Automatic Classification of Geologic Units in Seismic Images Using Partially Interpreted Examples

Abstract

Geologic interpretation of large seismic stacked or migrated seismic images can be a time-consuming task for seismic interpreters. Neural network based semantic segmentation provides fast and automatic interpretations, provided a sufficient number of example interpretations are available. Networks that map from image-to-image emerged recently as powerful tools for automatic segmentation, but standard implementations require fully interpreted examples. Generating training labels for large images manually is time consuming. We introduce a partial loss-function and labeling strategies such that networks can learn from partially interpreted seismic images. This strategy requires only a small number of annotated pixels per seismic image. Tests on seismic images and interpretation information from the Sea of Ireland show that we obtain high-quality predicted interpretations from a small number of large seismic images. The combination of a partial-loss function, a multi-resolution network that explicitly takes small and large-scale geological features into account, and new labeling strategies make neural networks a more practical tool for automatic seismic interpretation.

Does shallow geological knowledge help neural-networks to predict deep units?

“…The number of channels varies from six up to 32. In a similar experiment, Peters et al [2019a] showed that it is possible to obtain high-quality segmentations of the seismic images provided there are a sufficient number of known labeled columns. Here we will use lithology information from just two wells per image.…”

“…Recently, Peters et al [2018Peters et al [ , 2019a introduced partial loss-functions for non-linear regression and classification for seismic interpretation. These type of loss functions measure misfit at the known and sparsely distributed annotated label pixels only.…”

“…Therefore we need to use a loss function that only uses the known label pixels. We use the partial loss function that was introduced in a similar context by Peters et al [2019a], which reads L(D, {K}, C)) Q = −E(QC, Qf ({K}, D)).…”

Neural networks for geophysicists and their application to seismic data interpretation

“…are the labeled features that need to be recovered. Using deep convolution networks is therefore a straight forward extension of existing neural network technology and have been studied recently by many authors (see for example [Peters et al, 2018, 2019, Wu and Zhang, 2018, Waldeland et al, 2018, Poulton, 2002, Leggett et al, 2003, Lowell and Paton, 2018, Zhao, 2018 and references within). However, while it seems straight forward to use such algorithms, there are some fundamental differences between vision-related applications to seismic processing.…”