A novel method for predicting the geochemical composition of tailings with laboratory field and hyperspectral airborne data using a regression and classification-based approach

Abstract

ABSTRACT The increasing demand for precise and dependable models has led to the development of both sensors and statistical algorithms. However, numerous studies have demonstrated that model performance is highly dependent on a range of environmental factors, such as spatio-temporal fluctuations of moisture, sensor type, sample variability, preprocessing methods, and model selection. These factors can impact prediction results, leading to erroneous comparisons across lab, field, or imaging models. Samples for this study were collected from a tailing settling basin of a porphyry copper deposit near Erdenet, Mongolia. The database contains lab and field spectra and hyperspectral imagery from a HySpex imaging sensor. In this study we propose a workflow that includes a simulation that yields an appropriate regression threshold while addressing data-driven uncertainty. The workflow consists of two regression models and five classification models at different scales for quantitative geochemical, mineralogical, and textural prediction of tailing samples. Each model is compared to the acquisition space's performance potential. Acceptable R2 values for regression models are 0.58 for laboratory, 0.40 for field, and 0.31 for hyperspectral airborne data. Results of this study are not limited to tailing samples but can be applied on other fields of research such as geology, pedology or agriculture.