Abstract:
There is a growing interest in applying computer vision models to seismic interpretation, as manually segmenting seismic facies is often a time-consuming task. Foundation models like Segment Anything (SAM) have shown their effectiveness in segmentation tasks. This work explores SAM and SAM 2 models for segmenting geobodies and seismic layers across three different datasets: Parihaka, F3 Netherlands and Penobscot. We conducted experiments using Meta’s pre-trained SAM and SAM 2 models and fine-tuned them on seismic data. Fine-tuning considerably improved pre-trained models on segmenting geological features, with SAM 2 showing slightly better results than SAM. This highlights how it is possible to adapt visual Foundation Models to address seismic data analysis.
Referência:
CUSTÓDIO, Gustavo Torres; AOYAGI, Thiago Yuji; SAAR, Hugo Ferreira; SILVA, Cristina Maria Ferreira; GUERRA, Ney Ferreira de Souza; HELENO, Aline Fernandes; GAMBA, Carlos Tadeu de Carvalho; SILVA, Celso Luciano Alves da; VIRíSSIMO, Denis Bruno; SALES, Elisa Morande; SILLES, Felipe Silva; GUIRELLI NETTO, Leonides; GANDOLFO, Otavio Coaracy Brasil; RUBO, Otavio Rafael Andrello. Detecting geological features in seismic data using segment anything model 2 across multiple datasets. In: AMERICAN ASSOCIATION PETROLEUM GEOLOGISTS IMAGE’25: INTERNATIONAL MEETING FOR APPLIED GEOSCIENCE & ENERGY, 2025, Texas. Proceedings… 4p.
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