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Time-domain/transient electromagnetic (TEM) survey measures changes in the electromagnetic (EM) field with respect to time, after an artificially generated electric or magnetic field is discontinued. Therefore, only secondary magnetic fields are measured that are free from the primary magnetic fields. Theoretically, TEM measurements are frequency responses that provide information on the conductivities of shallow to deep sub-surfaces. TEM data, normally stacked, are corrected during data processing. TEM measurement responses for synthetic models can be numerically computed using the finite difference, finite element, and finite volume methods. In this review, initially we have discussed the basic theory of the TEM method and TEM survey systems by classifying them into loop and ground-wire transmitters. Subsequently, we discussed data processing, modelling, and inversion. Finally, we discussed field survey cases from various application fields by classifying them into land, marine, and airborne TEM systems.

Technological innovations within the context of electrical and electromagnetic (EM) surveys have allowed for a rapid, efficient, and easier acquisition of a high quantity of data. Such innovations have been integral in mineral exploration and groundwater surveys. On the other hand, conventional inversion of electrical or EM survey data is computationally time-consuming and expensive. To circumvent the limitations of conventional inversion, the implementation of deep learning (DL) using improved neural networks has garnered substantial attention. In this study, we review various DL methods that can be used as substitutes for traditional inversion methods. Specifically, we investigate cases highlighting the successful implementation of DL to electrical or EM surveys and also comprehensively examine the advantages and disadvantages of such an application of DL.

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To effectively delineate the spatial distribution of oil contaminant plumes, geophysical methods indirectly measure the physical properties of the subsurface and can provide spatial information and images on a large scale, as opposed to traditional direct methods such as borehole drilling, sampling, and chemical analysis, which are time-consuming and costly. However, delineating geophysical responses from non-aqueous phase liquids (NAPLs) contaminated sites is not straightforward due to inconsistent responses from biodegraded oil contaminants. Additionally, the presence of clay materials can complicate the interpretation of geophysical data in NAPL-contaminated sites. In this study, we present a case study of a multi-geophysical investigation, including seismic refraction, ground-penetrating radar (GPR), electrical resistivity tomography (ERT), and complex resistivity (CR), to delineate NAPL contamination in a clay-rich site. To reduce ambiguity in discriminating between oil contaminants and clay layers, we suggest constructing a 3D geological model that incorporates borehole data and geophysical data. Based on the 3D geological model, conductive zones generally correspond to high concentrations of hydrocarbons in the unsaturated zone, but it is difficult to distinguish contaminated areas from saturated soil. The IP response rapidly decreased to close to zero in several expected highly contaminated zones, which differs from the clay soil with high IP values. Finally, we compare the expected contaminated area from geophysical data and soil sampling data and discuss how geophysical interpretation can be improved in NAPL-contaminated sites.

Since landslide can cause huge damages to many facilities, close characterization of slopes is needed for appropriate reinforcements for the unstable ones in order to prevent the damages. Geophysical surveys, which can characterize a large area at a relatively low cost without disturbing slopes, have been widely employed for the assessment of slope stability in other countries. However, only conventional direct investigation methods are mainly used in Korea. In this paper, we analyzed various cases, which evaluated slope stabilities by characterizing slopes using geophysical exploration. First, we introduced changes in geophysical properties due to unstable media of slope like fracture location, fracture connectivity and distribution of groundwater level, and subsequently discussed the applicability of geophysical methods to the detection of the changes; the methods include electrical resistivity survey, seismic survey, self-potential survey, induced polarization survey and ground penetrating radar. Based on this description, we analyzed how geophysical surveys were performed on various slopes.

<p>Induced polarization (IP) methods can be classified into time domain IP (TDIP), complex resistivity (CR), and Spectral IP (SIP) surveys based on measurement method. In field surveys, TDIP measurements are the most widely performed thanks to the easier and less time-consuming acquisition than SIP. In the meantime, SIP measurements are preferred over TDIP for the analysis on dispersion characteristics of CRs of cores in laboratory experiments based on Cole-Cole parameters. Theoretically, the dispersion characteristics should be common in SIP and TDIP measurements if the nature of used sources in both measurements is the same. However, in real situations, ranges of time and frequency are limited due to limitations of equipment resulting in dissimilarities between SIP and TDIP. Despite the dissimilarities, it is attempted to mutual interpretation between TDIP and SIP data sets in recent researches. We analyze spectral dispersions of CRs from laboratory measurements data of SIP to estimate SIP parameters based on the Cole-Cole model. Using the dispersion characteristics, numerical models with IP anomalies are constructed for numerical simulation of not only SIP but also TDIP surveys. Through inversion of resulting SIP and TDIP synthetic data, we estimate Cole-Cole parameters from inverted SIP anomalies and chargeability decay curves of the inverted anomalies from TDIP. Based on the numerical experiments, we make further numerical tests considering field scale mining and contamination surveys.</p><p>This work was supported by the Energy Efficiency & Resources of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20194010201920) and Korea Ministry of Environment as "The SEM projects; 2018002440005"</p><p> </p>

Monitoring leakage of leachate from a landfill is critical in preventing possible contamination into the surrounding area. Among geophysical surveys, time-lapse (TL) electrical resistivity tomography (ERT) has been performed along eleven survey lines at four different time points in a landfill in Korea. The TL data sets were interpreted using an in-house 4D inversion algorithm. Changes in 4D inversion results were analyzed in order to detect leachate-contaminant region. Since a rainy season started during obtaining TL ERT data sets, effects of precipitation on TL ERT data are also analyzed. Changes in electrical resistivity (ER) showed that precipitation increases ER of contaminant zones. As hydrogeochemical data are helpful to interpret ERT inversion, we also classified soil textures from particle size analysis on soil samples obtained from observation wells in the survey site. The information of soil structure as well as the results of 4D inversion offered appropriate interpretation of preferential flow path.