SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces

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.