• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.41-44
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• 2011

• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.37-40
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• 2011

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1096-1099
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• 2000

A method to analyze the high speed inter-connects that are composed of frequency dependent lossy distributed lines is presented. Network modeling of hybrid systems is implemented by using the modified nodal admittance matrix in the Laplace transformation domain. The network response is computed by different two methods. One method Is the asymptotic waveform evaluation (AWE) method and other is numerical Laplace inversion method. The merits and demerits of two methods are discussed by applying to several concrete illustrative networks.

• 한국지구물리탐사학회:학술대회논문집
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• 2010.10a
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• pp.185-188
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• 2010

• Geophysics and Geophysical Exploration
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• v.24 no.1
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• pp.6-19
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• 2021

Subsurface physical properties can be obtained and imaged by seismic exploration, and various algorithms have been developed for this purpose. In this regard, root mean square error (RMSE) has been widely used to quantitatively evaluate the accuracy of the developed algorithms. Although RMSE has the advantage of being numerically simple, it has limitations in assessing structural similarity. To supplement this, full-reference image quality assessment (FR-IQA) techniques, which reflect the human visual system, are being investigated. Therefore, we selected six FR-IQA techniques that could evaluate the obtained physical properties. In this paper, we used the full-waveform inversion, because the algorithm can provide the physical properties. The inversion results were applied to the six selected FR-IQA techniques using three benchmark models. Using salt models, it was confirmed that the inversion results were not satisfactory in some aspects, but the value of RMSE decreased. On the other hand, some FR-IQA techniques could definitely improve the evaluation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.3
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• pp.203-211
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• 2023

A Markov chain Monte Carlo (MCMC) simulation is proposed for probabilistic full waveform inversion (FWI) in a layered half-space. Dynamic responses on the half-space surface are estimated using the thin-layer method when a harmonic vertical force is applied. Subsequently, a posterior probability distribution function and the corresponding objective function are formulated to minimize the difference between estimations and observed data as well as that of model parameters from prior information. Based on the gradient of the objective function, a proposal distribution and an acceptance probability for MCMC samples are proposed. The proposed MCMC simulation is applied to several layered half-space examples. It is demonstrated that the proposed MCMC simulation for probabilistic FWI can estimate probabilistic material properties such as the shear-wave velocities of a layered half-space.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.114-120
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• 2009

Rupture directivity is the important parameter in estimating damage due to earthquakes. However, the traditional moment tensor inversion technique cannot resolve the real fault plane or the rupture directivity. To overcome these limitations, we have developed a new inversion algorithm to determine the moment tensor solution and the rupture directivity for moderate earthquakes, using the waveform inversion technique in the frequency domain. Numerical experiments for unilateral and bilateral rupture models with various rupture velocities confirm that the method can resolve the ambiguity of the fault planes and the rupture directivity successfully. To verify the feasibility of the technique, we tested the sensitivity to velocity models, which must be the most critical factor in practice. The results of the sensitivity tests show that the method can be applied even though the velocity model is not perfect. If this method is applied in regions where the velocity model is well verified, we can estimate the rupture directivity of a moderate earthquake. This method makes a significant contribution to understanding the characteristics of earthquakes in those regions.

• Geophysics and Geophysical Exploration
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• v.7 no.1
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• pp.7-13
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• 2004

In this paper, we demonstrate that Common Mid-Point (CMP) cross-correlation gathers of multi-channel and multi-shot surface waves give accurate phase-velocity curves, and enable us to reconstruct two-dimensional (2D) velocity structures with high resolution. Data acquisition for CMP cross-correlation analysis is similar to acquisition for a 2D seismic reflection survey. Data processing seems similar to Common Depth-Point (CDP) analysis of 2D seismic reflection survey data, but differs in that the cross-correlation of the original waveform is calculated before making CMP gathers. Data processing in CMP cross-correlation analysis consists of the following four steps: First, cross-correlations are calculated for every pair of traces in each shot gather. Second, correlation traces having a common mid-point are gathered, and those traces that have equal spacing are stacked in the time domain. The resultant cross-correlation gathers resemble shot gathers and are referred to as CMP cross-correlation gathers. Third, a multi-channel analysis is applied to the CMP cross-correlation gathers for calculating phase velocities of surface waves. Finally, a 2D S-wave velocity profile is reconstructed through non-linear least squares inversion. Analyses of waveform data from numerical modelling and field observations indicate that the new method could greatly improve the accuracy and resolution of subsurface S-velocity structure, compared with conventional surface-wave methods.

• Geophysics and Geophysical Exploration
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• v.19 no.3
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• pp.136-144
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• 2016

Since seismic inversion is based on the wave equation, it is important to calculate the solution of wave equation exactly. In particular, full waveform inversion would produce reliable results only when the forward modeling is accurately performed because it uses full waveform. When we use finite-difference or finite-element method to solve the wave equation, the convergence of numerical scheme should be guaranteed. Although the general proof of convergence is provided theoretically, the consistency and stability of numerical schemes should be verified for practical applications. The implementation of source function is the most crucial factor for the consistency of modeling schemes. While we have to use the sinc function normalized by grid spacing to correctly describe the Dirac delta function in the finite-difference method, we can simply use the value of basis function, regardless of grid spacing, to implement the Dirac delta function in the finite-element method. If we use frequency-domain wave equation, we need to use a conservative criterion to determine both sampling interval and maximum frequency for the source wavelet generation. In addition, the source wavelet should be attenuated before applying it for modeling in order to make it obey damped wave equation in case of using complex angular frequency. With these conditions satisfied, we can develop reliable inversion algorithms.

• 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.