• Journal of the Korean Geotechnical Society
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• v.23 no.4
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• pp.33-46
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• 2007

HWAW (Harmonic Wavelet Analysis of Wave) method, which is non-destructive method using body and surface waves, has the advantages of obtaining 2D subsurface imaging because it uses a short receiver spacing to obtain the $V_s$ profile of whole depth. Even though the reliability of HWAW method has already been verified by using the numerical simulation in the various layered models, it is very difficult to evaluate the reliability of HWAW in the field because the exact $V_s$ values of the experimental site are unknown. In this study, a model testing site where the material properties and layer information could be controlled was constructed to verify the reliability of HWAW method. The detailed geometry of the testing site was strictly measured by surveying, and 140 vertical and horizontal geophones were established at the boundary of each layer to evaluate the dynamic material properties. Using the interval travel times between the upper and lower geophones, the body wave velocities of each layer were 2 dimensionally obtained as reference data, and comparative study using HWAW method was performed. By comparing 2D Vs profile obtained by HWAW method to the reference data, the reliability of HWAW method was verified.

• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.61-68
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• 2010

A pilot-scale model test was performed to estimate the availability of new material, Fe-loaded zeolite, as the filling material in permeable reactive barrier (PRB) against the contaminated groundwater with both Cd and Cr(VI). Aquifer was simulated by filling up a large scale soil tank with sands, and mobilizing the water flow by the head difference of water level in both ends of the tank. Then, the mixture of concentrated Cd and Cr(VI) solution was injected into the aquifer to form a contaminant plume, and its behavior through Fe-loaded zeolite barrier was monitored. The test results showed that Fe-loaded zeolite barrier successfully treated the contaminant plume containing both Cd and Cr(VI) and that the immobilized contaminants in the barrier were not desorbed or released. The results indicated that the Fe-loaded zeolite could be a promising material in PRBs against the multiple contaminants with different ionic forms like Cr(VI) and Cd.

• Journal of the Korean Geotechnical Society
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• v.23 no.2
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• pp.47-60
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• 2007

Conventional active surface wave measurements performed using a transient or continuous source are often limited in the maximum depth of penetration due to the difficulty of generating low-frequency energy with reasonably portable sources. This limitation may inhibit accurate seismic site response calculations because of the inability to define deeper subsurface structure. By measuring surface wave generated by passive sources including microtremors and cultural noise, it is possible to overcome this problem and develop soil stiffness profiles to much larger depth. Reliability of dispersion estimates from the passive surface wave measurements is critical to present reliable shear wave velocity profiles and can be improved by the measurements and analyses of passive surface waves based on correct understanding of systematic errors included in passive dispersion data. In this study, the systematic errors caused by poor wavenumber resolution and energy leakage into sidelobes in passive tests are mainly explored. Recommendations for reliable passive surface wave measurements and dispersion estimates are presented and illustrated at a site in San Jose, California, U.S.

• Journal of Korean Society on Water Environment
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• v.39 no.2
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• pp.142-152
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• 2023

Infiltration is a process by which precipitation infuses into subsurface soils. The process determines the surface flow and baseflow volume, and it is one of most important hydrological processes regarding nonpoint source pollution management. Therefore, the Ministry of Environment has developed a guideline to determine the impervious area ratio to understand the hydrological process in administrative districts and watersheds. The impervious area ratio can be determined using land use or land cover maps. Three approaches were explored to determine the impervious area ratio in 25 districts in Seoul. The impervious area ratio was determined by employing the Land registration map and Land property data in the first approach, Land property map in the second approach, and Land cover map in the third approach. The ratio ranged from 38.96% to 83.01% in the first approach, 38.98% to 83.02% in the second approach, and 37.62% to 76.63% in the third approach. Although the ranges did not provide any significant differences in the approaches, some districts displayed differences up to 9.48% by the approach. These differences resulted from the fact that the data were land use or land cover, especially in the area of airport, residential complex area, and school sites. In other words, division of the pervious and impervious areas in an individual plot was not allowed in the Land registration map, while it was allowed in the Land cover map. Therefore, it was concluded that there is a need to revise the guideline so that a reasonable impervious area ratio can be determined in the districts.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.157-171
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• 2022

Electrical resistivity tomography (ERT) is a traditional and representative geophysical method for determining the resistivity distributions of surrounding soil and rock volumes. Depth resolution profiles and sensitivity distribution sections of the resistivities with respect to various electrode configurations are calculated and investigated using numerical model data. Shallow vertical resolution decreases in the order of Wenner, Schlumberger, and dipole-dipole arrays. A high investigable depth in homogeneous medium is calculated to be 0.11-0.19 times the active electrode spacing, but is counterbalanced by a low vertical resolution. For the application of ERT depth resolution profiles and sensitivity distributions, we provide subsurface structure models for two types of land-creping failure (planar and curved), subvertical fracture, and weathered layer over felsic and mafic igneous rocks. The dipole-dipole configuration appears to be most effective for mapping land-creeping failure planes (especially for curved planes), whereas the Wenner array gives the best resolution of soil horizons and shallow structures in the weathered zone.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.69-76
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• 2023

Cavity causes settlement and its remediation after an accident results in significant time and economic losses. This study aims to experimentally evaluate the prospect of using infrared camera to detect and measure underground subsidence. Emissivity is necessary to detect the energy emitted from an object and accurately assess temperature using an infrared camera. The emissivity in laboratory tests is fixed to evaluate a reasonable distance between the infrared camera and the object, and temperature values are assessed at various distances. In field experiments, the cavity of the field experiment is simulated using a PVC pipe with a diameter of 5 cm, artificially buried at depths of 5 and 25 cm from the surface. The infrared camera measurements are taken from 4 PM to 3 PM of the next day (a total of 23 h). The analysis included the time-series temperature distribution and the cooling rate index assessment, which represents the temperature change rate per unit of time. The results showed that various temperature trends are observed depending on the location of the subsidence. This study demonstrates that the infrared camera can be used to assess the condition of the subsurface.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.697-707
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• 2008

The grid-based KIneMatic wave STOrm Runoff Model (KIMSTORM) by Kim (1998) predicts the temporal variation and spatial distribution of overland flow, subsurface flow and stream flow in a watershed. The model programmed with C++ language on Unix operating system adopts single flowpath algorithm for water balance simulation of flow at each grid element. In this study, we attempted to improve the model by converting the code into FORTRAN 90 on MS Windows operating system and named as ModKIMSTORM. The improved functions are the addition of GAML (Green-Ampt & Mein-Larson) infiltration model, control of paddy runoff rate by flow depth and Manning's roughness coefficient, addition of baseflow layer, treatment of both spatial and point rainfall data, development of the pre- and post-processor, and development of automatic model evaluation function using five evaluation criteria (Pearson's coefficient of determination, Nash and Sutcliffe model efficiency, the deviation of runoff volume, relative error of the peak runoff rate, and absolute error of the time to peak runoff). The modified model adopts Shell Sort algorithm to enhance the computational performance. Input data formats are accepted as raster and MS Excel, and model outputs viz. soil moisture, discharge, flow depth and velocity are generated as BSQ, ASCII grid, binary grid and raster formats.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.