Massive-scale unlabeled field and labeled synthetic seismic datasets of global shelf-edge clinothems

TL;DR

This paper introduces a large hybrid dataset of unlabeled and labeled synthetic seismic data for shelf-edge clinothems, enabling AI development for automated seismic interpretation.

Contribution

It creates a comprehensive benchmark dataset through field data curation and forward modeling, addressing data scarcity in seismic stratigraphy AI research.

Findings

Deep learning models trained on the dataset show promising accuracy.

The dataset effectively supports model training and evaluation.

Public release of the dataset facilitates further research.

Abstract

Seismic stratigraphic interpretation of shelf-edge clinothems is essential for revealing tectonic evolution, paleoclimate change, depositional dynamic conditions, and hydrocarbon generation and accumulation during basin filling. However, traditional interpretation methods remain labor-intensive, time-consuming, and highly subjective. Although AI-based method offer a potential solution for automated this task, its development has been limited by the scarcity of comprehensive and representative benchmark datasets for shelf-edge clinothems. This limitation primarily arises from limited field data availability, the scarcity of reliable geological labels, and the structural complexity and strong variability of clinothem-dominated systems. To address this gap, we develop a hybrid benchmark dataset through two complementary strategies of field data curation and geological and geophysical forward modeling, ultimately generating 3,000 unlabeled field and 4,000 labeled synthetic seismic data, respectively. We further evaluate several representative baseline deep learning models on these datasets, and the accurate results demonstrate that the curated dataset provides an effective and representative basis for model training, quantitative assessment, and practical application. Finally, we have publicly released this hybrid benchmark dataset (https://doi.org/10.5281/zenodo.18910271) to facilitate the development, validation, and assessment of deep learning methods for automated seismic stratigraphic interpretation.