• 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.1 no.3
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• pp.183-187
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• 1998

Seismic waveform estimation is based on the assumption that the seismic trace tying a well is one dimensional convolution of the propagating seismic waveform and the reflectivity series derived from well logs (sonic and density). With this assumption, the waveform embedded in a seismic trace can be estimated using a Wiener match filter. In this paper, I experimented a preprocessing procedure that applies both on the seismic trace and on the reflectivity series. The procedure is based on the assumption that the travel time can be estimated better from the seismic trace and that the instantaneous reflectivity values can be measured better on the well log. Thus the procedure is, 1) start-time adjustment and dynamic differential stretches are applied on the sonic log, and 2) seismic amplitudes are balanced such that the low frequency part of the seismic are matched to that of the reflectivities derived from well logs.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.459-463
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• 2003

Waveform inversion requires extracting a reliable low frequency content of seismic data for estimating of the low wave number velocity model. The low frequency content of the seismic data is usually discarded or neglected because of the band-limited response of the source and the receivers. In this study, however small the spectral of the low frequency seismic data is, we assume that it is possible to extract a reliable phase information of the low frequency from the seismic data and use it in waveform inversion. To this end, we exploit the frequency domain finite element modeling and source-receiver reciprocity to calculate the $Frech\`{e}t$ derivative of the phase of the seismic data with respect to the earth model parameter such as velocity, and then apply a damped least squares method to invert the phase of the seismic data. Through numerical example, we will attempt to demonstrate the feasibility of our method in estimating the correct velocity model for prestack depth migration.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.33-39
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• 2019

In this paper, we propose deep convolutional neural network(CNN) with bottleneck structure which improves the performance of earthquake classification. In order to address all possible forms of earthquakes including micro-earthquakes and artificial-earthquakes as well as large earthquakes, we need a representation and classifier that can effectively discriminate seismic waveforms in adverse conditions. In particular, to robustly classify seismic waveforms even in low snr, a deep CNN with 1x1 convolution bottleneck structure is proposed in raw seismic waveforms. The representative experimental results show that the proposed method is effective for noisy seismic waveforms and outperforms the previous state-of-the art methods on domestic earthquake database.

• Geophysics and Geophysical Exploration
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• v.17 no.4
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• pp.231-241
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• 2014

Recently, single channel seismic survey for engineering purpose have been used widely taking advantage of simple processing. However it is very difficult to obtain high fidelity subsurface image by single channel seismic due to insufficient fold coverage. Recently, seismic waveform inversion in multi channel seismic survey is utilized for accurate subsurface imaging even in complex terrains. In this paper, we propose the seismic waveform inversion algorithm for velocity model building using a single channel seismic data. We utilize the Gauss-Newton method and assume that subsurface model is 1-Dimensional. Seismic source estimation technique is used and offset effect is also corrected by removing delay time by offset. Proposed algorithm is verified by applying modified Marmousi2 model, and applied to field data set obtained in port of Busan.

• Journal of the Earthquake Engineering Society of Korea
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• v.13 no.5
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• pp.23-29
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• 2009

On December 13, 1996, an earthquake with local magnitude (M$_L$) 4.5 occurred in the Yeongwol area of South Korea. The epicenter was 37.2545$^{\circ}$N and 128.7277$^{\circ}$E, which is located inside the Okcheon Fold Belt. The waveform inversion analysis was carried out to estimate source parameters of the event according to the filtering bandwidth of seismic data. Using 0.02$\sim$0.2 Hz filtering bandwidth, focal depth and seismic moment were estimated to be 6 km and 1.3$\times$10$^{16}$ N$\cdot$m, respectively. This seismic moment corresponds to the moment magnitude (M$_W$) 4.7. The focal mechanism by the waveform inversion and P wave first motion polarity analysis is a strike slip faulting including a small thrust component, and the direction of P-axis is ENE-WSW. The moment magnitude estimated by spectral analysis was 4.8, which is similar to that estimated by waveform inversion. Average stress drop was estimated to be 14.3 MPa.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.99-104
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• 2009

We propose a waveform inversion method for SH-wave data obtained in a shallow seismic refraction survey, to determine a 2D inhomogeneous S-wave profile of shallow soils. In this method, a 2.5D equation is used to simulate SH-wave propagation in 2D media. The equation is solved with the staggered grid finite-difference approximation to the 4th-order in space and 2nd-order in time, to compute a synthetic wave. The misfit, defined using differences between calculated and observed waveforms, is minimised with a hybrid heuristic search method. We parameterise a 2D subsurface structural model with blocks with different depth boundaries, and S-wave velocities in each block. Numerical experiments were conducted using synthetic SH-wave data with white noise for a model having a blind layer and irregular interfaces. We could reconstruct a structure including a blind layer with reasonable computation time from surface seismic refraction data.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.440-449
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• 2018

Purpose: This study aims to design and verify an onsite EEWS that extracts the P-wave from a single seismic station and deduce the PGV. Method: The P-wave properties of Pd, Pv, and Pa were calculated by using 12 seismic waveform data extracted from historic seismic records in Korea, and the PGVs were computed using empirical equation on the P properties - PGV relationship and compared with the observed values. Results: Comparison of the observed and estimated PGVs within the alarm level shows the error rate of 86.7% as minimum. By reducing the PTW to 2 seconds, the alarm time can be shortened by 1 second and the seismic blind zone near the epicenter can be shortened by 6 Km. Conclusion: Through this study, we confirmed the availability of the on-site EEWS in Korea. For practical use, it is necessary to develop regression formula and algorithm reflect local effect in Korea by increasing the number of seismic waveform data through continuous observation, and to eliminate the noise from the site.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.202-209
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• 2019

In this study, an acoustic full-waveform inversion using Adam optimizer was proposed. The steepest descent method, which is commonly used for the optimization of seismic waveform inversion, is fast and easy to apply, but the inverse problem does not converge correctly. Various optimization methods suggested as alternative solutions require large calculation time though they were much more accurate than the steepest descent method. The Adam optimizer is widely used in deep learning for the optimization of learning model. It is considered as one of the most effective optimization method for diverse models. Thus, we proposed seismic full-waveform inversion algorithm using the Adam optimizer for fast and accurate convergence. To prove the performance of the suggested inversion algorithm, we compared the updated P-wave velocity model obtained using the Adam optimizer with the inversion results from the steepest descent method. As a result, we confirmed that the proposed algorithm can provide fast error convergence and precise inversion results.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.291-296
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• 2007

We measured potential waveform of load, displacement, micro electric signal generated by rock and mortar fracture using PXI A/D Converter. The rock type used for measurement was used granite, limestone and sandstone, and mortar specimen. we made measuring equipment of physical properties to confirm basic information of physical properties, measured physical properties of rock engineering, electric resistivity and seismic velocity. Potential waveform system was built using PXI A/D Converter and measured potential waveform of load, displacement, micro-electric signal generated using this during uniaxial compressive test by the specimen finished such test of physical properties. Using the saturated rock and mortar specimen, micro electric signal increased, and It didn't increase a signal in dried rock and mortar specimen according as load and strain rate increases. But signal also increased in saturated or dried specimen in case of sandstone. It was possible to check the close correlation relationship the signal and fracture behavior by a compressive load as the signal of fracture position was increased bigger than the other position. It was also possible to check the correlation relationship between physical properties and micro geo-electric signal.