Application of machine learning methods in the processing of 3D seismic images

Abstract

This paper presents a comprehensive technical review of machine learning (ML) and deep learning (DL) methods applied to 3D seismic image analysis in geophysical exploration. The exponential growth in seismic data acquisition has created a "big data" challenge where single 3D surveys generate terabytes of complex volumetric data, making traditional manual interpretation increasingly impractical and subjective. This work systematically examines the paradigm shift from hypothesis-driven manual interpretation to data-driven automated pat-tern recognition, addressing the critical need for more efficient, objective, and repeatable an-alytical tools in the energy sector, environmental monitoring, and geological risk assessment.
The paper provides an in-depth analysis of state-of-the-art neural network architectures and their specific applications in seismic interpretation. Convolutional Neural Networks (CNNs) and U-Net architectures are examined for their effectiveness in fault detection, achieving clean, consistent results in hours compared to months of manual work. Advanced architectures like TransUnet and Dual U-Net are explored for their ability to capture both local and global geological features. The study covers supervised learning approaches for lithofacies classification and stratigraphic interpretation, as well as unsupervised methods combining Principal Component Analysis (PCA) with Self-Organizing Maps (SOM) for ex-ploratory analysis in data-limited regions.
Significant emphasis is placed on practical implementation challenges and solutions. The paper details workflows for handling SEGY format data using Python ecosystems (segyio, segysak with xarray and dask), strategies for multi-attribute analysis, and approaches to overcome the critical bottleneck of labeled training data scarcity through synthetic data gen-eration, augmentation techniques, and utilization of public datasets (F3, FORCE, TerraNu-bis). Emerging research frontiers are thoroughly discussed, including self-supervised learn-ing (SSL) for leveraging unlabeled data, physics-informed neural networks (PINNs) for in-corporating domain knowledge, and uncertainty quantification (UQ) methods for assessing model confidence.