Seismic attributes are critical in understanding geologic factors, such as sand body configuration, lithology, and porosity. However, existing attributes typically reflect the combined response of multiple geologic factors. The interplay between these factors can obscure the features of the target factor, posing a challenge to its direct seismic characterization, particularly when the factor is subtle. To address this, we develop an innovative neural network designed to disentangle and characterize the individual geologic factors within seismic data. Our approach divides the geologic information in the seismic data into two categories: a single geologic factor of interest and an aggregate of all other information. A novel feature-swapping mechanism within our network facilitates the disentanglement of these two categories, providing an interpretable representation. We use a triplet loss function to differentiate data samples with similar waveforms but varying subtle geologic details, thus enhancing the extraction of distinct features. In addition, our network uses a cotraining strategy to integrate the synthetic and actual field data during the training process. This strategy helps mitigate the potential performance degradation arising from the discrepancies between simulated and actual field data. We apply our method to synthetic data experiments and field data from two geologically distinct areas. Current results indicate that our method surpasses traditional approaches, such as a deep autoencoder and a convolutional neural network classifier, in extracting seismic attributes with more explicit geophysical implications.

You do not have access to this content, please speak to your institutional administrator if you feel you should have access.