• Geophysics and Geophysical Exploration
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• v.9 no.2
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• pp.155-162
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• 2006

An abnormal seepage flow, which is mainly caused by the piping, is one of the major reasons for embankment dam failure. A leakage detection is therefore a vital part of an embankment dam's monitoring. Resistivity method, which is an efficient tool to detect leakage zones, has been used all over the world for an embankment dam's monitoring. Although the resistivity method gives us very useful information about the leakage problem, there is no more quantitative interpretation than the low resistivity zones in the 2-dimensional resistivity section are regraded simply as the anomalous seepage zones. Recently, resistivity monitoring technique is applied for the detection of leakage zones. However, its interpretation still remains in the stage of presenting the resistivity ratio itself. An increased seepage flow increases a porosity and an increasing porosity decreases the dam's stability. Therefore, the porosity is one of the major factors for an embankment dam's stability. Based on Archie's experimental formula, we try to evaluate a porosity distribution from the resistivity data which is obtained on the dam's crest. We also attempt to represent a procedure to evaluate a safety index of the embankment dam from the resistivity monitoring data.

• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.57-63
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• 1998

The electrical resistivity method, one of old and widely used geophysical prospecting methods, has extended its scope to civil & environmental engineering areas. The electrical resistivity imaging technique was performed at the waste disposal site located in Junju to verify the applicability to the environmental engineering area. The dipole-dipole array, with the dipole spacing of 10 m, was applied along eight survey lines. The field data were obtained under the control of automatic acquisition softwares and topographic effects were corrected during processing stage. The processed resistivity images show that very low resistivity develops inside the disposal site and the distribution of low resistivity is exactly in accord with the boundary of the site except the river side. The depth of low resistivity zones is deeper toward the river side, which is interpreted that there is a high possibility for contaminants to be scattered to the river. From resistivity images, it was feasible to deduce the depth of waste disposal as well as the horizontal/vertical distribution of the contaminated zone, which proved the applicability of the electrical resistivity imaging technique to the environmental engineering area.

• Geophysics and Geophysical Exploration
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• v.21 no.2
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• pp.103-111
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• 2018

The recent research aim of seismic trace interpolation is to effectively interpolate the data with spatial aliasing. Among various interpolation methods, the Matching Pursuit interpolation, that finds the proper combination of basis functions which can best recover traces, has been developed. However, this method cannot interpolate aliased data. Thus, the multi-component Matching Pursuit interpolation and moveout correction method have been proposed for interpolation of spatially aliased data. It is difficult to apply the multi-component Matching Pursuit interpolation to interpolating the OBC (Ocean Bottom Cable) data which is the multi-component data obtained at the ocean bottom because the isolation of P wave component is required in advance. Thus, in this study, we dealt with an effective single-component matching Pursuit interpolation method in OBC data where P-wave and S-wave are mixed and spatial aliasing is present. To do this, we proposed the Ricker wavelet based single-component Matching Pursuit interpolation workflow with moveoutcorrection and systematically investigated its effectiveness. In this workflow, the spatial aliasing problem is solved by applying constant value moveout correction to the data before the interpolation is performed. After finishing the interpolation, the inverse moveout correction is applied to the interpolated data using the same constant velocity. Through the application of our workflow to the synthetic OBC seismic data, we verified the effectiveness of the proposed workflow. In addition, we showed that the interpolation of field OBC data with severe spatial aliasing was successfully performed using our workflow.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Geophysics and Geophysical Exploration
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• v.11 no.4
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• pp.335-342
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• 2008

Electrical resistivity and seismic refraction surveys were conducted to investigate geologic structures and geotechnical characteristics of the subsurface, along with rock physical property measurements in Dokdo island. The survey results in Seodo island show that the fault adjacent to the fisherman's shelter is a normal fault and extended towards the NW direction, and that Bedded Lapilli Tuff in the downstream was more severely influenced by weathering and erosion than Trachy Andesite II in the upstream of the survey area. In Dongdo island, Trachy Andesite III and Scoria Bedded Lapilli Tuff were severely weathered and eroded, considered as weathered to soft rock formations, and their weathered zone becomes thicker towards the antiaircraft facility in the NE direction of the survey area. The study results also illustrate that Trachyte and Trachy Andesite are hardest, Massive Tuff Breccia is next, and Stratified Ash Tuff is the most soft rock in Dokdo island.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.171-184
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• 2023

Machine learning (ML)-based cavity detection using a large amount of survey data obtained from vehicle-mounted ground penetrating radar (GPR) has been actively studied to identify underground cavities. However, only simple image processing techniques have been used for preprocessing the ML input, and many conventional seismic and GPR data processing techniques, which have been used for decades, have not been fully exploited. In this study, based on the idea that a cavity can be identified using diffraction, we applied ML-based diffraction separation to GPR data to increase the accuracy of cavity detection using the YOLO v5 model. The original ML-based seismic diffraction separation technique was modified, and the separated diffraction image was used as the input to train the cavity detection model. The performance of the proposed method was verified using public GPR data released by the Seoul Metropolitan Government. Underground cavities and objects were more accurately detected using separated diffraction images. In the future, the proposed method can be useful in various fields in which GPR surveys are used.

• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.138-149
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• 2023

In geoscience and engineering the geological characteristics of sediment strata is crucial and possible if reliable borehole logging and seismic data are available. To investigate the characteristics of the shallow strata in the Korea Strait, laboratory sonic logs were obtained from deep borehole data and seismic section. In this study, we integrated and analyzed the sonic log data obtained from the drilling core (down to a depth of 200 m below the seabed) and multichannel seismic section. The correlation value was increased from 15% to 45% through time-depth conversion. An initial model of P-wave impedance was set, and the results were compared by performing model-based, band-limited, and sparse-spike inversions. The derived P-impedance distributions exhibited differences between sediment-dominant and unconsolidated layers. The P-impedance inversion process can be used as a framework for an integrated analysis of additional core logs and seismic data in the future. Furthermore, the derived P-impedance can be used to detect shallow gas-saturated regions or faults in the shallow sediment. As domestic deep drilling is being performed continuously for identifying the characteristics of carbon dioxide storage candidates and evaluating resources, the applicability of the integrated inversion will increase in the future.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Journal of the Korean Geotechnical Society
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• v.19 no.5
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• pp.319-326
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• 2003

In site investigation for tunnel designs, nowadays, geophysical exploration such as seismic exploration and electric resistivity exploration as well as drilling logging is frequently carried out. A method which can systematically make the utmost use of all available data obtained from investigation, therefore, is strongly required for the optimal evaluation of ground conditions in terms of rock mass class, etc. Many researchers have proposed using qualitative data to cope with the lack of quantitative data. In this study, an evaluation technique of rock mass classes in undrilled region was proposed based upon multiple indicator kriging method which is a geostatistical technique. It was shown that two types of data with different degree of uncertainty, for example, drilling logging data and geophysical exploration data, could be simultaneously utilized in evaluating rock mass classes for a real tunnel design.

• Journal of the Korean Geophysical Society
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• v.6 no.1
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• pp.13-23
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• 2003

Dipole-dipole resistivity and ground penetrating radar(GPR) surveys were performed on an abandoned landfill site filled with asbestos containing material. The main purpose of the study was to estimate spatial extension and volume of the landfill for evaluting the cost for developing appropriate remedial alternatives. Assuming that the bedrock is within 10 m depth, dipole spacings of 2, 2.5 and 5m were set for six survey lines for resistivity measurements. For More detailed information, GPR suvey using 225 Mhz antenna was carried out for twelve survey lines for the shallower information. DC resistivity structures showed few tens ~ hundreds ohm-m for the landfill or alluvial laver, and 1,000~ 5,000 ohm-m for the bedrock. The depth to bedrock is found out to be approximately 5m. GPR survey results represented very clear reflection and/or diffraction events from the boundaries as well as from the blocky construction wastes. With high-resolution GPR survey, depth of the bedrock was resolved up to 2m, which in turn, could be a good indicator for estimating the volume of the landfill. Those depths of bedrock were confirmed by backhoe excavation data for 13 sites. The total area and volume of the landfill were to be approximately 3,953 .$m^2$ and 4,033 $m^3$, respectively.