Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection

TL;DR

This paper introduces two machine learning frameworks for automated seismic interpretation: an unsupervised clustering method and a self-supervised deep learning model that delineates geological structures without manual labeling.

Contribution

The paper presents a novel combination of unsupervised clustering and self-supervised deep learning for seismic interpretation, enabling structure delineation without manual labels.

Findings

Successfully clustered seismic images into meaningful geological categories.

The deep learning model accurately delineates horizons, faults, salt domes, and chaotic structures.

Demonstrated competitive attribute extraction performance.

Abstract

We developed two machine learning frameworks that could assist in automated litho-stratigraphic interpretation of seismic volumes without any manual hand labeling from an experienced seismic interpreter. The first framework is an unsupervised hierarchical clustering model to divide seismic images from a volume into certain number of clusters determined by the algorithm. The clustering framework uses a combination of density and hierarchical techniques to determine the size and homogeneity of the clusters. The second framework consists of a self-supervised deep learning framework to label regions of geological interest in seismic images. It projects the latent-space of an encoder-decoder architecture unto two orthogonal subspaces, from which it learns to delineate regions of interest in the seismic images. To demonstrate an application of both frameworks, a seismic volume was clustered into various contiguous clusters, from which four clusters were selected based on distinct seismic patterns: horizons, faults, salt domes and chaotic structures. Images from the selected clusters are used to train the encoder-decoder network. The output of the encoder-decoder network is a probability map of the possibility an amplitude reflection event belongs to an interesting geological structure. The structures are delineated using the probability map. The delineated images are further used to post-train a segmentation model to extend our results to full-vertical sections. The results on vertical sections show that we can factorize a seismic volume into its corresponding structural components. Lastly, we showed that our deep learning framework could be modeled as an attribute extractor and we compared our attribute result with various existing attributes in literature and demonstrate competitive performance with them.