  This comprehensive review explores electrical and seismic refraction methods,
including their emerging techniques, namely electrical resistivity tomography
(ERT) and seismic refraction tomography (SRT). It discusses their crucial roles
in understanding surface-subsurface crustal dynamics, outlining their
principles, strengths, and limitations. Additionally, it provides insights into
the induced polarization method and briefly discusses the self-potential
method. The review thoroughly examines the mapping of surface-subsurface
resistivity and velocity structures, crucial for comprehending Earth's surface,
deep crustal, and hazard processes. Despite the numerous benefits of these
techniques, challenges persist, necessitating multidisciplinary approaches due
to lithology heterogeneities, geological process nuances, and geophysical data
uncertainties. Hence, the emergence of machine learning and deep learning in
ERT and SRT significantly enhances their accuracy in inversion modeling and
geological feature identification. This integration of domain-specific
knowledge with data- and image-driven approaches effectively addresses
subsurface characterization challenges. Case studies utilizing real-time field
electrical and seismic P-wave velocity datasets illustrate concepts,
accompanied by the development of new methodological frameworks and analytical
modeling. These frameworks provide systematic data modeling, facilitating
velocity-resistivity relationships for enhanced geological characterization.
This evidence underscores the importance of leveraging state-of-the-art
techniques to obtain a broader range of parameters for subsurface structure
delineation, environmental risk assessment, and informed decision-making.
Ongoing research innovation is anticipated to continue improving capabilities
in subsurface geophysical exploration.
