Recognizing Geochemical Anomalies Associated with Mineral Resources Using Singularity Analysis and Random Forest Models in the Torud-Chahshirin Belt, Northeast Iran

Abstract

Identifying the local geochemical anomalies from stream sediment samples is challenging in regional-scale exploration programs. For this purpose, some robust and reliable techniques must be applied to distinguish the geochemical targets from the background values. In this research, a procedure of several tools, including singularity mapping (SM), random forests (RF), success-rate curves, and the t-Student method, were employed to analyze the geochemical anomalies within the intrusive-plutonic Torud-Chahshirin belt (TCB), northeast Iran. In this regard, the success-rate curves were initially applied to extract efficient geochemical signatures. Then, singularity analysis was used on the selected geochemical elements (Au, Cu, Pb, and Zn), which were transformed via centered log-ratio (clr) transformation. In the next step, due to the complexity of the ore-forming processes in the TCB, the structural factors (e.g., fault intersection and faults with different orientations) were determined. Based on the success-rate curves, NE-trending faults and fault density were distinguished as critical structural criteria. Afterward, the RF model as a robust machine learning algorithm was executed on the four efficient SM-based geochemical layers and two efficient structural factors. The anomaly map derived by the RF model (Accuracy = 98.85% and Error = 1.15%) illustrates a very high relationship with Cu ± Au mineral occurrences. Therefore, the RF algorithm assisted by the singularity method is more trustworthy for highlighting the weak geochemical prospectivity areas in the TCB.

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia

“…Since Mandelbrot introduced the concept of “fractal geometry” in “The Fractal Geometry of Nature” in 1983, the fractal theory has gradually entered the public view . It has begun to be applied in analyzing and interpreting various complex phenomena. , With continuous research by scholars nationally and internationally, it has been discovered that elemental content exhibits scale invariance and that geological and mineralization processes often create fields characterized by self-similarity or statistical self-similarity, known as multifractal fields. ,− On the basis of this theory, various fractal and multifractal data processing models have arisen, including local singularity methods, , content-area (C-A), − concentration-volume (C-V), concentration-distance (C-D), concentration-number (C-N), spectrum-area (S-A), , Fractal projection pursuit classification (FPPC), spatially weighted singularity mapping (SWSM) and the number of feature spaces-eigenvalues (N-λ) fractal models . The aforementioned multifractal methods differ from traditional methods based on frequency distribution.…”