Unsupervised seismic facies classification using deep convolutional autoencoder

TL;DR

This paper presents an unsupervised deep convolutional autoencoder approach for seismic facies classification, enabling real-time geological pattern analysis without manual labeling or large datasets.

Contribution

It introduces a novel unsupervised deep learning method that automatically classifies seismic facies using feature clustering, eliminating the need for labeled data.

Findings

Accurately classifies seismic facies on real data

Provides instant facies maps

Enables real-time geological analysis

Abstract

With the increased size and complexity of seismic surveys, manual labeling of seismic facies has become a significant challenge. Application of automatic methods for seismic facies interpretation could significantly reduce the manual labor and subjectivity of a particular interpreter present in conventional methods. A recently emerged group of methods is based on deep neural networks. These approaches are data-driven and require large labeled datasets for network training. We apply a deep convolutional autoencoder for unsupervised seismic facies classification, which does not require manually labeled examples. The facies maps are generated by clustering the deep-feature vectors obtained from the input data. Our method yields accurate results on real data and provides them instantaneously. The proposed approach opens up possibilities to analyze geological patterns in real time without human intervention.