong Province, China is characterized by karst features. The upper part of the area is covered by quaternary Holocene flood and slope deposits, and the lower bedrock is Ordovician limestone, which is a moderately weathered massive structure with developed joints and fissured fractures, and solution grooves can be seen locally. The local government to better serve the surrounding residents and the surrounding facilities, here is planned for the substation, for later security construction, electrical resistivity tomography (ERT) method and ground penetrating radar (GPR) method were used to investigate the underground karst caves. The aim of the joint use of the two methods is to better describe the characteristics of karst landform, a 160 MHz shielding antenna was employed in the GPR survey, while the frequency limits the scope of the depth of the electromagnetic waves can reach. However, it provides a lot of shallow stratigraphic information, which well represents the boundary between overburden and bedrock and the development of karst. The use of ERT method further makes up for the depth defects of the GPR method, and two-dimensional imaging of underground resistivity distribution was carried out, which also helps to further identify the characteristics of underground karst. The combination of the two geophysical methods can accurately delineate the development of underground karst, which also proves that the combination of the GPR method and ERT method has good application value in karst landform detection, and can also be applied to a similar geological survey.

Application of Time-Domain Airborne Electromagnetic Method to the Study of Qingchengzi Ore Concentration Area in China

Qingchengzi is an important polymetallic mineral concentration zone in China. However, long-term mining has led to the depletion of proven reserves in the Qingchengzi ore concentration zone. Therefore, it is necessary to carry out prospecting in the periphery of the existing ore bodies. Taking into account the complex surface conditions of the Qingchengzi mining area, we carried out an airborne time-domain electromagnetic survey in this region and obtained a high-precision underground resistivity model which covers the whole area. The quasi-3D inversion result shows a good agreement with the surface geological information. After analyzing the relationship between the underground geological structures and the resistivity model, we constructed a geological model with the distribution of the main strata, the rock masses and the faults. The reconstructed resistivity distribution of the entire region added new knowledge to understand the geology of the Qingchengzi survey area.

Combining multi-source data to evaluate the leakage pollution and remediation effects of landfill

After a landfill leaks, pollutants gradually accumulate in the strata surrounding the landfill to form a pollution source area, which provides a continuous stream of pollutants to the spreading pollution plume. The identification of the source area is of great significance to assess the scope of the pollution plume and to take corresponding remedial measures. The focus of this study was to construct a numerical model by integrating electrical resistivity tomography (ERT) data, hydrochemical data, and stratigraphic data, which can be used to assess the extent of the contamination from a leaky landfill and to suggest remediation designs. The concentration distribution of the leachate in the source area was obtained based on the underground resistivity distribution and the petrophysical relationship, which was used as the initial concentration in the numerical model. Borehole data and stratigraphic data provided stratigraphic information for the model construction. Hydrochemical data were used to calibrate the numerical model. The numerical model constructed by integrating multi-source data represented the spatial heterogeneity of the stratigraphic information and the geochemical information obtained based on borehole data, and the quantitative evaluation results such as the spatial distribution of pollutants were more accurate. The results showed that the leaking pollutants were collected in the fractured zones around the landfill. The important priority channel for pollutant migration was the densely distributed fault zone in the north and south of the landfill. Affected by hydraulic gradients and fractures, the spread area on the north side of the landfill was much larger than that on the south side. Pollutants moved from southwest to northeast, consistent with groundwater flow. After 20 years of leakage, the maximum diffusion range of the pollutants was estimated to be 1620.36 m. The vertical barrier walls designed based on ERT data and numerical simulation results were expected to block further migration of the pollutants. Combining multi-source data to build a numerical model can provide a valuable basis for environmental impacts and remediation recommendations.

Quasi-2-D Robust Inversion of Semi-Airborne Transient Electromagnetic Data With IP Effects

Semi-airborne transient electromagnetic (SATEM) method has been efficiently used recently in geophysical exploration due to its relatively portability compared to the standard airborne surveys. However, the SATEM data are often complicated by induced polarization (IP) effects manifesting as abnormal decay and sign reversal in the responses. The IP information can be extracted from SATEM data by joint inversion of the electromagnetic data into the electrical resistivity and IP parameters described in Cole-Cole model. In this paper, we introduce a quasi-two-dimensional inversion scheme to recover the resistivity and IP parameters from SATEM responses by (1) a fast semi-analytical method for Jacobian matrix calculation; and (2) a staged inversion strategy with lateral constraints. The methodology is examined on two synthetic polarized models. Our study indicates that the proposed scheme can improve inversion stability and recover the underground resistivity and IP property distributions.

Review on Resistivity Inversion of Underground Abnormal Bodies

With the development of petroleum in China, most oilfields have entered the stage of high water cut development. Well ground resistivity method as a new type of electric prospecting method, which have influence on the formation of small, measuring low cost advantages, gradually become one of the key technology of remaining oil distribution in the detection of resistivity inversion is through observation of the underground space apparent resistivity data reconstruction of the underground resistivity distribution, can realize the resistivity imaging of the underground space. In order to achieve the morphological characterization and spatial location of the abnormal area of underground resistivity, and then to carry out geological interpretation, the definition and properties of resistivity inversion are summarized, and the bottleneck problems encountered in practical engineering application are re-recognized and analyzed. On this basis, the theoretical method, numerical method and inversion method based on machine learning are introduced to solve the inverse resistivity problem of underground abnormal body. The inversion method based on deep learning is emphatically introduced, and its advantages and disadvantages and applicability are evaluated. It is pointed out that inversion is an ideal tool for data analysis. Then, it is pointed out that the development direction of resistivity inversion of underground abnormal body is to propose an optimized inversion network architecture based on deep learning.

Fast transdimensional Bayesian transient electromagnetic imaging for urban underground space detection

The transient electromagnetic method (TEM) is one of the most effective geophysical methods for detecting urban underground space. The underground resistivity distribution obtained by this imaging technique is important for engineers in urban construction and planning. The conventional transdimensional Bayesian (Trans-Bayes) imaging method gives the credible interval of the data model and quantifies the uncertainty in the imaging results. However, due to the large number of calculations needed and the slow speed of TEM forward modelling, the engineering practicality of Trans-Bayes method is poor. To solve this problem, we propose a fast Trans-Bayes imaging approach based on the Bayesian information criterion (BIC) and the adaptive Metropolis (AM) algorithm. Our simulation and field experiments show that this BIC-AM approach can greatly improve Trans-Bayes imaging efficiency while ensuring imaging accuracy. This method may provide fast uncertainty estimates for urban TEM data.

Estimation of near-surface temperature in Suwawa Geothermal Prospect, Gorontalo, Sulawesi, Indonesia, based on magnetotelluric and artificial neural network

A geophysical survey using broadband magnetotelluric (MT) technology was carried out in Suwawa Geothermal Prospect Area, Gorontalo Province, Sulawesi Island, Indonesia. The target of that research is to evaluate the geothermal potential hidden below the surface, based on underground resistivity distribution. However, the information about resistivity alone is not enough to get a proper understanding of the geothermal system in this area. Another important subsurface feature that could be useful for the evaluation is temperature. In this study, an attempt to predict the subsurface temperature using resistivity and limited information from a shallow borehole thermogram was carried out. Employing the dependency between resistivity and temperature an indirect temperature estimator was built, thanks to the applicability of artificial neural network (ANN) to learn the pattern connecting both parameters. Comparing some neural network training data shows that the predictive powers of the calibrated neural network highly influenced by the geological difference between the location of borehole and MT station. The best trained ANN was then used to predict the temperature below the other MT stations. The result shows that a proper ANN architecture is important to improve the deeper temperature estimation. The best ANN estimator was obtained from the BT01 and AMT39 data pair, which has the highest correlation as well. This preliminary study gives useful insight into how resistivity could be an alternative tool to delineate the near-surface temperature profile, in order to get a more comprehensive image of the subsurface geothermal system.

Time Series Data Inversion and Monitoring Method for Cross-Hole ERT Based on an Improved Extended Kalman Filter

In recent decades, the DC resistivity method has been applied to geophysical monitoring because of its sensitivity to hydrogeological properties. However, existing inversion algorithms cannot give a reasonable image if fluid migration is sudden and unpredictable. Additionally, systematic or measurement errors can severely interfere with accurate object location. To address these issues, we propose an improved time series inversion method for cross-hole electrical resistivity tomography (cross-hole ERT) based on the Extended Kalman Filter (EKF). Traditional EKF includes two steps to obtain the current model state: prediction and correction. We improved the prediction step by introducing the grey time series prediction method to create a new regular model sequence that can infer the potential trend of underground resistivity changes and provide a prior estimation state for reference during the next moment. To include more current information in the prior estimation state and decrease the non-uniqueness, the prediction model needs to be further updated by the least-squares method. For the correction step, we used single time-step multiple filtering to better deal with the case of sudden and rapid changes. We designed three different numerical tests simulating rapid changes in a fluid to validate the proposed method. The proposed method can capture rapid changes in the groundwater transport rate and direction of the groundwater movement for real-time imaging. Model and field experiments were performed. The inversion results of the model experiment were generally consistent with the results of dye tracing, and the groundwater behavior in the field experiment was consistent with the predicted groundwater evolution process.

Convolutional neural network inversion of airborne transient electromagnetic data

ABSTRACTAs an efficient geophysical exploration technique, airborne transient electromagnetics shows strong adaptability to complex terrains and can provide subsurface resistivity information rapidly with a dense spatial coverage. However, the huge volume of airborne transient electromagnetic data obtained from a large number of spatial locations presents a great challenge to real‐time airborne transient electromagnetic interpretation due to the high computational cost. Moreover, the inherent non‐uniqueness of the inverse problem also limits our ability to constrain the underground resistivity structure. In this study, we develop an entirely data‐driven convolutional neural network to solve the airborne transient electromagnetic inverse problem. Synthetic tests show that the convolutional neural network is computationally efficient and yields robust results. Compared with the Gauss–Newton method, convolutional neural network inversion does not depend on the choices of an initial model and the regularization parameters and is less prone to getting trapped in a local minimum. We also demonstrate the general applicability of the convolutional neural network to three‐dimensional synthetic airborne transient electromagnetic responses and the field observations acquired from Leach Lake Basin, Fort Irwin, California. The efficient convolutional neural network inversion framework can support real‐time resistivity imaging of subsurface structures from airborne transient electromagnetic observations, providing a powerful tool for field explorations.

A Rotational Measurement Scheme of Surface Nuclear Magnetic Resonance for Shallow Frozen Lake Characterization in Urban Environments

Surface nuclear magnetic resonance (sNMR) can directly and quantitatively detect groundwater, but its application in urban environments faces problems, such as low signal-to-noise ratios (SNRs) and difficulties in laying the coils. This study presents a rotational sNMR measurement scheme to accurately image a frozen urban lake. Through synthetic data experiments, we first demonstrate that sNMR data measured with six rotations can accurately invert underground water-bearing structures. Even when the environmental noise is high, this scheme can reflect the distribution of the water content in a frozen lake. Moreover, due to the small coil size, the inversion result is less affected by the underground resistivity. In field experiments, a large amount of high-quality sNMR data with average SNRs up to 12.8 dB were obtained from a high-noise environment using three reference coils. The 2-D distributions of the water content in the ice, water, and mud layers of the frozen lake were determined using the data measured from six rotations. The water content in the lake was found to be approximately equal to 1 m³/m³. Although there are still some problems with the measurements, such as inaccurate relaxation times and low resolutions in deep areas, further improvements in sNMR and the rotational detection scheme can facilitate the application of this approach to urban groundwater detection.

Three-Dimensional Inversion of Controlled-Source Electromagnetic Data in Fanchang Region, Anhui Province

Fanchang area of Anhui Province is an important link in the middle and lower reaches of the Yangtze River. To study the distribution of rock mass near the surface of the area and the contact relationship between the strata, and to determine the possible ore body space, we carry out a 15km controlled-source electromagnetic(CSEM) survey. The observed data is the electric field amplitude data simulating wide-area electromagnetic. For the single-section electric field amplitude data of controlled source, because of the lack of magnetic field data, the impedance data needed for audio geomagnetic inversion cannot be formed. This paper uses the limited memory quasi-Newton(L-BFGS) method to perform a three-dimensional inversion with the source directly. The theoretical model test results show that the inversion method can recover the underground resistivity structure and obtain better anomalous body resolution. The three-dimensional inversion of the CSEM data in Fanchang area shows that we could get the best inversion results when the grid size is about 1/3~1/2 of the dot pitch, the background resistivity is 100 Ω·m, and the error level is 5%. The inversion result of the full-section data, including two sources, shows that the south and north end of the profile is high-resistivity, and the intermediate resistivity is low. However, in the middle of the profile, the resistivity is significantly higher than the two sides. Combined with geological data, this change reflects the distribution of strata/rock masses on the surface of the work area and the contact relationship between them. This consistency verifies the feasibility and necessity of three-dimensional source inversion for the CSEM field amplitude |Ex| data of multi-source and single profile. The underground three-dimensional resistivity structure can be effectively obtained by using the L-BFGS method with the controlled source.

Application of wide-field electromagnetic method in shale gas survey in Eastern Kunlun, Qinghai province, China

In order to study the distribution of shale gas reservoir in the Babaoshan Basin of Eastern Kunlun, the wide-field electromagnetic (WFEM) survey was carried out to obtain the spatial distribution characteristics of the underground electrical volume resistivity based on the delineation of the scope of the Babaoshan Basin by regional gravity data. The basic characteristics of the basement, basin framework, and extension, vertical change, burial depth of dark mud shale in this area were identified, and the electrical distribution of the Babaoshan mud shale horizon was revealed, which has been proved to be a good geological effect by drilling. The exploration results show that the WFEM has significant effects on the exploration of shale gas occurrence strata, which meets the needs of investigation and evaluation of multi-layered and large-scale shale gas, and plays a good demonstration role in the follow-up shale gas exploration.

Monitoring of permafrost degradation along the Bei’an-Heihe Expressway in China

We performed a regional geological survey in discontinuous permafrost (PF) area along the Bei’an to Heihe Expressway between September 2009 and October 2016 to investigate the state and change of PF in northeast China. Underground resistivity changes were periodically detected along the foot of the subgrade and soil temperatures were monitored under the road foundation. We combined local meteorological and average annual air temperature data to analyze changes in PF thickness. The climate data show that the average annual temperature has gradually increased in the study area since 1980, rising to 0 °C around 1990, and the frost number has decreased to less than 0.5 since 1988. The soil temperature results show that the PF temperature in this section is higher than − 1 °C and is in a high-temperature PF zone affected by changes in seasonal air temperature. The PF under the left foot of the subgrade (LPF) and under the road central separation zone of the road (CPF) both show a decreased PF table, increased PF base, and severe PF degradation. Owing to different cover thicknesses, as well as differences in heat transfer between frozen and unfrozen soil, the base of LPF degraded faster than its table, to CPF the opposite is true. Underground resistivity measurements to verify the accuracy of the PF degradation results. The ground temperature monitoring data and climate data show good consistency. Comprehensive analysis results show that PF in the study area is in a strong degradation stage and will soon disappear.

Geoelectrical tomography data processing and interpretation for Pb-Zn-Ag mineral exploration in Nash Creek, Canada

The geoelectrical tomography survey was carried out to explore and characterize a (Zn-Pb-Ag) sulphide deposit in Nash Creek (NC), New Brunswick province, Canada. The exploration strategy has been conducted by the 2-D survey for a well-cut grid consisting of twelve surface lines (profiles) each around 2 km long, and 300 m apart, for the total area around 9.5 km2. The datasets (resistivity and induced polarization) were acquired using the Iris El-Rec Pro system with pole-dipole electrodes array spaced 50 m apart, and ten levels of data datum. The results of the 2-D inversion revealed that the underground resistivity and chargeability values in the exploration area have a range of (5 to 1300 Ωm) and (0-9.5 mV/V), respectively. The sulphide mineralization zones in the exploration area are characterized by moderate resistivity values (150-300 Ωm) and moderate to low chargeability values (>5.5 mV/V), with a depth of around (90140 m) from the surface. The 3-D visualization model clearly reveals that three main zones of sulphide mineralization are present in the exploration area. The predicted geological reserve of the sulphide ore in the exploration area was calculated. The inverted models revealed a good agreement with the existing geological features in the exploration area.