Uncertainty Estimation of Subsurface Fault Interpretation Leveraging Multi-Quality Annotations
In this work show that while fault delineation is hampered by weak seismic reflectors, degraded image quality, and interpreter variability, non-expert annotations can approximate expert quality in certain cases and offer valuable insights for ambiguous images. We propose an algorithm (details omitted) that incrementally integrates additional interpreters in reverse quality order, which enhances disagreement and uncertainty modeling compared to approaches relying solely on scarce expert-labeled data.
Multi-Instance SAM2 for Seismic Segmentation
Human-Initiated Prompt Optimization (HIPPO) for Seismic Facies Segmentation Using the Segment Anything Model
In this work we propose Human-Initiated Prompt Optimization (HIPPO)—a novel method that enhances SAM’s segmentation capabilities in specialized domains. HIPPO leverages human input to establish an effective prompting strategy, allowing for better segmentation of seismic facies with minimal human effort.
Line SAM: a line-based prompting for seismic foundation models
n this work we propose a line-based prompting method for seismic foundation models that leverages the inherent geophysical line features—such as reflections and horizons—to better delineate target geobodies.
On the shared subspace between different seismic data sources
In this abstract, we analyze the way fault segmentation models transfer knowledge across different data sources under various training and finetuning conditions.
Domain Adaptation in Seismic Interpretation: A Normalization Problem
In this work, we train eight segmentation models on three fault segmentation datasets, Thebe, synthetic, and CRACKS, to study domain shifts. Our results show that domain adaptation in seismic images is closely tied to normalization, as dataset-specific intensity differences strongly affect model performance.
