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Headwave Stacking in Terms of Partial Derivative Wavefield
Shin, Changsoo 한국암반공학회 2004 Geosystem engineering Vol.7 No.1
Head wave stacking and velocity analysis are used to image the shallow subsurface, while CMP stacking and velocity analysis are used to image deep structures of the earth. I relate these concepts to partial derivative seismograms, which gives stacking straight line of head waves. The stacking straight line can be described kinematically by partial derivative seismograms, resulting in an interesting seismic imaging relationship.
Waveform inversion using a logarithmic wavefield
Changsoo Shin,Dong-Joo Min 한국산업응용수학회 2005 한국산업응용수학회 학술대회 논문집 Vol.- No.-
We propose a new objective function constructed by taking the logarithm of wavefields, which allows the separation of the objective function into three types using amplitude-only, phase-only and both. In our waveform inversion, we estimate the source signatures as well as the velocity structures by expressing the amplitudes and phases of the source signature in the objective function. We compute the steepest descent directions by using a matrix formalism derived from the frequency-domain finite-element/finite-difference modeling technique. Our numerical algorithms are similar to those of reverse-time migration and waveform inversion based on the adjoint state of the wave equation. In order to demonstrate the practical applicability of our algorithm, we use a synthetic data set from the Marmousi model. For synthetic data, the velocity structure inverted by our inversion algorithm is more compatible with the true velocity structure than that of the conventional waveform inversion algorithm.
Waveform inversion in the Laplace–Fourier domain
Shin, Changsoo,Ho Cha, Young Blackwell Publishing Ltd 2009 Geophysical journal international Vol.177 No.3
<P>SUMMARY</P><P>Since the pioneering work of Tarantola, waveform inversion has emerged as a tool for estimating velocity models of the subsurface using pre-stack seismic data. The waveform inversions have usually been performed in the time or frequency domain, but this can make it difficult to recover long-wavelength components of the velocity model due to the high non-linearity of the objective function and the lack of low-frequency components in the field data. Instead, it has been recently suggested that Laplace-domain waveform inversion can circumvent these limitations. By using the zero-frequency component of the damped wavefield, the Laplace-domain waveform inversion can recover long-wavelength structures of the velocity model even if low-frequency components less than 5 Hz are unreliable or would be unusable in conventional inversions. The main drawback is that the penetration depth of the Laplace-domain inversion depends on the offset distance and the choice of Laplace damping constants.</P><P>In this paper, we propose an improved Laplace–Fourier-domain waveform inversion to compensate for these weak points. This is accomplished by exploiting low frequency components (less than 5 Hz) of the damped wavefield. The success of this technique arises from the ‘mirage-like’ resurrection of low-frequency components less than 5 Hz and the unique characteristics of the complex logarithmic wavefield. The latter is capable of separating the wavefield into amplitude and phase components, allowing us to simultaneously generate both long-wavelength and medium-short-wavelength velocity models.</P><P>We successfully applied the Laplace–Fourier-domain waveform inversion to a synthetic data set of the BP model calculated using the time-domain finite difference method. This not only produced a more refined velocity model when compared to Laplace-domain inversion results, but it also improved the penetration depth of the inversion. Furthermore, when the velocity model produced by the Laplace–Fourier-domain waveform inversion was then used as an initial velocity model of a conventional frequency-domain inversion, we obtained an inverted velocity model containing almost every feature of the true BP model.</P><P>We applied our two-step, Laplace-domain waveform inversion to field data and obtained a refined velocity model containing short- and long-wavelength components. To convince ourselves of the accuracy of the inversion results, we computed a synthetic model using the estimated source wavelet and our velocity model from the inversion, and we obtained a migrated image and angle-domain common-image gathers at several points by a reverse-time pre-stack depth migration in the frequency domain. The reconstructed synthetic data were in good agreement with the field data and most parts of the reflections in the image gathers were flattened.</P>
네트워크RTK 드론을 이용한 정사영상 부분수정 방안 연구
신창수(Changsoo Shin),최윤수(Yunsoo Choi) 대한공간정보학회 2019 한국지형공간정보학회 학술대회 Vol.2019 No.11
최근 네트워크RTK 기능 탑재 드론의 출시로 인해 수평, 수직 호버링(hovering) 정확도 ±0.1M 확보가 가능하여 지상기준점 없이 정사영상이나 수치지형도 제작시 정확도가 점차 향상되고 있다. 본 연구에서는 수원시 서둔동지역에서 네트워크RTK 기능 탑재 드론을 활용하여 드론사진측량을 실시하여 Point Cloud, DSM 및 정사영상을 제작 후 정확도를 검토하였으며, 정사영상의 건물 및 도로 단위의 부분 수정 가능성을 확인하였다.
Estimation of the half-logistic distribution based on multiply Type I hybrid censored sample
Shin, Hyejung,Kim, Jungdae,Lee, Changsoo The Korean Data and Information Science Society 2014 한국데이터정보과학회지 Vol.25 No.6
In this paper, we consider maximum likelihood estimators of the location and scale parameters for the half-logistic distribution when samples are multiply Type I hybrid censored. The scale parameter is estimated by approximate maximum likelihood estimation methods using two different Taylor series expansion types ($\hat{\sigma}_I$, $\hat{\sigma}_{II}$). We compare the estimators in the sense of the root mean square error (RMSE). The simulation procedure is repeated 10,000 times for the sample size n=20 and 40 and various censored schemes. The approximate MLE of the second type is better than that of the first type in the sense of the RMSE. Further an illustrative example with the real data is presented.
On the maximum and minimum in a bivariate uniform distribution
Changsoo Lee,Hyejung Shin,Yeung Gil Moon 한국데이터정보과학회 2015 한국데이터정보과학회지 Vol.26 No.6
We obtain means and variances of max {X, Y} and min {X, Y} in the underlying Morgenstern type bivariate uniform variables X and Y with same scale parameters and different scale parameters respectively. And we obtain the conditional expectations in the underlying Morgenstern type bivariate uniform variables. Here, we shall consider the conditional expectations to know the dependence of one variable on the other variable and we consider the behaviors of means and variances ofmax {X, Y} and min {X, Y} with respect to changes in means, variances, and the correlation coefficient of the underlying Morgenstern type bivariate uniform variables.