• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.252-258
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• 2007

We have studied the feasibility of geostatistics approach to enhancing analysis of sparsely obtained seismic data by combining with satellite gravity data. The shallow depth and numerous fishing nets in The Yellow Sea, west of Korea, makes it difficult to do seismic surveys in this area. Therefore, we have attempted to use geostatistics to integrate the seismic data along with gravity data. To evaluate the feasibility of this approach, we have extracted only a few seismic profile data from previous surveys in the Yellow Sea and performed integrated analysis combining with the results from gravity data under the assumption that seismic velocity and density have a high physical correlation. First, we analyzed the correlation between extracted seismic profiles and depths obtained from gravity inversion. Next, we transferred the gravity depth to travel time using non-linear indicator transform and analyze residual values by kriging with varying local means. Finally, the reconstructed time structure map was compared with the original seismic section given in the previous study. Our geostatistical approach demonstrates relatively satisfactory results and especially, in the boundary area where seismic lines are sparse, gives us more in-depth information than previously available.

• Journal of the Korean earth science society
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• v.28 no.1
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• pp.105-111
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• 2007

Seismic and georadar prospecting methods have been used to detect deep seated small tunnel in Korea. The tunnel position interpretation of seismic method has been performed mainly by wave travel time and amplitude. But it was very unstable to interpret the exact tunnel position because of short interval of two measuring boreholes and picking mistake of first arrivals. To solve this problem, this study applied travel-time and amplitude data simulation methods to detect tunnel position.

• Geophysics and Geophysical Exploration
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• v.5 no.3
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• pp.145-149
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• 2002

The static correction, which is classified into refraction based static correction and reflection based residual static correction, removes distortions caused by irregularities of thickness or velocity in near-surface. Generally, refraction statics is a time consuming process because of high dependence on the interpreter's analysis. Therefore, for huge 3D seismic data, automatic static correction which minimizes the interpreter's analysis is required. In this research, we introduce an efficient method of refraction static correction for land 3D seismic survey.

• Journal of Korean Tunnelling and Underground Space Association
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• v.6 no.3
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• pp.247-258
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• 2004

Two 3-dimensional data processing techniques to predict the fractured zone ahead of a tunnel face by the tunnel seismic survey were proposed so that the geometric formation of the fractured zone could be estimated. The first 3-dimensional data processing technique was developed based on the principle of ellipsoid, The input data needed for the 3D migration can be obtained from the 2-dimensional tunnel seismic prediction (TSP) test where the TSP test should be performed in each sidewall of a tunnel. The second 3-dimensional migration technique that was developed based on the concept of wave travel plane was proposed. This technique can be applied when the TSP is operated with sources in one sidewall of a tunnel while the receivers are installed in both sidewalls. New migration technique was applied to an in-situ tunnelling site. The 3-dimensional migration was performed using measured TSP data and its results were compared with the geological investigation results that were monitored during tunnel construction. This comparison revealed that the proposed migration technique could reconstruct the discontinuity planes reasonably well.

• Geotechnical Engineering
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• v.20 no.4
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• pp.38-49
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• 2004

해상 반사법탐사는 해저 지반의 지층구조를 파악하는 기술로서 해저지층에 부존하는 가스나 골재 등 해저자원 탐사와 해저의 저장시설 건설, 파이프라인 설치 등 다양한 해양 토목공사를 위한 지반조사에 사용된다. 해상 반사법탐사의 기본적인 원리는 해수면 근처에서 인공적으로 음파를 발생시켜 해저면 하부의 지층으로 침투시키면 서로 다른 물성을 갖는 지층의 경계면에서 일부 음파는 반사되는데, 이 반사파를 수신하는 것이다. 탐사과정에서 얻어진 트레이스에는 반사파 이외에도 직접파, 다중반사파와 같은 잡음이 섞여있는데 자료처리를 통해 탄성파 단면도를 작성하고, 이를 해석하여 해저지반의 지질학적 구조를 파악하는 것이 해상 반사법탐사의 목적이다.

• Economic and Environmental Geology
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• v.57 no.1
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• pp.51-71
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• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.192-207
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• 2020

We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.

• The Journal of Engineering Geology
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• v.13 no.1
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• pp.129-137
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• 2003

We interpreted the seismic signal characteristics from crosswell seismic tomography in the underground cavity like abandoned mines. The first arrival time delay and amplitude attenuation showed clearly at the low velocity zone of cavity and fracture. Also ray density decreased by detour of raypath. As a result of the amplitude spectrum analysis of fresh rock and low velocity zone, there were no noticeable differences of the amplitude up to about 1000Hz frequency, but indicated that the one passed around cavity decreased about 7dB at 2000Hz, and 20dB at 3000Hz. It was possible to compare the signal characteristics between two media by extracting the signal data from the fresh rock zone and the underground cavity through the seismic crosswell tomography.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.91-107
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• 2024

Single-channel seismic exploration has proven effective in delineating subsurface geological structures using small-scale survey systems. The seismic data acquired through zero- or near-offset methods directly capture subsurface features along the vertical axis, facilitating the construction of corresponding seismic sections. However, substantial noise in single-channel seismic data hampers precise interpretation because of the low signal-to-noise ratio. This study introduces a novel approach that integrate noise reduction and signal enhancement via matrix rank optimization to address this issue. Unlike conventional rank-reduction methods, which retain selected singular values to mitigate random noise, our method optimizes the entire singular value spectrum, thus effectively tackling both random and erratic noises commonly found in environments with low signal-to-noise ratio. Additionally, to enhance the horizontal continuity of seismic events and mitigate signal loss during noise reduction, we introduced an adaptive weighting factor computed from the eigenimage of the seismic section. To access the robustness of the proposed method, we conducted numerical experiments using single-channel Sparker seismic data from the Chukchi Plateau in the Arctic Ocean. The results demonstrated that the seismic sections had significantly improved signal-to-noise ratios and minimal signal loss. These advancements hold promise for enhancing single-channel and high-resolution seismic surveys and aiding in the identification of marine development and submarine geological hazards in domestic coastal areas.

• Geophysics and Geophysical Exploration
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• v.23 no.2
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• pp.97-114
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• 2020

Recently, many studies have been actively conducted on the application of machine learning in all branches of science and engineering. Studies applying machine learning are also rapidly increasing in all sectors of seismic exploration, including interpretation, processing, and acquisition. Among them, fault detection is a critical technology in seismic interpretation and also the most suitable area for applying machine learning. In this study, we introduced various machine learning techniques, described techniques suitable for fault detection, and discussed the reasons for their suitability. We collected papers published in renowned international journals and abstracts presented at international conferences, summarized the current status of the research by year and field, and intensively analyzed studies on fault detection using machine learning. Based on the type of input data and machine learning model, fault detection techniques were divided into seismic attribute-, image-, and raw data-based technologies; their pros and cons were also discussed.