A decade review of AI subsurface imaging synthesizes three core challenges and ships CIG-Bench for fault, RGT, geobody, and property tasks with synthetic data and baselines.
Yazeed Alaudah, et al
2 Pith papers cite this work, alongside 92 external citations. Polarity classification is still indexing.
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Pith papers citing it
92
external citations · OpenAlex
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physics.geo-ph 2years
2026 2representative citing papers
RGT-Est transforms relative geologic time estimation into a sinusoidal space and applies pointwise, perceptual, and adversarial losses to achieve better stratigraphic consistency and horizon correlation on seismic data.
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Artificial Intelligence for Subsurface Imaging Understanding: A Decade Review of Challenges, Methods, Benchmarks, and Outlook
A decade review of AI subsurface imaging synthesizes three core challenges and ships CIG-Bench for fault, RGT, geobody, and property tasks with synthetic data and baselines.
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Learning Stratigraphically Consistent Relative Geologic Time from 3D Seismic Data via Sinusoidal Mapping
RGT-Est transforms relative geologic time estimation into a sinusoidal space and applies pointwise, perceptual, and adversarial losses to achieve better stratigraphic consistency and horizon correlation on seismic data.