The application of geophysical methods is widely used for the assessment of subsurface geological features, including the determination of potential groundwater aquifers, understanding of stratigraphy sequences, and structural analysis. The demand for groundwater exploration is on the rise, encompassing needs for domestic, industrial, and agricultural purposes. However, unsuccessful groundwater exploration has occurred owing to a lack of comprehensive subsurface information. Groundwater, as an alternative water source, remains largely unexplored within the Penang Island. In relation to this, a thorough study on the geophysics and geology should be carried out. It is certainly not as simple as just digging a well to draw water. Current technologies widely used for groundwater exploration include geophysical methods, remote sensing, Geographic Information System (GIS), hydrogeological, and machine learning. Therefore, geophysical methods were applied to obtain subsurface information to determine and classify the materials using selected geophysical properties. Geophysical methods, particularly Electrical Resistivity Tomography (ERT) is a non-invasive technique that allows for accurate identification of aquifer boundaries, saturated zones, and is cost-effective. This research aims to assess the subsurface features and evaluate the delineation of potential groundwater zones using 2-D electrical resistivity imaging and serves as a preliminary guide for identifying suitable locations for groundwater exploration in Universiti Sains Malaysia, Penang Island, Malaysia. Eight resistivity survey lines (LR01-LR08) are conducted within the research area. The 2-D resistivity imaging survey was conducted using Pole-dipole array for LR02, LR03, LR04, LR06, LR07, and LR08 while Wenner Schlumberger array for LR01 and LR03 with 5 m minimum electrode spacing. The Wenner-Schlumberger array was chosen due to site constraints in placing the remote electrodes within the study area. The research area’s geology is characterised by an underlying igneous rock, featuring a centrally positioned granitic range that extends in a north-south direction, accompanied by low-lying elevations ranging between 5 metres and 20 metres above sea level. For this research, an automated multi-electrode resistivity meter, the ABEM Terrameter SAS 4000 system is employed. The resistivity values underwent an inversion process using RES2DINV software to generate the electrical resistivity topography model. The resistivity profiles are cross plotted using Surfer 8 and Sketchup software to obtain the overview of resistivity anomalies continuity between the resistivity profiles. The results showed the resistivity values ranged from 1 Ωm to 20,000 Ωm. Subsurface materials were categorised into four types based on their resistivity value ranges: Water-saturated zones (1 Ωm to 300 Ωm), weathered material (301 to 1,000 Ωm), weathered bedrock (1,001 to 4,500 Ωm), and fresh bedrock (> 5,000 Ωm). Several faults and anomalies are interpreted as potential water-saturated zones for groundwater drilling purposes. They are the continuation of water-containing zones between LR02, LR05, and LR08 profiles. These zones are proposed as a suitable potential location for drilling for groundwater. This outcome could contribute to establishing a one-stop centre for hardrock groundwater resources and developing sustainable guidelines for their management in the granitic region of Malaysia.

Subsurface geological formations are vital for validating deep engineering design assumptions, particularly in weathered terrains where unstable ground conditions pose risks. Geophysical investigations often face challenges due to inverse problem uncertainties and inadequate subsurface data. While resistivity and seismic P-wave velocity (Vp) imaging offer valuable insights, subsurface complexity necessitates integrated approaches for reliable characterization. This study introduces a machine learning-assisted geophysical–geotechnical framework combining electrical resistivity tomography, seismic refraction tomography, and borehole-based standard penetration tests (SPT-N). ML optimization metrics, including k-means clustering, PCA, Silhouette, elbow, and supervised linear regression, enhance analytical precision. A field survey over an 800 m segment in Kabota-Tawau, Sabah, Malaysia, utilized 490 collocated resistivity–Vp datasets to optimize cluster identification and interpretation accuracy. The analysis delineated four lithological units based on resistivity and Vp variations correlating with surface-subsurface properties. Clustering demonstrated strong performance, with an R² value approaching 1, a Silhouette score of 0.78, and an 88% reduction in the sum of square errors. Vulnerable zones, including weathered layers, fractures, and faults, were identified as critical for geotechnical consideration. In contrast, relatively weathered bedrock with hard-to-very-hard properties was deemed optimal for deep structural foundations requiring minimal reinforcement. This non-invasive approach enhances subsurface characterization, offering a reliable framework for construction site suitability and groundwater resource identification. It provides significant value for sedimentary regions with similar geological settings, advancing geotechnical and environmental planning.

In urban areas with complex geological formations, such as Petaling Jaya in western Peninsular Malaysia, assessing subsurface conditions is crucial for the safe and efficient design of infrastructure foundations. Traditional methods often fail to address the variability and heterogeneity of soil-rock profiles, which are key to predicting load-bearing capacity and preventing structural failures. This study introduces a novel microgravity–geotechnical approach, integrating Bouguer and residual with enhanced gravity-derivative and gravity anomaly depth-structure modeling as well as borehole data (SPT-N and RQD) to examine subsurface features and structural instabilities. The gravity models highlight significant low-density anomalies corresponding to weathered silty sands, sandy silts, and fractured limestone, while derivative and tilt angle maps reveal curvilinear structures, suggesting tectonic or karstic dissolution processes. These features, along with observed weak zones, exacerbate the risk of uneven settlement, subsidence, and structural damage, which are already evident in the area’s infrastructure. Borehole and depth-structure gravity models confirm variability in subsurface materials, with competent layers extending between 18 and 85 m in depth, underscoring the importance of precise foundation siting. Areas with low-density or fractured materials are evidently unsuitable for shallow foundations and require reinforcement, including deep foundation anchoring and specialized piling. The findings of this research provide critical insights for infrastructure design in regions with complex geological features, offering guidance on reliable siting and remediation of failing infrastructure. The approach established in this study can be applied to other terrains facing similar subsurface challenges, promoting safer and more resilient infrastructure development.