Application of Artificial Intelligence and Machine Learning for Seismic Data Interpretation and Subsurface Characterization

https://doi.org/10.5281/zenodo.19660185

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized seismic data interpretation and subsurface characterization, shifting from labor-intensive manual workflows to automated, data-driven, and physics-constrained systems capable of handling massive, high-dimensional datasets. Supervised, unsupervised, and reinforcement learning paradigms now enable high-accuracy tasks including fault detection (via CNNs and GCNs), automated horizon picking (Vision Transformers and MDP models), seismic facies classification (U-Net, DeepLabV3+, FaciesMamba), and quantitative reservoir property prediction (porosity, permeability, shale volume) using FCNNs, Random Forests, and Physics-Informed Convolutional Neural Networks (PICNNs). Signal enhancement benefits from hybrid Transformer-GAN and collaborative denoising architectures that significantly improve signal-to-noise ratio while preserving subtle geological features. Physics-Informed Neural Networks (PINNs) and neural operators embed governing physical laws (wave equations, rock physics) into model training, ensuring consistency and generalization. Emerging trends include self-supervised learning, spiking neural networks, explainable AI (SHAP, Grad-CAM), and geo-foundation models for transfer learning across domains. These advancements deliver faster interpretation (up to 200×), reduced subjectivity, better uncertainty quantification, and improved decision-making in oil & gas exploration, earthquake early warning, and subsurface imaging. Challenges remain in labeled data scarcity, domain gaps, and model interpretability, but hybrid physics-ML frameworks position AI as a core enabler for precise, efficient, and reliable subsurface characterization in complex geological settings.