Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK

Abstract

Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simulations. In contrast, Machine Learning Algorithms (MLA) are (1) free of such limitations, (2) can incorporate information from multiple sources and therefore emerge with increasing interest in real-time resource estimation and automation. However, MLAs need to be explored for robust learning of phenomena, better accuracy, and computational efficiency. This paper compares MLAs, i.e., Multiple Linear Regression (MLR) and Random Forest (RF), with Ordinary Kriging (OK). The techniques were applied to the publicly available Walkerlake dataset, while the exhaustive Walker Lake dataset was validated. The results of MLR were significant (p < 10 × 10−5), with correlation coefficients of 0.81 (R-square = 0.65) compared to 0.79 (R-square = 0.62) from the RF and OK methods. Additionally, MLR was automated (free from an intermediary step of variogram modelling as in OK), produced unbiased estimates, identified key samples representing different zones, and had higher computational efficiency.

A Dual Robust Strategy for Removing Outliers in Multi-Beam Sounding to Improve Seabed Terrain Quality Estimation

“…The selection of variation function model is very important for the application of kriging algorithm [ 38 , 39 ]. In this section, Area 1 set was used for experiments.…”

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

“…The region is a high hydrocarbon-and minerals-producing area; therefore, such automated mapping tools would further lead to devising future strategies as an improved tool for further exploration beyond this region. The study can be extended further using explainable AI [72] to assess the learning of the model in the context of the response of input variables (bands) to the outputs (various formations). Further, the surface maps can be combined with AI-based spatial estimation models [72] applied to subsurface geochemical and geophysical data to develop 3D geological models of potential mineralized zones.…”

“…The study can be extended further using explainable AI [72] to assess the learning of the model in the context of the response of input variables (bands) to the outputs (various formations). Further, the surface maps can be combined with AI-based spatial estimation models [72] applied to subsurface geochemical and geophysical data to develop 3D geological models of potential mineralized zones. The approaches will enable the mineral exploration and mining industry to achieve Industry 4.0 [73] through IoT [74][75][76] and blockchain [77] solutions for secure data sharing within the mining industry.…”