Near Surface Geophysics - Volume 24, Issue 1, 2026

The knowledge of in situ petro‐physical properties of flood basalt is key to understanding the groundwater potential, deep (>1 km) hydrological networks, and the nature of the subsurface aquifers (confined or unconfined). Porous flow tops and permeable columnar joints/natural fractures can store and transport groundwater throughout the thickness of the basaltic pile, respectively. Additionally, bulk formation porosity (isolated or effective/interconnected) and fracture network provide further insights towards exploring the potential of Deccan basalt for CO2 sequestration. Thus, information on in situ properties of basalts is a pre‐requisite to take up such exploration programs. In the present study, in situ petrophysical properties of a ∼750 m long, continuous vertical section of Deccan basalt (depth 500–1247 m) are determined from analyses of high‐resolution geophysical well log data acquired in a 3 km deep scientific borehole, KFD1, in the Koyna region, Western India. KFD1 passed through 1247 m thick Deccan traps and continued 1767 m into the underlying granitic basement. Well log data, including natural gamma, caliper, electrical resistivity, self‐potential (SP), formation density, neutron porosity, sonic velocities, temperature, nuclear magnetic resonance and wellbore image data, were acquired using standard Schlumberger logging tools. Salient results from the study are as follows. (i) The massive and non‐massive (comprising vesicular/amygdaloidal, flow top breccia and red bole horizons) parts of individual lava flows are delineated by contrasting physical properties. (ii) Empirical relationships are proposed for density, electrical resistivity, P and S wave velocities as a function of porosity for Deccan basalt formation. (iii) The geophysical well log datasets provide evidence for deep percolation of groundwater. Although the non‐massive parts of lava flows facilitate storage of groundwater, the presence of water‐saturated, permeable fractures in massive basalt sections favours deep hydrological networks in Deccan basalt.

Ground‐penetrating radar (GPR) is a widely used technique for near‐surface exploration, providing subsurface imaging of underground targets. However, in practical surveys, random noise severely degrades image quality and compromises interpretational reliability. Conventional GPR denoising approaches often rely on manual parameter tuning and struggle to achieve an effective balance between noise suppression and feature preservation. To address this challenge, this study proposes a hybrid denoising model (HybridDenoiser) that integrates dictionary learning with deep convolutional networks. The model employs an encoder to extract multi‐level features, incorporates a learnable dictionary to achieve sparse representation of these features and uses a decoder for high‐fidelity image reconstruction. Experimental results on both synthetic and field data demonstrate that the proposed method effectively suppresses noise, significantly enhances signal‐to‐noise ratio and structural similarity and better preserves the reflection characteristics of subsurface targets. These findings confirm the model's strong practicality and generalization capability, offering a promising solution for improving GPR image quality in complex environments.

This study aims to assess the quality of marble blocks after the extraction procedure, by analysing electromagnetic wave signals’ amplitude values obtained with the geophysical method of ground penetrating radar (GPR). To achieve this, we developed an algorithmic process able to process GPR data collected on marble blocks, named ‘Study Marble Blocks Quality’. This newly developed program consists of five different algorithms that were used—in a mainly automatic manner—to examine variations in the reflectivity of the prospected marble blocks. These variations were mostly attributed to internal block heterogeneities and/or fracturing. Our analysis highlights the importance of the GPR amplitude‐based quality assessment of marble blocks, emphasizing on the careful selection of GPR data processing steps as well as the choice of the optimum spacing between measurement lines.