3D Characterization of Mining Waste Piles: Bridging Geophysical and Geochemical Techniques for Improved AMD Prediction

Mining waste piles constitute a substantial environmental liability due to their propensity to generate Acid Mine Drainage (AMD), a severe form of water contamination caused by the oxidation of sulfide minerals. The complexity and heterogeneity of these garbage piles make it difficult to forecast and mitigate AMD accurately. When applied alone, traditional characterization techniques—whether they be based on non-specific geophysical surveys or discrete geochemical sampling—are frequently inadequate. This review article summarizes the most recent developments in mining waste pile classification by promoting an integrated, synergistic strategy that unifies geophysical and geochemical methods. In order to overcome the limitations of both individual approaches, the paper explains the principles, strengths, and limitations of key geophysical methods, such as Electrical Resistivity Tomography (ERT) and Induced Polarization (IP), and geochemical methods, such as Acid-Base Accounting (ABA) and kinetic tests. The paper then shows how the spatial data from geophysical surveys can be used to intelligently guide geochemical sampling to create comprehensive three-dimensional models that turn fragmented information into a continuous, spatially resolved map of AMD risk. Lastly, the review discusses future directions, including the use of machine learning, remote sensing, and advanced sensor technology, while emphasizing the crucial need for standardized protocols and interdisciplinary training to ensure the wider adoption of this potent methodology. The integrated approach is the most reliable foundation for managing AMD proactively as opposed to reactively, producing more long-lasting and beneficial environmental results.