Shahrestani, Shahed, and Sanislav, Ioan (2025) Mapping geochemical anomalies using angle-based outlier detection approach. Journal of Geochemical Exploration, 269. 107635.

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Abstract

This study evaluates the effectiveness of the angle-based outlier detection (ABOD) method in identifying geochemical anomalies in the Ahar-Arasbaran Zone (AAZ) within the Alborz-Azerbaijan Magmatic Belt (AAMB), known for its diverse mineralization such as Cu-Mo porphyry deposits, epithermal base and precious metal veins, and Fe-Cu skarn deposits. ABOD and its fast approximation (FastABOD) were applied to datasets with 9 (Selective) and 32 (All) features. Results showed that with fewer variables, the performance difference between ABOD and FastABOD decreased, highlighting the impact of dimensionality on anomaly detection. Geochemical anomaly maps (ABOD_All, ABOD_Selective, FastABOD_All, FastABOD_Selective) were assessed for detecting known mineralization. ABOD_Selective demonstrated superior performance, effectively placing 76 % of skarn and 70 % of porphyry mineral occurrences into the highest anomaly class, despite its overall performance being approximately 61 %.

Additionally, ABOD was compared with independent component analysis (ICA), focusing on IC2 and IC5. ICA effectively highlighted unique geochemical patterns, with IC2 excelling in identifying Cu-enriched zones and ABOD effectively delineating both Au- and Cu-bearing zones. ABOD was also compared with local outlier detection methods LOF, kNN, and iNNE. LOF showed distinct anomaly distributions due to its local density approach, while kNN, iNNE, and FastABOD produced similar maps based on distance, isolation, and angle-distance. ROC analysis revealed no significant performance difference, though FastABOD showed slight superiority, particularly with mineralized samples as validation points. Moreover, applying principal component analysis (PCA) as feature selection method enhanced the performance of the FastABOD method in delineating geochemical anomalies related to hydrothermal mineralization. Finally, random forest regression identified key elements such as Sb, Au, As, and Cu as significant in distinguishing geochemical signals from various mineralization types.

Item ID:	84191
Item Type:	Article (Research - C1)
ISSN:	0375-6742
Copyright Information:	© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Date Deposited:	25 Nov 2024 22:49
FoR Codes:	37 EARTH SCIENCES > 3703 Geochemistry > 370301 Exploration geochemistry @ 40% 37 EARTH SCIENCES > 3703 Geochemistry > 370302 Inorganic geochemistry @ 30% 37 EARTH SCIENCES > 3705 Geology > 370508 Resource geoscience @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280107 Expanding knowledge in the earth sciences @ 100%
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