• 한국신재생에너지학회:학술대회논문집
• /
• 2007.06a
• /
• pp.497-500
• /
• 2007

탄성파 코다 파는 두 수진기에서 기록된 탄성파 자료의 상호상관으로부터 두 신호에 대한 순간응답을 구하고 이로부터 지층정보를 구하는데 이용된다. 여기에서는 인공합성 탄성파 자료와 가스 하이드레이트 현장자료에 적용하여 상호상관 모음도와 가상음원 모음도 (virtual source)를 구하고자 하였다. 인공합성자료는 해저면 탄성파 탐사법 (ocean bottom seismic)을 모델로 이용하여 인공합성 탄성파 단면도를 제작하였으며, 탄성파 코다 파를 살펴보기 위해 인공 OBS 자료 중 첫 번째 트레이스를 가상음원으로 정하고 모든 음원 모음도와 상호상관으로 가상응원 단면도를 제작하였다. 현장자료 적용으로는 해저면 기인 고진폭 반사파인 BSR (bottom simulating reflection)을 포함하고 있는 자료를 선정하여 상호상관 단면도와 가상음원 단면도를 제작하였다. 중합단면도상에 나타난 가스 분출지역은 상호상관 단면도에서도 나타났으며, 중합단면도상 BSR부분은 vs 단면도에서 강한 반사파를 보여줌을 알 수 있었다.

• Geophysics and Geophysical Exploration
• /
• v.25 no.2
• /
• pp.71-84
• /
• 2022

When acquiring seismic data, various types of simultaneously recorded seismic noise hinder accurate interpretation. Therefore, it is essential to attenuate this noise during the processing of seismic data and research on seismic noise attenuation. For this purpose, machine learning is extensively used. This study attempts to attenuate noise in prestack seismic data using unsupervised machine learning. Three unsupervised machine learning models, N2NUNET, PATCHUNET, and DDUL, are trained and applied to synthetic and field prestack seismic data to attenuate the noise and leave clean seismic data. The results are qualitatively and quantitatively analyzed and demonstrated that all three unsupervised learning models succeeded in removing seismic noise from both synthetic and field data. Of the three, the N2NUNET model performed the worst, and the PATCHUNET and DDUL models produced almost identical results, although the DDUL model performed slightly better.

• Geophysics and Geophysical Exploration
• /
• v.13 no.2
• /
• pp.159-168
• /
• 2010

Recent trend in petroleum/gas exploration is an application of 3D seismic exploration technique. Unlike the conventional 2D seismic data processing, 3D seismic data processing is considered as the one which requires expensive commercial software systems and high performance computer. This paper propose a practical 3D seismic processing methodology on a personal computer using public domain software such as SU, SEPlib, and SEPlib3D. The applicability of the proposed method has been demonstrated by successful application to a well known realistic 3D synthetic data, SEG/EAGE 3D salt model data.

• The Journal of Engineering Geology
• /
• v.23 no.4
• /
• pp.457-465
• /
• 2013

We integrated and correlated datasets from surface and subsurface geophysics, drilling cores, and engineering geology to identify geological interfaces and characterize the joints and fracture zones within the rock mass. The regional geometry of a geologically weak zone was investigated via a fence projection of electrical resistivity data and a borehole image-processing system. Subsurface discontinuities and intensive fracture zones within the rock mass are delineated by cross-hole seismic tomography and analyses of dip directions in rose diagrams. The dynamic elastic modulus is studied in terms of the P-wave velocity and Poisson's ratio. Subsurface discontinuities, which are conventionally identified using the N value and from core samples, can now be identified from anomalous reflection coefficients (i.e., acoustic impedance contrast) calculated using a pair of well logs, comprising seismic velocity from suspension-PS logging and density from logging. Intensive fracture zones identified in the synthetic seismogram are matched to core loss zones in the drilling core data and to a high concentration of joints in the borehole imaging system. The upper boundaries of fracture zones are correlated to strongly negative amplitude in the synthetic trace, which is constructed by convolution of the optimal Ricker wavelet with a reflection coefficient. The standard deviations of dynamic elastic moduli are higher for fracture zones than for acompact rock mass, due to the wide range of velocities resulting from the large numbers of joints and fractures within the zone.

• Geophysics and Geophysical Exploration
• /
• v.26 no.2
• /
• pp.84-93
• /
• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• Journal of the Korean Geophysical Society
• /
• v.4 no.2
• /
• pp.113-120
• /
• 2001

The discrete wavelet transform(DWT) has potential as a tool for supplying discriminatory attributes with which to distinguish seismic events. The wavelet transform has the great advantage over the Fourier transform in being able to localize changes. In this study, a discrete wavelet transform is applied to seismic traces for identifying seismic events and picking of arrival times for first breaks and S-wave arrivals. The precise determination of arrival times can greatly improve the quality of a number of geophysical studies, such as velocity analysis, refraction seismic survey, seismic tomography, down-hole and cross-hole survey, and sonic logging, etc. provide precise determination of seismic velocities. Tests for picking of P- and S- wave arrival times with the wavelet transform method is conducted with synthetic seismic traces which have or do not have noises. The results show that this picking algorithm can be successfully applied to noisy traces. The first arrival can be precisely determined with the field data, too.

• Geophysics and Geophysical Exploration
• /
• v.24 no.3
• /
• pp.89-97
• /
• 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
• /
• v.14 no.1
• /
• pp.25-41
• /
• 2011

AVO analysis was conducted on hydrocarbon-bearing structures by applying the crossplot and offset-coordinate amplitude polynomial techniques. To evaluate the applicability of the AVO analysis, it was conducted on synthetic data that were generated with an anticline model, and field data from the hydrocarbon-bearing Colony Sand bed in Canada. Analysis of synthetic data from the anticline model demonstrates that the crossplot method yields zero-offset reflection amplitude and amplitude variation with negative values for the upper interface of the hydrocarbon-bearing layer. The crossplot values are clustered in the third quadrant. The results of AVO analysis based on the coefficients of the amplitude polynomial are similar to those from the crossplots. These well correlated results of AVO analysis on field and synthetic data suggest that both methods successfully investigate the characteristics of the reflections from the upper interface of a hydrocarbon-bearing layer. Analysis based on the incident-angle equation facilitates the application of various interpretation methods. However, it requires the conversion of seismic data to an incident angle gather. By contrast, analysis using coefficients of the amplitude polynomial is cost-effective because it allows examining amplitude variation with offset without involving the conversion process. However, it warrants further investigation into versatile application. The two different techniques can be complement each other effectively as AVO-analysis tools for the detection of hydrocarbon reservoirs.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2008.10a
• /
• pp.107-112
• /
• 2008

In the case of the deep reservoirs like the gas reservoirs in the East-sea, it is often difficult to observe AVO responses in CMP gathers. Because the reservoir becomes more consolidated as its depth deepens, P-wave velocity does not decrease significantly when the pore fluid is replaced by the gas. In this study, we analyzed the effects of Poisson's ratio difference on AVO response with a variety of Poisson's ratios for the upper and lower layers. The results show that, as the difference in Poisson's ratio between the upper and lower layers decreases, the change in the reflection amplitude with incidence angle decreases. To consider the limitation of AVO responses shown in the gas reservoir in East-sea, the velocity model was made by simulation Gorae V structure with seismic data and well logs. The results of comparing AVO responses observed from the synthetic data with theoretical AVO responses calculated by using material properties show that the amount of the change in reflection amplitude with increasing incident angle is very small when the difference in Poisson's ratio between the upper and lower layers is small. In addition, the characteristics of AVO responses were concealed by noise or amplitude distortion arisen during preprocessing. To overcome such limitations of AVO analysis of the data from deep reservoirs, we need to acquire precisely reflection amplitudes in data acquisition stage and use processing tools which preserve reflection amplitude in data processing stage.

• Geophysics and Geophysical Exploration
• /
• v.4 no.2
• /
• pp.34-44
• /
• 2001

Forward modeling by construction of synthetic data is usually practiced in a horizontal surface and a few subsurface structures. However, in-situ surveys often take place in such topographic changes that the corrupted field data always make it difficult to interpret the right signals. To examine the propagation characteristic of elastic waves on the irregular surface, a general mesh generation code for finite element method was modified to consider the topography. By implementing this algorithm, the time domain modeling was practiced in some models with surface topography such as mound, channel, etc. The synthetic data obtained by receivers placed on surface also agreed with the analytic solution. The snapshots showing the total wave-field revealed the propagation characteristic of the elastic waves through complex subsurface structures and helped to identify the signals on the time traces. The transmission of Rayleigh waves along the surface, compressive waves, and sheer waves was observed. Moreover, it turned out that the Rayleigh waves behave like a new source at the edge.