application of the self-potential method for detecting leakage and assessing flooding risk in mining operations

Self-potential (sp) methods have proven effective for detecting subsurface fluid flow and identifying leakage pathways, particularly in mining environments prone to flooding. this paper investigates the application of sp surveys to assess leakage risk and predict potential flooding in mining sites. the proposed methodology involves sp data acquisition using fixed-station measurements and subsequent data processing with matlab-based algorithms. these algorithms filter redundant data, enhancing focus on signals indicative of fluid flow. the methodology was validated through two numerical examples representing shallow and deep mining environments. in the first example, sp measurements over a shallow mine revealed significant negative anomalies near specific locations, suggesting fluid leakage pathways. these findings were visualized using 2d contour and 3d surface plots, highlighting the potential zones of seepage. the second example, applied to a deeper mine, further demonstrated the sp method’s reliability in identifying flood risks through clearly defined negative anomalies. the filtering techniques in the algorithm effectively removed noise, ensuring accurate detection of fluid pathways. the results confirm that the sp method, combined with robust data processing techniques, offers a reliable and cost-effective approach to monitoring leakage and flood risk in mining operations. this study supports the practical implementation of sp methods in geophysical surveys for mine safety and environmental protection.