This comprehensive review examines electrical and seismic refraction methods, emphasizing their advanced applications in electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). These techniques are crucial for understanding surface–subsurface crustal dynamics, offering critical insights into resistivity and velocity structures for geological and geohazard assessments. The review also explores the induced polarization (IP) and self-potential (SP) methods as complementary approaches. Despite their proven utility, electrical and seismic refraction approaches face limitations arising from lithological heterogeneities, complex geological settings, and inherent data uncertainties. These challenges highlight the need for multidisciplinary strategies, including methodological innovations and integrative data frameworks. Recently, machine learning (ML) techniques have been increasingly applied to these geophysical methods, particularly joint ERT and SRT analyses, optimizing nonlinear inversion processes and improving the interpretation of complex subsurface conditions. The case studies presented in this review evaluate how supervised and unsupervised ML techniques enhance ERT and SRT by improving data interpretation, refining inversion accuracy, automating lithological differentiation, and predicting seismic velocity from resistivity data. The findings emphasize the growing significance of integrating traditional geophysical methods with data-driven approaches to improve subsurface modeling. Continued innovations in ERT and SRT methodologies, along with emerging computational tools and ML applications, will further enhance their effectiveness in geological, hydrological, environmental, and hazard assessments.

Noninvasive geoelectrical subsurface characterisation provides screening of the earth medium to resolve complexity in subsurface geology caused by inhomogeneity of the overburden strata and bedrock architectures. The subsurface geological, hydrogeophysical and engineering conditions of Etioro-Akoko, southwestern Nigeria, were investigated using electrical resistivity tomography (ERT). This was aimed at unravelling the hydrogeodynamic and litho-structural complexity that are responsible for low groundwater yield in wells/boreholes and foundation failures. Field 2D resistivity data sets of the study area were inverted and used to produce geotomographic models for detailed insights into the complex subsurface geological setting. Results of the 2D resistivity inverted models showed three to four distinct layers; the topsoil, weathered layer, partially weathered/fractured bedrock and fresh bedrock. Bedrock structures occasioned by fracturing and deep weathering of the bedrock were delineated with resistivities and thicknesses ranging from 40 to 950 Ωm and 10–25 m, respectively. The fracture systems; F1, F2, F3, F4 and F5 in NW-SE, NNW-SSE, NE-SW and ENE-WSW orientations act as the major groundwater collecting centres in the area. The orientations and geometries of these geologic features are the manifestations of structural deformation of the underlying geology. Fourteen hand-dug well and four borehole points were proposed based on the ERT results. Conversely, the localised bedrock structures and oscillating bedrock topography were suggestive of potential threats to the foundations of engineering structures in the studied area. Reinforcement of concrete foundations at certain sites where ERT suggested that the underlying strata were not capable of bearing loads was recommended as well. This study has offered a detailed understanding of the subsurface geological disposition for sustainable groundwater development and siting of durable civil engineering structures in the studied area and other areas with typical complex geological settings.