Deep learning in seismic surface wave analysis for building near-surface velocity models: Emerging applications, challenges, and new perspectives
Year: 2026
Type: article
Abstract: Seismic surface wave analysis has been widely adopted for characterizing shallow subsurface shear wave velocity (Vs) structures. The application of surface-wave analysis is now expanding from seismic imaging to time-lapse monitoring of the dynamic changes in subsurface properties. However, conventional workflows of surface-wave data processing and inversion face challenges in labor cost, subjectivity, and computational efficiency. The rapid rise of deep learning offers a transformative new paradigm in surface-wave analysis. This review examines the integration of deep learning in the key steps of surface-wave analysis: data acquisition optimization, automated dispersion curve extraction, intelligent Vs inversion, and time-lapse analysis. We analyze the emerging deep learning applications and challenges in surface-wave analysis and provide a perspective on future studies toward fully automated, intelligent, and generalized workflows for subsurface imaging and monitoring.
Source: Big Data and Earth System
Authors: Binbin Mi, Xinhua Chen, Feng Chen
Institutions: Zhejiang University, China University of Geosciences
Language: English
Topic: Seismic Waves and Analysis
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
Award: G6728159545
OpenAlex ID: W7127309044