A new synthesis of seismic research shows that artificial intelligence, when combined with physical principles, is rapidly transforming how scientists image the Earth's shallow and crustal structures. By embedding AI across the full surface-wave analysis workflow—from automated signal extraction to inversion and interpretation—researchers demonstrate major gains in speed, consistency, and scalability. At the same time, the study warns that purely data-driven models can produce results that lack physical meaning, even when they appear accurate. The findings highlight the need for physics-guided AI frameworks that balance computational efficiency with interpretability, offering a more reliable foundation for seismic imaging in both research and real-world applications.
Surface-wave methods are widely used to probe subsurface structures because wave dispersion naturally links frequency to depth. Yet traditional workflows remain slow, subjective, and computationally demanding, relying heavily on manual interpretation and iterative inversion. These challenges limit their use in dense monitoring networks and time-sensitive engineering applications. Artificial intelligence has emerged as a powerful alternative, enabling automation and dramatic speedups. However, many AI-based approaches operate as black boxes, raising concerns about physical reliability and generalization across geological settings.
In a review published on November 28, 2025, in Big Data and Earth System, researchers from Zhejiang University of Technology, Zhejiang University, and Anhui University of Science and Technology examine how artificial intelligence is reshaping surface-wave seismic methods. The article surveys recent advances in automated dispersion analysis, deep-learning-based inversion, physics-guided modeling, and explainable AI. By systematically comparing data-driven sensitivity patterns with classical seismic theory, the authors assess both the promise and the current limitations of AI-driven seismic imaging.
The review shows that AI has reshaped nearly every step of surface-wave analysis. Deep learning models can now automatically extract dispersion information from complex seismic data, removing the need for time-consuming manual picking. Once trained, neural networks can invert dispersion measurements into shear-wave velocity models far faster than traditional optimization methods, making large-scale imaging feasible. Crucially, the study emphasizes that speed alone is not enough. By comparing network-derived Jacobians with classical physical sensitivity kernels, the authors reveal that some AI models rely on statistical correlations rather than physically meaningful depth–frequency relationships. This mismatch can lead to misleading interpretations, particularly in poorly constrained depth ranges.
The review also highlights emerging solutions. Physics-guided and physics-informed models incorporate geological knowledge or governing equations into network design, improving stability and interpretability. A featured case study demonstrates how AI-assisted feature analysis can help identify subsurface karst cavities from seismic velocity models more objectively than manual inspection. Together, these results show that AI is most powerful when it complements—rather than replaces—physical understanding. The authors note that without physical consistency, fast results can still be misleading, and interpretability must become a core component of AI-based inversion.
Physics-guided AI surface-wave methods could significantly improve applications ranging from urban hazard assessment and infrastructure planning to groundwater monitoring and environmental studies. Faster, automated workflows enable near-real-time analysis from dense sensor networks, including emerging distributed acoustic sensing systems. At the same time, interpretable AI models help practitioners identify uncertainty and avoid overconfidence in automated results. As standardized datasets and physically informed architectures continue to develop, AI-driven seismic imaging is poised to move from experimental innovation to routine, reliable practice in Earth science and engineering.
