## 1 Introduction
Among the numerous available data interpolation techniques, the kriging technique of geostatistical analysis was determined to be more suitable for the evaluation of target geotechnical mapping. Kriging is a geostatistical interpolation technique that considers both the distance and degree of variation between known data points when estimating values in unknown areas. A kriging estimate is a weighted linear combination of the known sample values around the location to be estimated. The weighted average of the data available on nodes fixes the weight of all the nodes on the basis of the spatial variation and distance from the cell under consideration to determine the output value for each cell. The main strength of kriging is its unbiasedness and ability to predict the spatial distribution of uncertainty [1; 2]. The validation of generated maps was carried out through confirmatory tests at arbitrary selected additional ten points. Input parameters for modeling and mapping soil properties often consist of samples from preselected spatial locations, although samples are collected at several different soil depth intervals. However, it may be of interest or the conditions may require to assess the properties of soil at the locations other than the sampled locations or between the sampled locations. It is the task of the data analyst to put the data together in such a way that useful and reliable conclusions can be drawn for the soil depths of practical interest [3]. The effect of spatial variation on geotechnical properties of soil has been studied, and depositional environment has been reported to affect these properties [4]. The spatial modeling of geotechnical parameters has been utilized to assess the geotechnical properties by creating 3D models for the construction of the Lavrion Technological and Cultural Park [5]. Soil stratigraphic profiles were generated by employing algorithms that use individual cone penetration test (CPT) sounding as an input. These stratigraphic profiles have been reported to reduce ambiguity in generatedprofiles [6]. Memory-based learners and machine learning algorithms were determined to perform better than other regression analysis techniques, as reported in [7; 8]. The probabilistic approach has been ranked higher than deterministic approaches in geotechnical engineering owing to its better incorporation of uncertainty. Geotechnical uncertainty can be quantified through the statistical analysis of the database or on the basis of the judgment of an engineer performing the analysis [9]. A 2D Bayesian compressive sampling approach is developed to address uncertainties in the interpretation of spatially varying smaller geodatabase; this approach is compared with the kriging interpolation method for geostatistical analysis described in [10; 11]. The site variability modeling of cone penetration resistance has been determined to facilitate geotechnical engineers to better understand site conditions and produce a design with higher reliability [12]. A framework to predict soil behavior by modeling two significant sources of uncertainty, which are the measurement error and spatial variability, has been developed [13]. The prediction of various soil properties or soil classes on the basis of soil sampling over small area by employing numerical modeling techniques reduces their effectiveness owing to limited spatial variability incorporation. Digital soil mapping is an emerging technique to overcome the above-mentioned limitation in soil surveys [14]. Different approaches have been employed to model soil depth in France, and multi-resolution kriging has been reported to produce smoother maps compared to straightforward digital soil mapping and regression modeling [15]. The Illinois State Geological Survey (ISGS) has illustrated the workflow for the generation of 3D geologic mapping in ArcScene [16]. Geotechnical and geological characterization of a region in Portugal and geophysical and geotechnical approaches for micro-zonation studies for Hispaniola Island has also been appraised [17; 18].