• The Journal of Engineering Geology
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• v.16 no.4 s.50
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• pp.327-335
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• 2006

Three processing strategies of seismic refraction data are tested in terms of velocity and depth profiles or structures for mapping of geological discontinuities: GRM(generalized reciprocal method), GLI(generalized linear inversion), Tomography. The test data used in this study are the shot gathers reconstructed by numerical modeling for the structures of 3 planar layers(horizontal, inclined), the buried vertical fracture zones, and vertical fault zones. Tomography is shown to be very efficient for mapping of more complicated tone such as vertical fault and buried fracture zones, whereas GRM and GLI can be useful for horizontal and/or inclined layers, probably on their bases of analysis of first arrivals in travel time curves.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.168-177
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• 2020

Seabed multiple reflections (seabed multiples) are the main cause of misinterpretations of primary reflections in both shot gathers and stack sections. Accordingly, seabed multiples need to be suppressed throughout data processing. Conventional model-driven methods, such as prediction-error deconvolution, Radon filtering, and data-driven methods, such as the surface-related multiple elimination technique, have been used to attenuate multiple reflections. However, the vast majority of processing workflows require time-consuming steps when testing and selecting the processing parameters in addition to computational power and skilled data-processing techniques. To attenuate seabed multiples in seismic reflection data, input gathers with seabed multiples and label gathers without seabed multiples were generated via numerical modeling using the Marmousi2 velocity structure. The training data consisted of normal-moveout-corrected common midpoint gathers fed into a U-Net neural network. The well-trained model was found to effectively attenuate the seabed multiples according to the image similarity between the prediction result and the target data, and demonstrated good applicability to field data.

• Geophysics and Geophysical Exploration
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• v.13 no.4
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• pp.307-314
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• 2010

Although conventional seismic data processing is based on the assumption that the media are isotropic, the subsurface is often anisotropy in shale formation or carbonate with cracks and fractures. This paper presents the anisotropic parameter and seismic modeling in transversely isotropic media with a vertical symmetry axis using seismic physical modeling. The experiment was successfully carried out with VTI media, laminated bakelite material, using contact transducer of p and s-wave transmission. The variation of velocities with angle of incidence was clearly shown in anisotropic material. Comparing these velocities with the calculated phase velocities, the (P) and (S)-wave velocity observed in anisotropic material was a very good agreement with the calculated values. Anisotropic parameter ${\varepsilon}$, ${\delta}$, ${\gamma}$ was estimated by using Lame's constant calculated from the observed velocity. For the purpose of testing (S)-wave polarization, a birefringence experiment was carried out. The higher velocity was associated with the polarization parallel to the fracture, and the lower velocity was associated with the polarization perpendicular to the fracture.

• Economic and Environmental Geology
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• v.39 no.6 s.181
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• pp.711-717
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• 2006

In order to study gas hydrate, potential future energy resources, Korea Institute of Geoscience and Mineral Resources has conducted seismic reflection survey in the East Sea since 1997. one of evidence for presence of gas hydrate in seismic reflection data is a bottom simulating reflector (BSR). The BSR occurs at the interface between overlaying higher velocity, hydrate-bearing sediment and underlying lower velocity, free gas-bearing sediment. That is often characterized by large reflection coefficient and reflection polarity reverse to that of seafloor reflection. In order to apply depth migration to seismic reflection data. we need high performance computers and a parallelizing technique because of huge data volume and computation. Phase shift plus interpolation (PSPI) is a useful method for migration due to less computing time and computational efficiency. PSPI is intrinsically parallelizing characteristic in the frequency domain. We conducted conventional data processing for the gas hydrate data of the Ease Sea and then applied prestack depth migration using message-passing-interface PSPI (MPI_PSPI) that was parallelized by MPI local-area-multi-computer (MPI_LAM). Velocity model was made using the stack velocities after we had picked horizons on the stack image with in-house processing tool, Geobit. We could find the BSRs on the migrated stack section were about at SP 3555-4162 and two way travel time around 2,950 ms in time domain. In depth domain such BSRs appear at 6-17 km distance and 2.1 km depth from the seafloor. Since energy concentrated subsurface was well imaged we have to choose acquisition parameters suited for transmitting seismic energy to target area.

• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.131-135
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• 2006

A seismic waveform inversion for prestack seismic data based on the Gauss-Newton method is presented. The Gauss-Newton method for seismic waveform inversion was proposed in the 80s but has rarely been studied. Extensive computational and memory requirements have been principal difficulties. To overcome this, we used different sizes of grids in the inversion stage from those of grids in the wave propagation simulation, temporal windowing of the simulation and approximation of virtual sources for calculating partial derivatives, and implemented this algorithm on parallel supercomputers. We show that the Gauss-Newton method has high resolving power and convergence rate, and demonstrate potential applications to real seismic data.

• 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.4
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• pp.229-237
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• 2023

The use of artificial sources such as explosives and mechanical vibrations for seismic exploration in urban areas poses challenges, as the vibrations and noise generated can lead to complaints. As an alternative to artificial sources, the surface waves generated by traffic noise can be used to investigate the subsurface properties of urban areas. However, traffic noise takes the form of plane waves moving continuously at a constant speed. To apply existing surface wave processing/inversion techniques to traffic noise, the recorded data need to be transformed into a virtual shot gather format using seismic interferometry. In this study, various seismic interferometry methods were applied to traffic noise data, and the optimal method was derived by comparing the results in the Radon and F-K domains. Additionally, the data acquired using various receiver arrays were processed using seismic interferometry, and the results were compared and analyzed to determine the most optimal receiver array direction for exploration.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.166-171
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• 2001

A digital seismic data acquisition and processing system using a PC has been developed in order to replace the analog data acquisition system of shallow marine seismic survey. An A/D converter that has 12bits of resolution and 225KHz of conversion rate was ued to acquire data, and a data acquisition software was developed as a Windows program which provides convenience of use. Raw data acquired at field has been saved to the hard-disk simultaneously. The signal to noise ratio, vertical and horizontal resolution could be improved by a digital data processing of the raw data. The digital processing of the raw data includss gain recovery, filtering, deconvolution, and muting. With the prediction deconvolution algorithm multiple reflections appearing on the shallow marine seismic section could be removed successfully.

• Geophysics and Geophysical Exploration
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• v.4 no.4
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• pp.115-123
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• 2001

The bright spot is an indicator for natural gas on seismic stack sections, but it is also shown on layers where the acoustic impedance contrast is large. In order to distinguish sharply between gas and impedance contrast we need additional detailed data processing such as velocity analysis, AVO analysis and seismic complex analysis including measures of seismic amplitude, frequency, and phase. In this study, we performed detailed velocity analysis, complex analysis and DHI (Direct Hydrocarbon Indicator) analysis which is the result of amplitude variation according to the incident angles. The seismic complex analysis gives us the geological information which depends on geophysical properties at the interest layer. For the complex analysis, we computed several seismic attributes such as the instantaneous amplitude, the first and the second derivatives of the instantaneous amplitude, the instantaneous phase, the instantaneous frequency and weighted average instantaneous frequency. Then we applied these analysis techniques to a seismic data of Korea offshore which had been logged. From the result of this data analysis, it could be said that high possibility area for gas layer detection has amplitude anomalies in the instantaneous amplitude, the instantaneous frequency and the DHI section resulting from the AVO analysis. If there are not any other anomalies in detailed data processing, it will have low possibility for gas layer detection.

• 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.