Pretrain-to-alignment learning paradigm to improve geophysical AI applicability under scarce field labels and synthetic-to-field gaps: A case study of relative geologic time estimation in global shelf-edge clinothems

TL;DR

This paper introduces a pretrain-to-alignment learning paradigm that enhances geophysical AI's robustness and accuracy in real-world applications with limited labels and domain gaps, demonstrated through a geologic time estimation case study.

Contribution

It presents a novel, unified progressive learning workflow combining self-supervised pretraining, synthetic supervision, and domain adaptation for geophysical AI tasks.

Findings

Achieved accurate and robust geologic time estimation across multiple surveys.

Significantly improved fine-scale stratigraphic and structural detail representation.

Validated on 3,000 datasets from various sedimentary basins.

Abstract

Artificial intelligence (AI) has been increasingly applied to various geophysical scenarios, yet its practical deployment remains limited by scarce field labels, pronounced synthetic-to-field domain gaps, and insufficient physical consistency under complex and variable field conditions. To address these challenges, we propose a pretrain-to-alignment learning paradigm that systematically integrates self-supervised pretraining, synthetic supervision, prior-driven refinement, and domain-adaptation fine-tuning into a unified progressive learning workflow. In this paradigm, geophysical AI models are developed through sequential stages that progressively build field-relevant representations, task-specific mapping capability, field consistency, and target-specific adaptability. We validate this paradigm using cross-survey relative geologic time (RGT) estimation in global shelf-edge clinothems as a representative case study. Results from 3,000 field datasets spanning multiple sedimentary basins demonstrate that the proposed paradigm achieves accurate, robust, and well-generalized performance across diverse field surveys, while significantly improving fine-scale stratigraphic and structural details. More broadly, this study provides a practical methodological reference for a broader range of geophysical AI tasks, such as interpretation, regression, and inversion problems.