Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts
arXiv:2606.15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation. The Segment Anything Model (SAM), in particular, offers powerful zero shot segmentation capabilities through prompt based interaction, thus making it a promising tool for seismic interpretation. However, most existing applications of SAM rely on fine tuning for specific geological targets, which requires extensive labeled data, incurs high computational cost, and often compromises the model's generalizati
The proliferation of powerful foundation models like SAM is enabling their application in specialized, data-intensive fields where traditional methods are costly and time-consuming, driven by increasing compute availability and model accessibility.
This development indicates a significant efficiency gain in critical resource exploration (like seismic interpretation for oil & gas or mineral deposits) through advanced AI, democratizing access to sophisticated analytical tools.
The reliance on extensive labeled datasets and high computational costs for bespoke AI solutions in seismic interpretation is diminishing, replaced by more adaptable, prompt-based foundation models.
- · Energy exploration companies
- · Geospatial AI developers
- · AI model providers (e.g., Meta/SAM)
- · Developing nations with resource potential
- · Traditional seismic interpretation software vendors
- · Consulting firms relying on manual interpretation
- · Companies with high data labeling costs
Seismic interpretation becomes significantly faster and more cost-effective, potentially accelerating new discoveries.
Reduced exploration costs could stabilize or increase supply for certain resources, impacting global commodity markets.
Broader adoption of zero-shot AI in other specialized scientific domains, leading to cross-sector productivity gains and new AI-driven research methodologies.
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Read at arXiv cs.AI