Abstract
Outcrop analogs are the high-resolution equivalent of carbonate reservoirs often utilized to decipher sub-seismic lateral and vertical depositional facies heterogeneity. Ground penetrating radar (GPR) is one of the most popular geophysical methods to extend the dimensionality of the depositional bodies identified on the 2D outcrop face. Correlating radargram signals to outcrop depositional facies requires exclusive and subject-specific expertise unfamiliar to most field geologists. This study presents a creative application of forward modeling and the conditional generative adversarial network (CGAN) to construct a photorealistic 3D “behind-the-outcrop” model from radargram and a drone-based digital outcrop model (DOM). This study tested the method to “see” the stromatoporoid/coral buildups in the interior of an outcrop cliff in central Saudi Arabia. The digital “behind-the-outcrop” model provides a straightforward medium for geologists to visualize and interpret rock formation instead of interpreting radargram signals.
Original language | English (US) |
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Pages | 2055-2059 |
Number of pages | 5 |
DOIs | |
State | Published - Aug 15 2022 |
Event | 2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022 - Houston, United States Duration: Aug 28 2022 → Sep 1 2022 |
Conference
Conference | 2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022 |
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Country/Territory | United States |
City | Houston |
Period | 08/28/22 → 09/1/22 |
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Geophysics