CIG-Bench: New Benchmark and Survey for AI-Driven Subsurface Imaging Interpretation
Researchers have created CIG-Bench, a comprehensive benchmark for applying artificial intelligence to subsurface imaging interpretation, which is critical for oil and gas exploration, carbon storage, and hazard monitoring. The benchmark synthesizes analysis of 652 publications from 2015-2025 and addresses key challenges including ambiguous signals, non-unique interpretations, and the lack of standardized evaluation methods in the field. This work aims to accelerate AI deployment in geophysical interpretation by providing unified datasets, evaluation protocols, and pretrained models.
A new research paper published on arXiv presents CIG-Bench, a systematic survey and benchmark for AI-driven subsurface imaging understanding. The authors analyzed 652 publications over the past decade and identified four major tasks in the field: structural interpretation, geobody identification, seismic facies analysis, and property estimation. The benchmark addresses three critical frontier challenges: interpretation under complex geological conditions, cross-survey semantic generalization with limited data, and the absence of reliable benchmarks for fair comparison. The proposed CIG-Bench integrates unified evaluation protocols, pretrained models, and datasets combining synthetic data for quantitative testing with real surveys for qualitative assessment. The authors emphasize that successful advancement requires integrating human expertise, physical constraints, and geological priors into AI models, along with treating uncertainty quantification as a core model output.
What's missing
The paper does not specify the exact composition of the benchmark datasets (number of samples, geographic distribution of real surveys, or specific synthetic data generation methods), nor does it provide preliminary performance metrics comparing different AI approaches on the proposed benchmark tasks.
What different sources said
- arXiv physicsCenter
CIG-Bench: A Comprehensive Survey and Benchmark for AI-Driven Subsurface Imaging Understanding
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