Utilizing Multifractal and Compositional Data Analysis Combined with Random Forest for Mineral Prediction in Goulmima, Morocco

Morocco is rich in Mississippi Valley Type (MVT) copper deposits. Currently, geochemical surveying is being conducted in the Goulmima region in pursuit of breakthroughs in mineral exploration. This paper focuses on the delineation of prospecting targets in the Goulmima area based on the ongoing 1:100,000 geochemical survey work in Morocco. The study employs compositional data transformation to perform isometric log-ratio (ilr) transformations on raw data, followed by the Spectrum-Area (S-A) fractal processing, and then uses the Random Forest (RF) algorithm for mineral prediction. Finally, the prediction results are further delineated using the Concentration-Area (C-A) fractal model to identify high-probability areas, marking two prospecting targets. The results show: (1) the ilr transformation reduces the closure problem of the original data and improves their symmetry, thereby more effectively revealing the spatial structural features of the elements; (2) the principal component analysis (PCA) performed on the ilr-transformed data successfully identifies two main element combinations, representing high-temperature hydrothermal environments (Mo-Sn-Ti-W-U) and low-temperature mineralization environments (CaO-Pb-Zn), consistent with the regional mining history; (3) the application of the S-A multifractal model effectively distinguishes between anomalies and background distributions in the geochemical data of the study area, and combines fault buffer zones as the basis for mineral prediction; (4) the C-A fractal model further subdivides the prediction results, dividing potential mining areas into high, medium, and low probability zones, and ultimately identifies two prospecting targets.