Petroleum Science >2022,??lssue 1:??- DOI: https://doi.org/10.1016/j.petsci.2021.10.010
SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces Open?Access
文章信息
作者：San-Yi Yuan, Yue Zhao, Tao Xie, Jie Qi, Shang-Xu Wang,
作者單位：
投稿時間：
引用方式：San-Yi Yuan, Yue Zhao, Tao Xie, Jie Qi, Shang-Xu Wang, SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces, Petroleum Science, Volume 19, Issue 1, 2022, Pages 162-179, https://doi.org/10.1016/j.petsci.2021.10.010.
文章摘要
Abstract
Manually picking regularly and densely distributed first breaks (FBs) are critical for shallow velocity-model building in seismic data processing. However, it is time consuming. We employ the fully-convolutional SegNet to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces. Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs, we can obtain a well-trained SegNet model. When any unseen gather including the one with irregular trace spacing is inputted, the SegNet can output the probability distribution of different categories for waveform classification. Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background. Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer SegNet can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones, even when the proportion of randomly missing traces reaches 50%, 21 traces are missing consecutively, or traces are missing regularly.
Manually picking regularly and densely distributed first breaks (FBs) are critical for shallow velocity-model building in seismic data processing. However, it is time consuming. We employ the fully-convolutional SegNet to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces. Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs, we can obtain a well-trained SegNet model. When any unseen gather including the one with irregular trace spacing is inputted, the SegNet can output the probability distribution of different categories for waveform classification. Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background. Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer SegNet can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones, even when the proportion of randomly missing traces reaches 50%, 21 traces are missing consecutively, or traces are missing regularly.
關鍵詞
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First-break picking; Deep learning; Irregular seismic data; Waveform classification