Three-dimensional resistivity models based on observed data in a grid pattern aids in understanding the volume and positional relations of subsurface regions having resistivity contrast with respect to the surrounding medium. However, acquiring ERT profiles in a regular 2D grid is labor intensive, time-consuming, and costly process. To overcome these constraints, pseudo 3D visualizations generated from multiple parallel 2D resistivity profiles are often used. Moreover, acquiring parallel 2D resistivity profiles in a grid is also often constraints by logistical challenges such as presence of utilities, waste mounds waterlogged patches etc. Therefore, in this work, a methodology is devised for creating a 3D database from inverse modeling results of multiple 2D resistivity profiles oriented in random direction in a region. In this approach, the azimuths of randomly oriented profiles are measured and utilized for introducing the y-coordinate. The proposed methodology is tested on 2D ERT datasets acquired at a chromite ore processing residue (COPR) waste dumping site.3D models generated through this approach has provided an enhanced visualization of conductive anomalous zones (i.e., < 15 Ohm.m) originating from the waste dump site. Further, the results obtained from the proposed methodology is compared with the quasi-3D volume generated by applying the 3D inversion algorithm on the compiled 2D ERT profiles datasets. The 3D visualization based on the proposed approach shows better correlation with anomalous zones present in the 2D inversion results of individual profiles. Thus, the methodology provides an efficient tool for creating 3D visualization models, and can be generalized for existing approaches...
Research Article
Enhanced 3D visualization based on the inverse modeling of non-parallel 2D ERT profiles: An approach for assessing anomalous zone
https://doi.org/10.21203/rs.3.rs-3098514/v1
This work is licensed under a CC BY 4.0 License
Version 1
posted
You are reading this latest preprint version
Electrical resistivity tomography (ERT)
Azimuth
Quasi-3D volume
Fence diagram
Chromite ore processing residue (COPR).
Electrical resistivity method is widely applied in various near surface studies such as delineating and monitoring of groundwater contamination zones (Lopes et al., 2006; Bichet et al., 2016; Mishra et al., 2019), identification of artificial recharge zones (Panda et al., 2020; Mishra et al., 2023), subsurface cavity detection (Metwaly and AlFouzan, 2013; Kidanu et al., 2020), geotechnical investigation (Attwa and Shinawi, 2017; Devi et al., 2017; Hasan et al., 2021), detection of buried structures for Archaeological prospection (Argote-Espino et al., 2013; Fischanger et al., 2019), mapping saline interface in coastal regions (Kazakis et al., 2016; Costall et al., 2018), and investigation of landslide prone regions (Perrone et al., 2014; Falae et al., 2019; Ullah et al., 2023) etc. Over the years, this method has evolved from basic 1D sounding to more complex 2D or 3D electrical resistivity tomography (ERT) imaging mainly due to advancement in computation facility and automation in data acquisition procedures (Griffiths and Barker, 1993; Loke et al., 2013). Although, the 3D ERT datasets provides most realistic models by imaging the multidimensional subsurface structure more accurately but acquisition of datasets in a 2D grid (for 3D modeling) is labor intensive, costly, and often constraints by logistical challenges present in the field (Mastrocicco et al., 2010; Aizebeokhai et al., 2011; Loke et al., 2013). On the other hand, ERT survey along a profile thereby 2D modeling is still more popular due to flexibility of data acquisition in various settings avoiding any hindrances and easiness to handle the modeling scheme (Bernstone et al., 1997; Dahlin and Loke, 1997). Thus, interpretation of ERT datasets is often performed based on 2D resistivity inverse models generated for such individual ERT profile data.
In addition, for near surface studies it is very crucial to map the distribution of the anomalous zones, preferential pathways, fracture networks, and their connectivity in the entire study area through 3D models. It could be trivial to visualize and interpret the entire study area by only looking into the individual 2D resistivity models generated from individual profile data set. To overcome this limitation, a few approaches are evolved to enhance the visualization and interpretation. In one of the approaches, uniform color code range is utilized across all the profiles by assuming that the geological variations are not very significant over a small grid (Olofsson et al., 2006; Cuthbert et al., 2009; Bichet et al., 2016; Mandal et al., 2019; Mishra et al., 2019). Although this approach has helped in delineating the anomalous zones and their variations across all the profiles but visualizing the entire anomalous zone in a single frame from such discrete 2D sections is still a limitation. Further, to overcome this limitation, quasi 3D models generated from multiple 2D resistivity profiles become popular and widely used as pseudo 3D plot in many studies such as imaging active faults (Vanneste et al., 2008), mapping spatial distribution of ore deposits in mineral exploration studies (Côrtes et al., 2016; Coelho et al., 2020; Embeng et al., 2022), monitoring leachate plumes and its subsurface distribution pattern in groundwater contamination studies (Olofsson et al., 2006; Moreira et al., 2017; Di Maio et al., 2018; Osinowo et al., 2018; Helene et al., 2020), imaging subsurface subsidence linked with Krast voids (Hamdan et al., 2012; Muhammad et al., 2012; Kidanu et al., 2020), Archeological prospection of buried features (Papadopoulos et al., 2006); mapping distribution of hazardous industrial waste seepage (Biosca et al., 2021). In this approach, the inverted datasets of multiple parallel ERT profiles data acquired in a grid are collated together in a single database to generate the quasi-3D models (Bernstone et al., 1997). However, acquiring ERT data along parallel profiles and in a grid is often not possible due to accessibility issues in the field, such as disposal of waste in unstructured manner in open dumps or landfills, presence of utilities and concrete structures in urban settings, marshy or waterlogged regions inside the study site etc. The presence of such type of field issues hinders the ERT data acquisition in parallel profiles or grid which further limits the creation of 3D database for generating the quasi-3D models (Chen et al., 2022). Similarly, few authors have applied the approach in which first the 2D ERT raw datasets corresponding to each of the profiles is collated into a single raw data file, afterwards the single raw data file is processed using 3D inversion algorithms to generate the quasi-3D volume (Bentley and Gharibi, 2004; Rucker et al., 2009; Rosqvist et al., 2020).
The plotted 2D resistivity section (i.e., pseudo-section) obtained after data acquisition contains the cross section of the subsurface in x-z plane and measured values of apparent resistivity corresponding to each of the quadrupoles (i.e., two pairs of current and potential electrodes) which is further processed or modelled to get the true resistivity distribution of the subsurface. Now, generally when the profiles are acquired in parallel lines with constant spacing between the profiles, it is possible to introduce the local coordinate system (Loke, 2018) for each of the profiles acquired in a grid. In order to bring in the y coordinate, a local coordinate system is introduced considering one of the profiles as starting profiles thus the local coordinate (x, y) is (0,0) for this particular profile and accordingly rest of the profiles will be assigned into the local coordinate system. Now for each of the profiles, we have x-y-z coordinates. However, in the case of non-parallel profiles, introducing a local coordinate system is challenging due to non-unform variations in the values of y coordinates for different profiles. In this work, a methodology has been developed for creating a regular grid database for 3D visualization (i.e., quasi-3D model) from the data acquired along multiple randomly oriented and non-parallel ERT profiles. In the existing literature creating the 3D database for such scenario is not well documented and rarely reported which many times limits the practitioners to interpret the findings solely on the basis of variations visible in the individual 2D ERT resistivity models. Hence, the crucial understanding about the overall subsurface variations and their connectivity could be missed out.
Further, to test the efficacy, the proposed methodology is applied on ERT profile datasets acquired over a historical COPR waste dumping site at Umaran, Kanpur, India wherein data acquisition in parallel profiles or regular grid was not possible due to accessibility issues. This site receives waste from the industries manufacturing basic chromium sulfate (BCS) from chromite ore between 1980-2005 (Matern et al., 2017). Although these industries are not functional now, the waste dumped by them is still lying there in the open land. The seepage of carcinogenic Cr (VI) from this waste into the nearby aquifer is a major concern as highlighted by the previous studies (Srivastava et al, 2013; Matern et al., 2017, Bhattacharya et al., 2019). Thus, mapping the distribution of contamination zone using the 3D resistivity models is very important to enhance the understanding contamination plume and its spread in this studied site. Such quasi-3D visualization even from non-parallel ERT profile data will also be helpful in locating the source, preferential pathways and their connectivity in the area.
The methodology adopted for this study has been presented by a workflow as given in Fig. 1. It begins with the compilation of inverted 2D ERT model values for each profile data as exported in ‘xyz’ format from the Res2DInv software or any other standard software that is being used for the inverse modeling of ERT profile datasets. In the second step, we will look for the availability of latitude and longitude values (or co-ordinates) of all the electrodes in the profile. If the above information is available, then the datasets corresponding to all the profiles will be collated together in a single file (Fig. 1). On the other hand, if the above information are not available for all the electrodes, then at least the coordinates of starting and end point electrodes or azimuth of one of the electrodes are required to further continue with this technique. Now, for the above case the following steps are required to create the required 3D database.
2.1 Introduction of Y column
The exported 2D ERT inverse model results file corresponding to each of the acquired profile in ‘xyz’ format stores information about the incomplete cartesian coordinates (as y-coordinate is still not present) and true resistivity values of each of data points. In the exported file, the first column corresponds to ‘x-location’ i.e., the horizontal location of the data point which is the mid-point of the electrode spread on which the measurements are made (i.e., at the mid-point of the potential electrodes). The second column represents the ‘z-location’ i.e., the vertical position of the data points and it is kept at the median depth of investigation of electrode array chosen during the data acquisition (Edwards 1977; Loke, 1999). The third or last column represents the resistivity values (ρ) at the point. However, to create a 3D database, another column carrying information about the y-locations of data points is necessary, therefore a fourth column for ‘y-location’ is introduced in each of the exported data files. Initially, the y-coordinate for each profile is set to zero values with an assumption that the profiles are oriented in east-west direction.
2.2 Assigning latitude and longitude
The prior information about the GPS coordinates of the first and last electrodes will be used to assign the GPS position to the remaining locations in the profile. First, the GPS derived coordinate will be assigned to the respective electrodes and by measuring the distance between the unknown location with respect to the known locations, the latitude and longitude of each of the other location will be estimated and assigned. Once this information is obtained for all the profiles are now aligned towards east-west direction with latitude and longitude values for each station location. The azimuth will further play the role to align all the profiles along their true directions.
2.3 Estimation of Azimuth
This is one of the crucial steps in the creation of the database for 3D visualization. In this step, the azimuth of each of the plotted lines are measured (Table 1). The azimuth values obtained for each of the profiles are applied for rotating the profiles using the simple trigonometric formulas. The azimuth values are obtained by measuring the angle between the lines in a clockwise direction. This is done by measuring the angle between the line with respect to north considering the initial coordinates as origin. In this work, the initial and end coordinates of all the measured profiles are known, and the maps are projected in universal timer Mercator (UTM) making the calculation of the azimuth a very straight forward work. As lengths of the acquired profiles are not very long and the latitude is not too far from the equator, we simplified our work by assuming that the cartesian coordinate is valid and the Mercator distortion is negligible (Table 1).
Table 1 Details of ERT profiles acquired over the COPR waste dumps site in Umaran, Kanpur dehat, India. Azimuth is measured in the clockwise direction from the geographical north
| S. No. | Profile | Profile length |
Direction | GPS coordinates (Start point) | GPS coordinates (End point) |
Degree rotation |
| 1 | P1 | 235 m | SSW-NNE | 26°24'16.99"N 80° 2'46.22"E |
26°24'23.26"N 80° 2'50.84"E |
33.50° |
| 2 | P2 | 355 m | SSW-NNE | 26°24'13.99"N 80° 2'46.11"E |
26°24'23.80"N 80° 2'52.96"E |
33.13° |
| 3 | P3 | 235 m | SW-NE | 26°24'11.74"N 80° 2'47.87"E |
26°24'16.85"N 80° 2'54.19"E |
48.0° |
| 4 | P4 | 295 m | SW-NE | 26°24'10.26"N 80° 2'49.29"E |
26°24'17.10"N 80° 2'56.67"E |
44.0° |
| 5 | P5 | 295 m | SW-NE | 26°24'8.66"N 80° 2'50.62" |
26°24'16.27"N 80° 2'57.40"E |
38.0° |
| 6 | P6 | 235 m | SW-NE | 26°24'6.60"N 80° 2'53.01"E |
26°24'13.06"N 80° 2'57.50"E |
31.8° |
| 7 | P7 | 230 m | SW-NE | 26°24'6.15"N 80°2'55.50"E |
26°24'12.51"N 80°2'59.93"E |
32.10° |
| 8 | P8 | 230 m | SE-NW | 26°24'5.36"N 80° 2'52.87"E |
26°24'11.13"N 80° 2'47.81"E |
− 39.70° |
| 9 | P9 | 295 m | SE-NW | 26°24'6.61"N 80° 2'53.88"E |
26°24'13.56"N 80° 2'46.68"E |
− 43.33° |
| 10 | P10 | 355 m | SE-NW | 26°24'8.32"N 80° 2'55.22"E |
26°24'17.25"N 80° 2'47.06"E |
− 38.90° |
| 11 | P11 | 295 m | SE-NW | 26°24'12.34"N 80° 2'56.21"E |
26°24'19.21"N 80° 2'49.11"E |
− 43.25° |
| 12 | P12 | 355 m | SE-NW | 26°24'12.69"N 80° 2'59.87"E |
26°24'21.05"N 80° 2'51.22"E |
− 42.80° |
The above formula (i.e., eq. 3) will be applied to measure the azimuth for all the profiles. The measured azimuth values will give us an idea about amount of rotation required for each of the profiles (Table 1). Once the profiles are rotated, the stations along east-west directions are now brought to their actual positions/orientation. Afterwards, all the profiles data with their true orientation are compiled in a single file which contains the latitude, longitude along with the inverted resistivity values.
2.4 Creation of Quasi-3D volume
The combined database can now be imported into any gridding software. This database can be reprojected with any suitable projection method and then gridded in three dimensions to obtain a quasi-3D volume of the whole study area. As the lines are independently subjected to inversion there exist some sharp boundaries and the contours follow the pattern of the fence diagram. To overcome this issue, we have applied filtering to smoothen the contours. The generated 3D database can now be utilized for creating the enhanced visualization of 2D ERT profiles in various 3D models such as fence diagram/ horizontal or vertical slices/ 3D volume/ iso-surfaces etc.
3.1 Mapping groundwater contamination zone over a historical chromite ore processing residue (COPR) dump site
The efficacy of the proposed approach is tested with the ERT data acquired over a historical chromite ore processing residue (COPR) dump site at Umaran village, Kanpur dehat, UP, India. This area is known for a small cluster of industries producing basic chromium sulphate (BCS) from chromite ore using the traditional age-old high lime process (Matern et al., 2017). In this ore extraction process a huge amount of residue termed ‘COPR’ is generated which is highly rich in chromium (Cr). The residue generated is dumped adjacent to agricultural lands or along the roadside in proximity to the industrial cluster. The previous geochemical studies performed in this region have highlighted the presence of high level of Cr concentration (i.e., more than the permissible limit of 0.05 mg/L) in the groundwater samples collected from various drinking water sources such handpumps, overhead tanks, tube well etc. (Srivastava et al, 2013; Matern et al., 2017, Bhattacharya et al., 2019). Also, the previous geophysical study based on 2D ERT performed by Mishra et al. (2019) has highlighted the anomalous zones as low resistivity zones (< 15 Ohm.m) which might have allowed the seepage of contamination plume that leached out from the COPR waste site. The authors have used the uniform color scheme for all the 2D resistivity models to highlight the continuity of the low resistivity zones. Thus, the interpretation was performed on the basis of the variations obtained in the individual 2D resistivity models. The study was helpful in delineating the firsthand information about the spatial extent and migration pathway of the anomalous subsurface zones. However, detailed spatial extent of the low resistive anomalous zones could not be understood due to nonavailability of 3D visualization models such as fence diagram, slice plots, iso-surface maps etc. This was mainly restricted by the accessibility issues that led to acquire the data along randomly oriented profiles. Maintaining the grid pattern was mainly constraints by some of the challenges such as random distribution of waste mounds with 1–2 m height, presence of a transmission tower which is usually built to support the overhead high voltage powerline, and the proximity of the waste site to an unplanned industrial setup. These constraints have resulted in the deviation of the profiles orientation from the actual orthogonal and parallel lines (see Fig. 2). A total of twelve ERT profiles (P1-P12) are used for this work (Table 1) (Mishra et al., 2019). The inverted datasets of each of the profiles (P1-P12) are imported in the ‘xyz’ format and subsequently a y-coordinate is introduced in each of the datasets by measuring the azimuth of each of the lines with respect to the east-west direction. Afterwards, all the modified datasets are collected into a single file to be utilized for creating the 3D visualization plots.
The fence diagram plot generated for the site captured the resistivity distribution across all the profiles i.e., P1-P12 in a single frame (Fig. 3). Also, it is evident that the crossover points of all the profiles matched in terms of resistivity values observed which is a strong validation of the subsurface variations. The profiles P7, P8 and P12 are acquired outside the dump site in southeast, northwest and southwest direction, respectively. The top part of the profiles P7 and P12 are mostly resistive, however, as we move deeper into the 2D resistivity sections we observe that a low resistivity zone is present below the profile P8 but not in the modeled section of profile P12. Similarly, the other profile P8 shows mostly the low resistivity variations. In addition, the fence diagram indicates that the low resistivity zones (i.e., < 15 Ohm.m) are mostly located in the south to southeastern direction (Fig. 3).
In order to visualize the lateral continuity and distribution of the low resistivity zone (> 15 Ohm.m), a quasi-3D volume is reconstructed from the collated 2D ERT data (Fig. 4). The generated 3D volume is clearly demarcating the high resistivity and low resistive zones within the COPR waste dump site. As this dump site is in proximity to settlements, the physical boundaries are in close contact with the agricultural land, and the profiles are acquired in a manner that the variations around this boundary could be captured (P1-P6) (Mishra et al., 2019). Now, it is evident that the high resistivity zones are mostly distributed towards the agricultural land side and low resistivity zones are within the open dumpsite.
Also, from the quasi-3D volume we infer that the spatial extent of the low resistivity zones is limited in the initial parts of the profiles (P1-P4) but as move towards the initial parts of profiles (P4-P6) these low resistivity zones are more spread out spatially. Also, the spatial extent of low resistive zones is more in central part in comparison to the starting and end part of the quasi-3D volume. However, to further characterize the distribution of low resistive zones, an iso- surface map representing the low resistivity zones is created by making the resistivity variations above 15 Ohm.m as transparent (Fig. 5). The iso- surface map obtained suggests that as we go deeper below the high resistivity region (which is transparent now) in the northern direction, an isolated patch of low resistivity is visible which is continuing from the low resistivity regions present below the waste dump boundary. Thereby, implicating that contamination plume is migrating below the agricultural land in the northeastern direction.
In addition, the horizontal slices generated at the depth levels of 0 m, 20 m and 40 m from the 3D database clearly indicate the lateral extent of the low resistivity zones at various depth levels (Fig. 6). The horizontal slice of the top layer indicates that the low resistivity zones are mostly restricted below the dump site and a sharp contrast is observed as we move away from the dump site, i.e., towards the agriculture boundary. Now, as we move into the next horizontal slice, which is located at a depth of 20 m, it is observed that the low resistivity zone is migrating below the agricultural land towards the Northeast direction which was also evident from the iso-surface map (Fig. 5). Similarly, the horizontal slice at depth 40 m is also indicative of the migration of the low resistivity zones in the northeast direction as well as in south and southwestern direction (Fig. 6).
This technique is designed for creating a 3D database from the 2D inversion results of multiple ERT profiles oriented in random directions by measuring the azimuth of all the profiles and subsequently using it for introducing the y-coordinate which is not present in the 2D ERT inverted datasets. On the other hand, the existing approach for collating ERT datasets of multiple profiles is limited to the profiles which are acquired in a parallel grid by creating a local coordinate system (Loke, 2018). However, the proposed methodology developed in this work doesn’t require a local coordinate system. Instead, it measures the azimuth for each of the profiles on the basis of coordinates of initial and end location of the profiles (see Figs. 2 and 7a). Therefore, the latter approach has an advantage in terms of its applicability for the case of profiles acquired in random directions as well as for the profiles which are acquired parallel to each other (azimuth will be constant for all the profiles). In addition, the application of the proposed methodology can be further extended for generating the quasi-3D volume by applying the 3D inversion algorithm on the acquired ERT profile datasets. Now, to generate the quasi-3D volume using the 3D inversion algorithm, first, the developed methodology is applied for collating the acquired 2D ERT datasets into a single file to be used for 3D inversion algorithm (Fig. 7b). Thus, the proposed methodology is generalized well on both the approaches of creating quasi-3D volumes.
Further, a comparison is made between the above-mentioned approaches of generating a quasi-3D volume (Figs. 7a and 7b). To make the fair comparison, the inversion algorithm is kept the same (i.e., robust). Upon comparing with Fig. 7a, the quasi-3D volume obtained from the 3D inversion of 2D ERT datasets (Fig. 7b) also indicates the presence of sharp resistivity contrast in the top part of the dumpsite when we move towards the agricultural land. Similarly, the central part of the 3D volume is mostly comprising of low resistivity zones (< 15 Ohm.m). Further, a 3D difference plot showing the difference between both the approaches by calculating the difference of the volume is shown in Fig. 7c. In the central part of Fig. 7c, it observed that the difference between them is very less which indicates that both the quasi-3D plots are in good agreement. Which might be attributed to the fact that the data density is sufficient in this part as a reasonable number of profiles are crossing each other (Fig. 2). This reasoning is further corroborated by the fact that the 3D difference plot indicates that the larger variations are mostly confined towards the end part of the 3D volume where lesser number of profiles are present (Figs. 7c and 2). Moreover, if we compare both the approaches quantitatively. In our case, it is observed that performing a 3D inversion algorithm on the compiled ERT profile datasets is computationally extensive and requires 5–6 hours (with a workstation having Intel ® Xeon ® 6 core processor, 16 GBs of RAM and 15 MBs cache size) more in comparison to inverting them individually using 2D inversion algorithm (with Intel ® 4 core ™ i5 processor, 8 GBs RAM and 6 MBs cache size). In addition, the 3D inversion algorithm requires a high-end workstation with good configuration as the number of nodes created in the forward modelling is more than 32 lakhs and the low configuration machines crash during the inversion process. Although, being computationally extensive, 3D inversion results can be more accurate in comparison to 2D inversion along individual profile as they consider 3D variation of subsurface anomalous zones if the acquired data are in regular grid i.e., along parallel profiles. Therefore, while creating the quasi-3D volumes using the proposed methodology, the spatial uniformity of ERT profiles and data density plays a crucial role to obtain the accurate 3D visualization of the subsurface variations in the challenging and complex field scenarios.
Once the single 3D database file is created in the proposed approach, it can now be utilized for generating visually enhanced representation commonly used in various near surface studies such as Quasi-3D volume, horizontal slices/fence diagrams, iso-resistivity maps etc. thereby providing a one stop solution for enhanced interpretation of ERT data. Also, all the data points are georeferenced (i.e., have GPS coordinates) along with associated with corresponding depth and resistivity values. Such georeferenced datasets will be further useful in creating 2D ERT cross-sections, slice plots, and fence diagrams in multiple directions as required for the interpretation. However, 2D inverse modeling of individual ERT profile data works under an assumption that the subsurface resistivity varies only in the direction of the profile (i.e., x direction) and with depth (i.e., z direction). Therefore, the quasi-3D representation generated from such datasets requires an optimal spacing between the adjacent parallel profiles and as per the existing practice it should not be more than four times the electrode spacing (Gharibi and Bentley, 2005; Mastrocicco et al., 2010; Kidanu et al., 2020). Further, this approach is designed in such a way that it can accommodate all the ERT profiles irrespective of their directions. Therefore, in scenarios where acquiring datasets in parallel profiles is constrained due to field limitations, suitable number of profiles as a gap filer should be added where there is sparsity present in the datasets. Still all the datasets can be compiled using the present approach and can get an enhanced 3D visualization of quasi-3D resistivity variation of the study area.
A conceptual workflow is developed for creating a 3D database from the 2D ERT database of profiles having non-parallel orientation in a grid by measuring the azimuth of all the profiles. The 3D database can be further utilized for generating quasi-3D models such as fence diagram, slice plots, iso-surface maps, quasi-3D volume etc. for enhancing the visualization of subsurface anomalous zones. Further, the methodology is applied on the 2D ERT datasets acquired over historical COPR waste dump sites, wherein the data acquisition in a parallel grid was not possible due to the accessibility issues. The proposed approach helps to derive various quasi-3D models from the compiled 2D resistivity inversion results of individual profiles. This has effectively enhanced the overall understanding about the distribution of low resistive zones (i.e., < 15 Ohm.m) in the study area. The resistivity values at the crossover points of the ERT profiles is the same at every location, thus providing a strong validation. Also, the iso-surface map reveals the spatial extent of the low resistive zones (< 15 Ohm.m) in the entire site. Therefore, the workflow developed in this work provides a vital aid for creating a 3D visualization of the subsurface resistivity variation from non-parallel 2D ERT data which was earlier limited only for parallel 2D ERT profiles. The proposed methodology is versatile in terms of its applicability for the case of parallel and non-parallel 2D ERT profiles as well as generating the quasi-3D volume using both 2D and 3D inversion algorithms. Also, various quasi-3D models prepared using the 3D database could be used for locating the critical zones and extent of variation of subsurface anomalous zones. In addition, by generating the quasi-3D models and iso-surface maps, a quantitative estimate about the volume and position of the subsurface anomalous zones could be made effectively.
Funding
This work was supported by CRG/2020/002275. Author AM (3rd author) has received research support from Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Author Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Utsav Mishra, Ansuman Bakshi and Animesh Mandal. The first draft of the manuscript was written by Utsav Mishra and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Acknowledgements
Authors gratefully acknowledge the financial support (File No. CRG/2020/002275) from Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, to carry out the field work as required for this study. Authors also thankfully acknowledge the support of all the team members in smooth conduction of the geophysical surveys. The first author (UM) thanks the Ministry of Human Resource Development (MHRD), Government of India for awarding the research fellowship.
- Aizebeokhai, A. P., Olayinka, A. I., Singh, V. S., & Uhuegbu, C. C. (2011). Effectiveness of 3D geoelectrical resistivity imaging using parallel 2D profiles. Current Science, 1036-1052.
- Argote-Espino, D., Tejero-Andrade, A., Cifuentes-Nava, G., Iriarte, L., Farías, S., Chávez, R. E., & López, F. (2013). 3D electrical prospection in the archaeological site of El Pahñú, Hidalgo State, Central Mexico. Journal of Archaeological Science, 40(2), 1213-1223.
- Attwa, M., & El-Shinawi, A. (2017). An integrative approach for preliminary environmental engineering investigations amidst reclaiming desert-land: a case study at East Nile Delta, Egypt. Environmental Earth Sciences, 76(8), 1-18.
- Bentley, L. R., & Gharibi, M. (2004). Two-and three-dimensional electrical resistivity imaging at a heterogeneous remediation site. Geophysics, 69(3), 674-680.
- Bernstone, C., Dahlin, T., & Jonsson, P. (1997). 3D Visualization of a Resistivity Data Set—An example from a Sludge Disposal site. In Symposium on the Application of Geophysics to Engineering and Environmental Problems 1997 (pp. 917-925). Society of Exploration Geophysicists.
- Bhattacharya, M., Shriwastav, A., Bhole, S., Silori, R., Mansfeldt, T., Kretzschmar, R., & Singh, A. (2019). Processes governing chromium contamination of groundwater and soil from a chromium waste source. ACS Earth and Space Chemistry, 4(1), 35-49.
- Bichet, V., Grisey, E., & Aleya, L. (2016). Spatial characterization of leachate plume using electrical resistivity tomography in a landfill composed of old and new cells (Belfort, France). Engineering Geology, 211, 61-73.
- Biosca, B., Arevalo-Lomas, L., Izquierdo-Diaz, M., & Diaz-Curiel, J. (2021). Detection of chlorinated contaminants coming from the manufacture of lindane in a surface detritic aquifer by electrical resistivity tomography. Journal of Applied Geophysics, 191, 104358.
- Chen, X., Guo, Z., Liu, C., Liu, J., & Wu, Q. (2022). Groundwater Detection Using the Pseudo-3D Resistivity Method: A History of Case Studies. Applied Sciences, 12(13), 6788.
- Coelho, C. V. S., Moreira, C. A., Rosolen, V., Bueno, G. T., Salles, J., Furlan, L. M., & Govone, J. S. (2020). Analyzing the spatial occurrence of high-alumina clays (Brazil) using electrical resistivity tomography (ERT). Pure and Applied Geophysics, 177, 3943-3960.
- Côrtes, A. R., Moreira, C. A., Veloso, D. I., Vieira, L. B., & Bergonzoni, F. A. (2016). Geoelectrical prospecting for a copper-sulfide mineralization in the Camaquã sedimentary basin, Southern Brazil. Geofísica internacional, 55(3), 165-174.
- Costall, A., Harris, B., & Pigois, J. P. (2018). Electrical resistivity imaging and the saline water interface in high-quality coastal aquifers. Surveys in geophysics, 39(4), 753-816.
- Cuthbert, M. O., Mackay, R., Tellam, J. H., & Barker, R. D. (2009). The use of electrical resistivity tomography in deriving local-scale models of recharge through superficial deposits. Geological Society of London.
- Dahlin, T., & Loke, M. H. (1997). Quasi-3D resistivity imaging-mapping of three-dimensional structures using two-dimensional DC resistivity techniques. In 3rd EEGS Meeting (pp. cp-95). European Association of Geoscientists & Engineers.
- Devi, A., Israil, M., Anbalagan, R., & Gupta, P. K. (2017). Subsurface soil characterization using geoelectrical and geotechnical investigations at a bridge site in Uttarakhand Himalayan region. Journal of Applied Geophysics, 144, 78-85.
- Di Maio, R., Fais, S., Ligas, P., Piegari, E., Raga, R., & Cossu, R. (2018). 3D geophysical imaging for site-specific characterization plan of an old landfill. Waste Management, 76, 629-642.
- Edwards, L. S. (1977). A modified pseudosection for resistivity and IP. Geophysics, 42(5), 1020-1036.
- Embeng, S. B. N., Meying, A., Ndougsa-Mbarga, T., Moreira, C. A., & Amougou, O. U. O. (2022). Delineation and Quasi-3D Modeling of Gold Mineralization Using Self-Potential (SP), Electrical Resistivity Tomography (ERT), and Induced Polarization (IP) Methods in Yassa Village, Adamawa, Cameroon: A Case Study. Pure and Applied Geophysics, 179(2), 795-815.
- Falae, P. O., Kanungo, D. P., Chauhan, P. K. S., & Dash, R. K. (2019). Electrical resistivity tomography (ERT) based subsurface characterisation of Pakhi Landslide, Garhwal Himalayas, India. Environmental Earth Sciences, 78, 1-18.
- Fischanger, F., Catanzariti, G., Comina, C., Sambuelli, L., Morelli, G., Barsuglia, F., & Porcelli, F. (2019). Geophysical anomalies detected by electrical resistivity tomography in the area surrounding Tutankhamun's tomb. Journal of Cultural Heritage, 36, 63-71.
- Gharibi, M., & Bentley, L. R. (2005). Resolution of 3-D electrical resistivity images from inversions of 2-D orthogonal lines. Journal of Environmental and Engineering Geophysics, 10(4), 339-349.
- Griffiths, D.H., & Barker, R.D. (1993) Two-dimensional resistivity imaging and modelling in areas of complex geology. Journal of Applied Geophysics 29, 211-226. https://doi.org/10.1016/0926-9851(93)90005-J.
- Hamdan, H. A., Economou, N., Kritikakis, G., Andronikidis, N., Manoutsoglou, E., Vafidis, A., & Apostolidou, G. (2012). 2D and 3D imaging of the metamorphic carbonates at Omalos plateau/polje, Crete, Greece by employing independent and joint inversion on resistivity and seismic data. International Journal of Speleology, 41(2), 7.
- Helene, L. P. I., Moreira, C. A., & Bovi, R. C. (2020). Identification of leachate infiltration and its flow pathway in landfill by means of electrical resistivity tomography (ERT). Environmental Monitoring and Assessment, 192, 1-10.
- Hasan, M., Shang, Y., Meng, H., Shao, P., & Yi, X. (2021). Application of electrical resistivity tomography (ERT) for rock mass quality evaluation. Scientific reports, 11(1), 23683.
- Kazakis, N., Pavlou, A., Vargemezis, G., Voudouris, K. S., Soulios, G., Pliakas, F., & Tsokas, G. (2016). Seawater intrusion mapping using electrical resistivity tomography and hydrochemical data. An application in the coastal area of eastern Thermaikos Gulf, Greece. Science of the Total Environment, 543, 373-387.
- Kidanu, S., Varnavina, A., Anderson, N., & Torgashov, E. (2020). Pseudo-3D electrical resistivity tomography imaging of subsurface structure of a sinkhole—A case study in Greene County, Missouri. AIMS Geosciences, 6(1), 54-70.
- Loke, M. H. (2018). Rapid 2D Resistivity & IP Inversion using the least-squares method. Geotomo Software. Manual.
- Loke, M.H., Chambers, J.E., Rucker, D.F., Kuras, O., & Wilkinson, P.B. (2013) Recent developments in the direct-current geoelectrical imaging method. Journal of applied geophysics 95, 135-156. https://doi.org/10.1016/j.jappgeo.2013.02.017.
- Loke, M. H. (1999). Electrical imaging surveys for environmental and engineering studies: A practical guide to 2D and 3D survey.
- Lopes, M., Avillez, G., Costa, C. N., & Almeida, J. A. (2006). Groundwater contamination plume monitoring in sealed waste dumps. Engineering geology, 85(1-2), 62-66.
- Mandal, A., Basantaray, A. K., Chandroth, A., & Mishra, U. (2019). Integrated geophysical investigation to map shallow surface alteration/fracture zones of Atri and Tarabalo hot springs, Odisha, India. Geothermics, 77, 24-33.
- Mastrocicco, M., Vignoli, G., Colombani, N., & Zeid, N. A. (2010). Surface electrical resistivity tomography and hydrogeological characterization to constrain groundwater flow modeling in an agricultural field site near Ferrara (Italy). Environmental Earth Sciences, 61, 311-322.
- Matern, K., Weigand, H., Singh, A., & Mansfeldt, T. (2017). Environmental status of groundwater affected by chromite ore processing residue (COPR) dumpsites during pre-monsoon and monsoon seasons. Environmental Science and Pollution Research, 24, 3582-3592.
- Metwaly, M., & AlFouzan, F. (2013). Application of 2-D geoelectrical resistivity tomography for subsurface cavity detection in the eastern part of Saudi Arabia. Geoscience Frontiers, 4(4), 469-476.
- Mishra, U., Chandroth, A., Basantaray, A. K., Chel, S., & Mandal, A. (2019). Assessing chromite ore processing residue (COPR) waste dump site using electrical resistivity tomography (ERT): a case study from Umaran, Kanpur, India. Environmental monitoring and assessment, 191, 1-14.
- Mishra, U., Mohapatra, A. K., Mandal, A., & Singh, A. (2023). Identification of potential artificial groundwater recharge sites in an alluvial setting: A coupled electrical resistivity tomography and sediment characterization study. Groundwater for Sustainable Development, 20, 100875.
- Moreira, C. A., Carrara, A., Helene, L. P. I., Hansen, M. A. F., Malagutti Filho, W., & Dourado, J. C. (2017). Electrical resistivity tomography (ERT) applied in the detection of inorganic contaminants in suspended aquifer in Leme city (Brazil). Brazilian Journal of Geophysics, 35(3), 213-225.
- Muhammad, F., Samgyu, P., Young, S. S., Ho Kim, J., Mohammad, T., & Adepelumi, A. A. (2012). Subsurface cavity detection in a karst environment using electrical resistivity (er): a case study from yongweol-ri, South Korea. Earth Sciences Research Journal, 16(1), 75-82.
- Olofsson, B., Jernberg, H., & Rosenqvist, A. (2006). Tracing leachates at waste sites using geophysical and geochemical modelling. Environmental Geology, 49, 720-732.
- Osinowo, O. O., Falufosi, M. O., & Omiyale, E. O. (2018). Integrated electromagnetic (EM) and Electrical Resistivity Tomography (ERT) geophysical studies of environmental impact of Awotan dumpsite in Ibadan, southwestern Nigeria. Journal of African Earth Sciences, 140, 42-51.
- Panda, K. P., Upadhyay, A., Jha, M. K., & Sharma, S. P. (2020). Mapping of laterite zones using 2D electrical resistivity tomography survey in parts of Paschim Medinipur, West Bengal, India: An approach for artificial groundwater recharge. Journal of Earth System Science, 129, 1-11.
- Papadopoulos, N. G., Tsourlos, P., Tsokas, G. N., & Sarris, A. (2006). Two‐dimensional and three‐dimensional resistivity imaging in archaeological site investigation. Archaeological Prospection, 13(3), 163-181.
- Perrone, A., Lapenna, V., & Piscitelli, S. (2014). Electrical resistivity tomography technique for landslide investigation: A review. Earth-Science Reviews, 135, 65-82.
- Rosqvist, H., Leroux, V., Dahlin, T., Svensson, M., Lindsjö, M., Månsson, C. H., & Johansson, S. (2011). Mapping landfill gas migration using resistivity monitoring. In Proceedings of the Institution of Civil Engineers-Waste and Resource Management (Vol. 164, No. 1, pp. 3-15). ICE Publishing.
- Rucker, D. F., Schindler, A., Levitt, M. T., & Glaser, D. R. (2009). Three-dimensional electrical resistivity imaging of a gold heap. Hydrometallurgy, 98(3-4), 267-275.
- Srivastava, A., Singh, S., Prakash, R., & Srivastava, S. K. (2013). Impact of tanneries on ground water quality in kanchandpur area, Kanpur dehat district, UP. Journal Ins. Pub. Health Engineers, 2, 19-26.
- Ullah, F., Su, L. J., Kazantseva, E. S., & Alam, M. (2023). Investigating the location of zone prone to reactivation due to water saturation on landslide debris using electrical resistivity tomography. Bulletin of Engineering Geology and the Environment, 82(5), 1-13.
- Vanneste, K., Verbeeck, K., & Petermans, T. (2008). Pseudo-3D imaging of a low-slip-rate, active normal fault using shallow geophysical methods: the Geleen fault in the Belgian Maas River valley. Geophysics, 73(1), B1-B9.
No competing interests reported.