Development and optimization of geotechnical soil maps using various geostatistical and spatial interpolation techniques: a comprehensive study

The substantial spatial variability of subsoil information in small grids poses a significant challenge for geological and geotechnical experts. In this regard, comprehensive geotechnical soil maps (GSMs) can address this challenge by employing smart interpolation techniques. However, the existing literature lacks a comprehensive evaluation of the effectiveness of different interpolation techniques in handling the spatial variability of geotechnical data, emphasizing the need for further research. Therefore, this study evaluates the performance of different geostatistical and spatial analyst interpolation techniques using extensive geotechnical data from the Lahore region. Statistical evaluation using key performance indicators (KPIs) manifested that inverse distance weighting (IDW) under spatial analyst performs superiorly. GSMs were further developed at multiple depths based on standard penetration test (SPT-N), shear wave velocity (Vs), soil type, plasticity index (P.I), soil activity (A), and chemical contents. Besides, regression correlations were established for the SPT-N, Vs, P.I, and A values for prompt pre-estimation and pre-evaluation of the geotechnical properties. Results reveal that the study area is predominated with fine-grained soil at shallow depth (i.e., 3.0 m) with SPT-N, Vs, P.I, and A values ranging between 1 and > 30, 0 and > 272 (m/s), 0 and > 30, and 0 and > 1.25, respectively, whereas at higher depths, coarse-grained soil is dominant. The soil chemical content at 1.5 m below the existing ground level falls within permissible limits. Furthermore, GSM validation using KPIs presents a good fit of predicted values with real-scale data. The study findings provide comprehensive GSMs using an optimized interpolation model, enhancing economic and infrastructure stability by providing valuable information during the planning and designing stages.