• 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.2 no.4
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• pp.184-190
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• 1999

A study of gas hydrate is a worldwide popular interesting subject as a potential energy source. A seismic survey for gas hydrate have performed over the East sea by the KIGAM since 1997. General indicators of natural submarine gas hydrates in seismic data is commonly inferred from the BSR (Bottom Simulating Reflection) that occurred parallel to the see floor, amplitude decrease at the top of the BSR, amplitude Blanking at the bottom of the BSR, decrease of the interval velocity, and the reflection phase reversal at the BSR. So the seismic data processing for detecting gas hydrates indicators is required the true amplitude recovery processing, a accurate velocity analysis and the AVO (Amplitude Variation with Offset) analysis. In this paper, we had processed the field data to detect the gas hydrate indicators, which had been acquired over the East sea in 1998. Applied processing modules are spherical divergence, band pass filtering, CDP sorting and accurate velocity analysis. The AVO analysis was excluded, since this field data had too short offset to apply the AVO analysis. The accurate velocity analysis was performed by XVA (X-window based Velocity Analysis). This is the method which calculate the velocity spectrum by iterative and interactive. With XVA, we could determine accurate stacking velocity. Geobit 2.9.5 developed by the KIGAM was used for processing data. Processing results say that the BSR occurred parallel to the sea floor were shown at $367\~477m$ depths (two way travel time about 1800 ms) from the sea floor through shot point 1650-1900, the interval velocity decrease around BSR and the reflection phase reversal corresponding to the reflection at the sea floor.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.723-726
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• 2011

전 세계적으로 사회기반시설 및 신재생 에너지 생산을 위한 공간부족 문제를 해결하기 위하여 초대형 부유구조물에 대한 관심이 증가하고 있다. 다양한 사용분야에 대한 안정성 확보를 위해 파랑하중에 의한 동적 응답과 부유체의 강성 대비 길이 효과로 인해 발생할 수 있는 특이 거동에 대한 분석이 필요하다. 초대형 부유구조물의 경우 중앙부에 비해 양끝 단에서 과도한 응답이 발생하며, 이를 해결하기 위해 입사파가 들어오는 초대형 부유구조물의 단부에 부착되는 다양한 감요장치(anti-motion device)가 제안되어지고 있다. 초대형 부유구조물에 감요장치가 적용될 경우 입사파에 의한 동적 파압을 감소시켜 구조물의 전반적인 응답을 줄여줄 수는 있지만 감요장치의 질량이 클 경우 오히려 끝단의 응답을 증폭시킬 수 있다. 본 논문에서는 양단 자유 경계조건의 탄성지지된 보를 이용하여 초대형 부유구조물의 길이와 끝단에 부착된 감요장치의 질량으로 인한 영향을 분석하였다.

• 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.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.14-25
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• 2022

The passive surface wave method measures seismic signals from ambient noises or vibrations of natural phenomena without using an artificial source. Since passive sources are usually in lower frequencies than artificial ones being able to ensure the information on deeper geological structures, the passive surface wave method can investigate deeper geological structures. In the passive method, frequency dispersion curves are obtained after data acquisition, and the dispersion curves are analyzed by assuming 1D-layered earth, which is like the method of active surface wave survey. However, when computing dispersion curves, the passive method first obtains and analyzes coherence curves of received signals from a set of receivers based on spatial autocorrelation. In this review, we explain how passive surface wave methods measure signals, and make data processing and interpretation, before analyzing field application cases.

• The Journal of Engineering Geology
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• v.22 no.4
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• pp.459-466
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• 2012

A difficulty encountered in engineering seismic mapping is that reflection events from shallow discontinuities are commonly overlapped with coherent noise such as air wave, direct waves, head waves, and high-amplitude surface waves. Here, the radial trace transform, a simple geometric re-mapping of a trace gather (x-t domain) to another trace gather (v-t domain), is applied to investigate the rejection effect of coherent linear noises. Two different types of data sets were selected as a representative database: good-quality data for intermediate sounding (hundreds of meters) in a sedimentary basin and very noisy data for shallow (${\leq}50m$) mapping of the weathered zone and bedrock surface. Results obtained with cascaded application of the radial transform and low-cut filtering proved to be as good as, or better than, those produced using f-k filtering, and were especially effective for air wave and direct wave. This simple transform enables better understanding of the characteristics of various types of noise in the RT domain, and can be generally applied to overcoming diffractions and back-scatterings caused by joints, fractures, and faults commonly that are encountered in geotechnical problems.

• Journal of the Korea Concrete Institute
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• v.27 no.4
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• pp.363-375
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• 2015

This research investigated the effects of diameter and material of energy frame on the impact velocity or strain rate of Strain Energy Frame Impact Machine (SEFIM). The impact speed of SEFIM have been clearly affected by changing the diameter and material of the energy frame. The reduced diameter of the energy frame clearly increased the impact velocity owing to the higher strain at the moment of coupler breakage. And, titanium alloy energy frame produced the fastest speed of impact among three materials including steel, aluminum and titanium alloys because titanium alloy has faster wave velocity than steel. But, aluminium energy frame was broken during impact tests. In addition, the tensile stress versus strain response of high performance fiber reinforced cementitious composites at higher and wider strain rates between 10 and 72 /sec was successfully obtained by using four different energy frames.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.

• Geophysics and Geophysical Exploration
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• v.26 no.1
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• pp.8-17
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• 2023

Various sources, such as wind, waves, ships, and gas leaks from the seafloor, forms bubbles in the ocean. Underwater bubbles cause signal scattering, considerably affecting acoustic measurements. This characteristic of bubbles is used to block underwater noise by attenuating the intensity of the propagated signal. Recently, researchers have been studying the large-scale release of methane gas as bubble plumes from the seabed. Understanding the physical properties and distribution of bubble plumes is crucial for studying the relation between leaked methane gas and climate change. Therefore, a water tank experiment was conducted to estimate the distribution of bubble plumes using seismic imaging techniques and acoustic signals obtained from artificially generated bubbles using a bubble generator. Reverse time migration was applied to image the bubble plumes while the acquired acoustic envelope signal was used to effectively estimate bubble distribution. Imaging results were compared with optical camera images to verify the estimated bubble distribution. The water tank experiment confirmed that the proposed system could successfully image the distribution of bubble plumes using reverse time migration and the envelope signal. The experiment showed that the scattering signal of artificial bubble plumes can be used for seismic imaging.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.807-814
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• 2004

Land seismic reflection survey has been increasingly demanded in various civil engineering works because of its own ability to delineate layers, water table, to detect cavities or fracture zones, to estimate seismic velocities of each layer. However, our shallow subsurface structures are very complex. The relatively thin layer(mostly soil) to the wavelength directly followed by a basic rock with high impedance used to generate complicated surface waves, kind of channel waves with high amplitude that is dominate in entire seismograms and hence the useful reflection events will be almost hopelessly immersed in the undesired surface waves. Thus, it would seem that the use of traditional seismic survey could not be likely to provide in itself a satisfactory information about our exploration targets. This paper hence introduces an efficient measuring strategy illustrating a properly controlled arrangement of the vertical single force sources commonly used, yielding a very sharply elongated form of P-energy with a minimum of S radiation energy, what we call, P-beam source. Abundant experiments of physical modeling showed that in that way the surface waves could be enormously reduced and the reflection events would be additive and thus reinforced. Examples of field data are also illustrated. The contribution of P-beam source will be great in civil engineering area as well as in general geological exploration area.