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

Bigdeli, Amirreza ; Maghsoudi, Abbas ; Ghezelbash, Reza

Minerals (Basel), 2023-11, Vol.13 (11), p.1399 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Recognizing Geochemical Anomalies Associated with Mineral Resources Using Singularity Analysis and Random Forest Models in the Torud-Chahshirin Belt, Northeast Iran
Autor: Bigdeli, Amirreza ; Maghsoudi, Abbas ; Ghezelbash, Reza
Assuntos: Algorithms ; Anomalies ; Belts ; China ; Copper ; Data mining ; Fault lines ; Fractals ; Geochemistry ; Geology ; Gold ; Iran ; local geochemical anomalies ; Machine learning ; Mineral exploration ; Mineral resources ; Mineralization ; Mines and mineral resources ; Model accuracy ; random forest ; Robustness ; Scientific imaging ; Sediment samplers ; Sediment samples ; Sediments ; Sediments (Geology) ; Singularities ; singularity mapping ; Statistical methods ; success-rate curve ; Support vector machines ; t-Student value ; Torud-Chahshirin belt ; Zinc
É parte de: Minerals (Basel), 2023-11, Vol.13 (11), p.1399
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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

Bigdeli, Amirreza ; Maghsoudi, Abbas ; Ghezelbash, Reza

Basel: MDPI AG

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