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http://dx.doi.org/10.7582/GGE.2021.24.2.53

Case Analysis of Seismic Velocity Model Building using Deep Neural Networks  

Jo, Jun Hyeon (Department of Energy Resources Engineering, Pukyong National University)
Ha, Wansoo (Department of Energy Resources Engineering, Pukyong National University)
Publication Information
Geophysics and Geophysical Exploration / v.24, no.2, 2021 , pp. 53-66 More about this Journal
Abstract
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.
Keywords
velocity model building; deep neural networks; seismic exploration;
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