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FAQ: AI-Enhanced Seismic Imaging - Physics-Guided Approaches for Reliable Subsurface Analysis
TL;DR
Physics-guided AI seismic imaging offers faster, more reliable subsurface analysis, giving companies an edge in urban planning and resource exploration.
AI automates surface-wave analysis by extracting dispersion data and inverting it into velocity models, but requires physical constraints to ensure meaningful results.
This approach enables better hazard assessment and groundwater monitoring, making communities safer and environmental protection more effective.
AI can now detect hidden karst cavities from seismic data, revealing underground features invisible to traditional methods.
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The research examines how artificial intelligence, when combined with physical principles, is transforming seismic imaging methods for analyzing Earth's shallow and crustal structures, specifically focusing on surface-wave analysis workflows.
Purely data-driven AI models can produce results that lack physical meaning even when they appear accurate, so physics-guided AI frameworks are needed to balance computational efficiency with interpretability and ensure trustworthy seismic inversion.
AI enables automation across the surface-wave analysis workflow, with deep learning models automatically extracting dispersion information from complex seismic data and neural networks inverting measurements into shear-wave velocity models far faster than traditional optimization methods.
Traditional workflows remain slow, subjective, and computationally demanding, relying heavily on manual interpretation and iterative inversion, which limits their use in dense monitoring networks and time-sensitive engineering applications.
Researchers from Zhejiang University of Technology, Zhejiang University, and Anhui University of Science and Technology conducted the review, which was published on November 28, 2025 in Big Data and Earth System with DOI: 10.1016/j.bdes.2025.100039.
Some AI models rely on statistical correlations rather than physically meaningful depth-frequency relationships, which can lead to misleading interpretations, particularly in poorly constrained depth ranges.
Physics-guided and physics-informed models incorporate geological knowledge or governing equations into network design, improving stability and interpretability, and AI-assisted feature analysis can help identify subsurface features like karst cavities more objectively than manual inspection.
The technology offers a more reliable foundation for seismic imaging in both research and real-world applications, including dense monitoring networks, time-sensitive engineering applications, and objective identification of subsurface features like karst cavities.
The research shows that AI is most powerful when it complements—rather than replaces—physical understanding, balancing computational efficiency with interpretability through physics-guided frameworks.
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