The Direct Current Resistivity (DCR) method is one of the well-known geophysical methods used for a wide range of areas such as mining geophysics, hydrogeophysics, and archaeogeophysics investigations. DCR data is generally collected along profile using multi-electrode and multi-channel measurement systems and interpreted using ‘two-dimensional (2D) or three-dimensional (3D) inversion algorithms. The inverse problem of DCR data is ill-posed (nonlinear, nonunique, and unstable). Therefore, the generally smoothing regularization inversion method is used for DCR data inversion. Additionally, a homogenous resistivity model is used as the initial model in regularized inversion. Hence, we generally obtain a smooth resistivity model after 2D/3D inversion. However, some structures such as buried archaeological targets, cavities, and fault structures have sharp boundaries with their neighboring medium. In this research, we propose enhancing 2D DCR data inversion results using a convolutional neural network (CNN), aiming for sharp boundaries. We developed a U-net-based CNN algorithm, named DCR2D_Net_Archeo. This method utilizes 2D inversion results as the input, with the real resistivity model serving as the output, streamlining geophysical data interpretation for archeological applications. We tested the DCR2D_Net_Archeo algorithm by using synthetic and real data. We showed that the developed resistivity model enhancement algorithm, DCR2D_Net_Archeo, improves smooth inversion results and buried archeological remains' size and position can be delineated from those enhanced models. KEYWORDS: DC Resistivity, 2D, Inversion, Deep Learning, archaeo-geophysics. ACKNOWLEDGEMENT: This study is part of the Ph.D. thesis of the first author and the manuscript about this study has been submitted to Pure and Applied Geophysics. This study is also made under the Ankara University Technopolis R&D projects (STBP code: 084286).

Geophysical methods have long been utilized for non-destructive testing of concrete structures, focusing on key techniques such as Ground Penetrating Radar (GPR), Direct Current Resistivity (DCR), and Seismic Methods (ultrasonic seismic). These methods assess factors like the condition of reinforcement bars, internal discontinuities, structural strength, and corrosion in concrete. In particular, DCR data, often collected with the Wenner array for fixed electrode distance, directly evaluates concrete corrosion through apparent resistivity. While some laboratory-scale resistivity tomography studies exist, this study introduces a novel measurement setup designed for multi-electrode and multi-channel DCR instruments. The setup enables data collection using different electrode arrays on building columns' single, adjacent, and opposite surfaces. We analyzed and compared the 3D inversion results obtained from synthetic and experimental data using various measurement setups and electrode arrays. This presentation will highlight the comparative results and insights gained from these configurations. Acknowledgment: This study was supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under project ID 121Y281. We extend our sincere gratitude to TÜBİTAK for their valuable support.

Al-Hassa area features Saudi Arabia's largest oasis and one of the world's largest naturally irrigated land. Moreover, Al-Hassa area is very close to Ghawar, known as the largest conventional oil-field in the world. Additionally, more than 280 natural springs used in the past to water the farmland where the water in some of the springs, is used to be warm. The quality of water also exhibits spatial variations, hinting at a complex subsurface that must be characterized. Finally, the available geological information from outcrops, are very limited, since the majority of the study area is covered by a sand-layer. Based on the above, it seems that this important for its natural resources (oil & gas, groundwater, low-enthalpy geothermy) area is partly unexplored or the data are not available. The purpose of this work is to reconstruct the 3D subsurface geophysical structure of the study area by combining different geophysical electromagnetic (EM) methods. Thus, three EM geophysical methods to construct a detailed 3D model of the subsurface were applied. Specifically, 46 magnetotelluric (MT) stations, 6 audio magnetotelluric (AMT) stations, and 35 transient electromagnetic (TEM) stations were acquired within Al-Hassa National Park. The data from all these EM soundings were processed and integrated to achieve the highest resolution from the surface to the maximum depth of investigation. 2D and 3D processing, and joint interpretation was applied to all EM data. The EM findings were confirmed by gravity measurement conducted in the same area. The integration of various geophysical data sets, including TEM, MT, AMT and gravity data, uncovers lateral discontinuities in resistivity, a complex structure, and fracture zones acting as pathways or barriers to groundwater flow. This comprehensive modelling approach offers invaluable insights into the subsurface dynamics, enhancing our understanding for the complexity of the study area.

Al-Hassa, located in eastern Saudi Arabia, hosts the world’s largest oasis and naturally irrigated land. Historically, 280 natural springs irrigated farms, with varying water quality suggesting a complex subsurface regime. To explore this, a multi-geophysical approach was applied in a remote part of the Al-Hassa National Park, where minimum cultural noise from agricultural and industrial activities is present. Five geophysical methods—210 gravity stations, a 3.6 km magnetic profile, 46 magnetotelluric (MT), six audio-magnetotelluric (AMT), and 35 transient electromagnetic (TEM) stations—were acquired to reconstruct a 3D subsurface model. Processing and integration of gravity and electromagnetic data revealed a complex underground structure with lateral resistivity (pr) discontinuities, a possible salt dome structure, and fracture zones affecting groundwater flow. Key findings include low-resistivity anomalies indicating potential basins filled with low-density (pd) sediments and high-resistivity zones suggesting basement rocks. The MT model reaches 4.5 km depth (z), while the 2D gravity model extends to 1.8 km. Low-resistivity zones in the MT data correlate with high-potential aquifers. The comparison of the gravity, TEM, and MT data showed good agreement, confirming the subsurface features. These results indicate significant hydrogeological complexity, impacting groundwater management and resource exploration. This comprehensive modeling approach provides insights into the qualitative hydrogeological characteristics and deeper subsurface conditions, potentially impacting the world’s largest conventional oilfield, Ghawar, located in the vicinity of the study area (A).

Summary This paper aims to unravel the geological complexities of the region, focusing on identifying key tectonic features and fracture orientations using gravity and magnetotelluric methods. A significant aspect of the study is the correlation found between these geological structures and groundwater quality, highlighting the impact of subsurface formations on environmental resources. The data from gravity surveys provided insights into subsurface density variations, while MT surveys offered valuable information about the electrical resistivity distribution. The combination of these methods proved effective in offering a comprehensive view of the subsurface features. The research findings are crucial for the sustainable management and preservation of natural resources in the region, demonstrating the effectiveness of integrated geophysical methods in environmental geophysics. This study contributes to a deeper understanding of the Middle Eastern geological framework, particularly in relation to the ongoing tectonic activities between the Arabian and Eurasian plates.

In this study, we suggested using a convolutional neural network (CNN) based algorithm to enhance two-dimensional (2D) Direct Current Resistivity (DCR) data inversion results. We developed U-net based CNN algorithm, named DCR2D_Net_Archaeo. We generated 1080 sets of 2D resistivity models that simulate buried archaeological remains. We calculated synthetic data for those models for different electrode arrays. We added 2% random noise to apparent resistivity data sets and inverted those data sets. We used the 2D inversion results as input and the corresponding real resistivity model as output. By using those 1080 input and output data sets we developed the DCR2D_Net_Archaeo algorithm. First, we tested this algorithm by using synthetic data. We showed that the developed algorithm improved the 2D classical smoothing regularization inversion and the buried body's geometry and depth can be found very close to the real model. Afterward, we also tested the developed algorithm with real data collected from two different archaeological sites. We showed that the buried wall cross-section location and depth are better found by the DCR2D_Net_Archaeo algorithm than by using the conventional inversion codes for the inversion of DCR data if we compare it with the excavated wall structure.

<p>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.</p> <p><strong>Acknowledgement:  </strong>This study is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) Project with Grant Number 119Y197. We thanks TUBITAK.</p>

Global optimization methods have recently become an essential option in the processing and interpretation of geophysical data sets. In this study, we compared the optimization capability of five global optimization techniques in terms of their ability to localize a global solution and computation cost using objective functions for direct current resistivity (DCR), and seismic refraction (SR) data processing and interpretation. The genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), surrogate optimization (SO), and pattern search (PS) optimization techniques were tested. The recently developed combined (global and local) optimization algorithm that uses the local optimization’s output to minimize the search space for the global search method and achieve faster convergence is applied to solve the optimization problem for comparison. Because all the optimization algorithms try to minimize the same objective function, we benchmarked the comparison with the result of the local optimization algorithm using the Gauss–Newton (GN) method. The combined optimization algorithms run for 500 iterations for both synthetic and real data. All the optimization algorithms satisfactorily reconstructed the positive anomalies (synthetic data) and defined a suspected fault (real data). However, considering the result of the local optimization as a benchmark (in terms of misfit and run time), only the GA and PSO satisfactorily reduce the misfit obtained from both DCR and SR inversion using the GN method. Therefore, the GA and PSO should be considered as the proper techniques for inverting the real DCR and SR data sets, with sequential use of local and global optimization methods.

Geophysical inversion is usually carried out to quantitatively analyze the earth model and estimate its physical properties. Successful delineation of these properties such as layer boundaries, or other near-surface structures are crucial to understand the near-surface inhomogeneity. In this study, we focus on the use of joint inversion of seismic refraction and geoelectrical resistivity datasets using local and global optimization methods. The idea is to integrate the two optimization techniques to minimize the challenges faced by each algorithm when applied alone. This hybrid algorithm (local and global) is applied on synthetic data representing simple resistivity and velocity models. About 70% of the anomalies in both seismic and DC resistivity methods were reconstructed in terms of amplitude and geometry using the local optimization algorithm, while the global optimization algorithm shows improved results as it reconstructed about 80% of the amplitude and geometry of the anomalies in both geophysical methods. The result of the synthetic application shows that the hybrid algorithm provides promising outputs in terms of resolution, geometry and amplitude of the anomalies, and computation run time.

The main geological structures in the Dammam Dome are defined by integrating geophysical measurements and applying new methodological approaches. Dammam Dome is characterized by a well-developed fracture/joints system; thus, high complexity of the subsurface is expected. Direct Current Resistivity (DCR) and Seismic Refraction (SR) geophysical survey aimed to map the Dammam Dome’s near-surface features. The geophysical data were acquired along two profiles in the northern part of Dammam Dome. To maximize the results from conducting DCR and SR measurements over a complex area, a combined local and global optimization algorithm was used to obtain high-resolution near-surface images in resistivity and velocity models. The local optimization technique involves individual and joint inversion of the DCR and SR data incorporating appropriate regularization parameters, while the global optimization uses single and multi-objective genetic algorithms in model parameter estimation. The combined algorithm uses the output from the local optimization method to define a search space for the global optimization algorithm. The results show that the local optimization produces satisfactory inverted models, and that the global optimization algorithm improves the local optimization results. The joint inversion and processing of the acquired data identified two major faults and a deformed zone with an almost N–S direction that corresponds with an outcrop were mapped in profile one, while profile two shows similar anomalies in both the resistivity and velocity models with the main E–W direction. This study not only demonstrates the capability of using the combined local and global optimization multi-objectives techniques to estimate model parameters of large datasets (i.e., 2D DCR and SR data), but also provides high-resolution subsurface images that can be used to study structural features of the Dammam Dome.

We present the results of a direct current (DC) resistivity and time-domain induced polarization (TDIP) survey exploring a copper ore deposit in Elbistan/Turkey. The ore deposit is elongated below a valley and is of disseminated form with sulfide content. DC and IP data were acquired using the pole-dipole array on eight parallel profiles crossing the valley perpendicularly. The length of each profile was 300 m with an inter-profile distance of about 50 m. The data were interpreted by a newly developed 2D DC/TDIP inversion algorithm. The finite element algorithm uses a local smoothness constrained regularization on unstructured meshes. The finite element forward solution, as well as the inverse problem, is solved by an iterative preconditioned conjugate solver. The depth of investigation (DOI) was determined from cumulative sensitivities of the 2D inversion algorithm results. Because of the dissemination of the ore, the 2D inversion of the DC data was ambiguous: However, due to the sulfide content, a strong chargeability anomaly associated with the ore body was detected. We show that chargeability anomalies can generally be detected in the absence or presence of corresponding resistivity anomalies. This highly chargeable structure was confined in lateral direction. Although the lower boundary of the structure could not be resolved by the applied field set-up, a rough estimation of it could be derived at a depth of 90 m using synthetic modeling analyses. The 2D chargeability models are consistent with existing borehole information.

Most geophysical inversions face the problem of non-uniqueness, which poses a challenge in the mapping and delineation of the subsurface anomalies. To tackle this challenge, a combined local and global optimization approach is considered for jointly inverting two-dimensional direct current resistivity (DCR) and seismic refraction (SR) data that aim to estimate the corresponding physical model parameters. In this combined approach, the output of the local optimization method is used to determine the search space and tuning parameters for the global optimization algorithm. The multi-objective genetic algorithm (non-dominated sorting genetic algorithm) was utilized to jointly optimize the objective functions of two different methods. Because the genetic algorithm is a population-based optimization method, it requires numerous forward calculations. To deal with the expected high computational cost associated with this approach, parallel computing was utilized for the forward function evaluations to reduce the run time of the entire process. The proposed approach was tested using synthetic two-dimensional resistivity and velocity models that had three different types of anomalies (dyke, positive, and combined positive and negative). The results showed an improvement in the anomaly delineation in the output of the combined local and global optimization method compared with the local optimization method. Additionally, similar synthetic models were tested using only the single objective global optimization algorithm (conventional global optimization), which showed promising anomaly delineation.

In this study, we investigated the use of stainless steel electrodes in metallic ore deposit exploration using induced polarization (IP) data. Nowadays, IP data are measured using multi-electrode and/or multi-channel measurement systems. The major advantage of this system is that a large amount of data can be collected in a short time. However, large numbers of non-polarized electrodes are needed in IP survey applications with these systems. Therefore, the data acquisition time is increased, and thus geophysical operational costs increase and we lose the advantage of fast data acquisition by the multi-electrode measurement system. We tested the use of steel electrodes for IP measurements using the superiority of new-generation devices to eliminate the polarization potential during measurement. We conducted IP measurements at a known metallic ore deposit located in Central Anatolia in Turkey to compare different data sets collected using steel electrodes and Cu–CuSO4 non-polarized electrodes. We investigated different time cycles (0.5 s, 1 s, 2 s, 4 s) to determine their effectiveness for steel electrodes. The results for the 2 s and 4 s time cycles were satisfactory. We also compared thin-surface and large-surface non-polarized electrodes and steel electrodes using the results of the 2 s optimal data acquisition time. The data obtained with large-surface and steel electrodes were more stable than the data obtained with thin-surface non-polarized electrodes. Additionally, we compared the use of all electrodes in parallel and in series. The results showed that the serial approach could also be used with appropriate measurement commands.

Summary Full QC of MT data is usually performed by interrupting data recordings. This limits the amount of low-frequency data recorded and therefore the quality of the degrades the response. In this study, we present a new type of workflow supported by the live transfer of MT data over a cellular network. QC of operations and data can be performed remotely at any time under this new workflow. This permits extending the duration of the recordings and matches the expected data quality for every recording. This approach was tested during a geothermal exploration project, consisting of 3 MT systems hooked up to a cellular network. Receivers transferred the data to the cloud in real-time. Live QC allowed us to move the MT sites to the next location after acquiring the lowest desired frequency with sufficient data quality. Daily production increased by %45 using the workflow without sacrificing the data quality.

Summary The roll-along measurement technique can be used as 3D acquisition procedure in electrical resistivity tomography. The total number of electrode combinations is very large in this technique. We suggest a modified roll-along measurement technique in this study. The parallel lines are separated as current and potential in this technique. Moreover, measurements are carried between the current line and its neighbour potential lines. The standard roll-along commands include both cross-line and in-line measurements. Whereas, there is no in-line measurements in technique suggested. We compared our data set with other data sets which were calculated standard roll-along and in-line (2D parallel) using a synthetic model application. In our synthetic data example (19x11 electrodes), the number of data is about 4.4 times less in the modified roll-along technique than in the standard roll-along technique. The inversion results demonstrate that the subsurface structures were better recovered in terms of their locations, upper and bottom depths, resistivities.

Saltwater intrusion and its spatial distribution using a multidisciplinary approach were investigated on the northeast coast of Bafra Plain, which is one of the most important delta plains in Turkey. Intensive agricultural activity in the study area, which supplies the local and international market with agricultural products, is increasing the importance of this ecosystem. Even the groundwater potential and supply are high; the main problem is the groundwater degradation due to salinization. Moreover, local lithological, tectonic and hydrological complexities increase the uncertainty in the interpretation of the collected (chemical, geophysical, etc.) data, providing an inaccurate geomodel. Specifically, in the study area, there are mixtures of freshwater/saltwater aquifers and geological units containing clay lenses/thin layers and fine-grained alluvium. At the same time, these geological formations have similar resistivity responses, and the ambiguity of the collected electric and electromagnetic geophysical data is high. Therefore, a third geophysical method (seismic refraction), which is sensitive to different physical parameters to constrain the uncertainty in the interpretation, was used. In this study, the salinization of Bafra Plain and complex aquifer system was revealed with the combined use of direct-current resistivity and transient electromagnetic data constrained by seismic refraction data and guided by hydrogeological and hydrochemical data. Using information obtained from multidisciplinary study, we inferred that the intrusion is monitored inland up to 3.5 km. The intrusion is traced after approximately the first 10 m depth in the central part of the plain. Additionally, in some areas, it was concluded that the intrusion is interrupted by clay lenses. Also, drainage channels constructed in the plain have brought soil salinization under control but have not completely succeeded in controlling the saltwater intrusion into the deeper aquifer. The saltwater intrusion can move more inland if groundwater pumping is not controlled, especially in areas close to the river.

Using the unstructured mesh, a new two-dimensional joint inversion algorithm has been developed for Radiomagnetotelluric and Direct current resistivity data. The unstructured mesh is generated with triangular cells, whose vertical and lateral lengths increase towards the depths. The Finite Element Method (FEM) has been used in the forward modelling part of the developed joint inversion algorithm. In the previous studies, structured grid-based joint inversion algorithms have been developed using the Finite Difference Method (FDM). In the structured grid-based algorithms, when the mesh is being generated with rectangular cells, the vertical lengths of the cells get bigger towards the depths while the lateral lengths remain constant. With the structured mesh, the undulated surface topography cannot be represented well enough. Also, because of the incompatible aspect ratio of model cell sizes in deeper model sections, the resolution of the model parameters will get smaller and cannot be resolved well with the structured grids. Imaging of surface topography and underground resistivity structures by the new algorithm requires fewer elements than those using structured grids. Therefore, the developed algorithm is faster than traditional 2D inversion algorithms. Furthermore, the resolution of the deeper model parameters has been increased by using the definition of the unstructured grid. A regularized inversion scheme with a smoothness-constrained stabilizer has been employed to invert the data. First, we have tested the developed joint inversion algorithm using synthetic data simplified from archaeological and mine site scenario and the results have been compared with the conventional algorithms using structured grids. We have also tested our algorithm with the real data which were collected from mineral investigation site at approximately 10 km east of the Elbistan district of Kahramanmara province, in the west of the Taurus Mountains, Turkey. The results show that the developed joint inversion algorithm is a powerful tool to detect both resistive and conductive targets. (C) 2019 Elsevier B.V. All rights reserved.

We investigated the effectiveness of a focusing regularization technique for the inversion of direct current (DC) resistivity data for a typical engineering problem. A smoothing stabilizer (Laplacian of model parameters) is generally preferred in the inversion (OCCAM's inversion) of DC resistivity data. Smooth reconstructions may be produced with this stabilizer, but some specific problems might require more focused images for adequate interpretations. For this reason, we investigated the capabilities of the minimum gradient support (MGS) stabilizer for providing shaper results. This stabilizer allows the a sharper reconstruction because its main effect is to minimize the area where strong differences occur between adjacent model parameters. We also analyze the effects of the focusing parameter, which is the parameter in the MGS expression controlling the level of sharpness of the final result. Our strategy for the selection of the optimal focusing parameter allows the resolution of distinct resistivity contrasts. Moreover, some artifacts that may arise in the use of the a very small focusing parameter disappear while using the normalized focusing parameter. We demonstrate these results by using both synthetic and field data examples. In the field data test, the subsurface image reconstructed using the proposed MGS approach matches well with the lithology inferred from borehole drillings.

A regularized inversion with a smoothing (SM) stabilizer is commonly used in 3D inversion of direct-current (DC) resistivity data. We have developed a new 3D inversion algorithm for DC resistivity data to investigate the efficiency of various stabilizing functionals. This algorithm is capable of incorporating 3D regularized inversion with [Formula: see text]-norm, [Formula: see text]-norm, SM, minimum support (MS), minimum gradient support (MGS), first-order minimum entropy, and total variation (TV) stabilizers. This is, to our knowledge, a comprehensive study of the effects of these seven different stabilizers on the inverse solution using synthetic and field data. As expected, the structure boundaries are found to be smooth when a SM stabilizer is used in the synthetic data examples. The upper bounds of the structures are recovered close to true model with [Formula: see text]-norm, [Formula: see text]-norm, first-order minimum entropy, and TV stabilizers, but the lower bounds cannot be recovered. Moreover, these stabilizers cannot completely resolve the resistivity of structures. The boundaries and resistivity of the structures are determined to be close to the true model with MS and MGS stabilizers. Similar results are obtained from inversions of field data collected in the Kültepe archaeological site (Kayseri, Turkey). A buried wall structure is correctly identified when the inversion used MS or MGS stabilizers. The archaeological structure found in the excavation studies reveals the success of the solution using MS and MGS stabilizers. These two stabilizers are recommended for an exploration of sharp boundaries structures.

Summary In this study, the use of stainless steel and non-polarizable electrodes was compared in the data acquisition of induced polarization (IP) data with multi electrode-channel measurement systems. The measurements were conducted in the metallic mine site by using stainless steel, thin and fat non-polarized electrodes with Cu-CuSO4 solution. The IP values obtained from all measurements were compared with the decay curve and pseudo-sections. Moreover, comparison was made using two-dimensional subsurface models which are obtained from two-dimensional inversion of each data set. The results show that stainless steel electrodes can be used instead of non-polarized electrodes in measuring systems with high measurement accuracy and powerful transmitter.