The graph attention network and its post-hoc explanation for recognizing mineralization-related geochemical anomalies

Ying, Xu; Zhang, Gubin

doi:10.1016/j.apgeochem.2023.105722

Cited by 15 publications

(1 citation statement)

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“…Exploration geochemistry is one of the leading methods in mineral exploration and quantitative prediction of mineral resources, widely used for seeking various types of mineral deposits and conducting mineral evaluations. − Since the 1970s, geologists have gathered extensive high-quality, multiscale, multielement geochemical data by applying exploration geochemistry techniques. Consequently, how to effectively mine the ore-forming information hidden in geochemical exploration data at different scales and identify such information has become the frontier and a hot topic of study. − The related analysis methods mainly include principal component analysis, factor analysis, cluster analysis, , local enrichment index method, spatially weighted principal component analysis, and fractal and multifractal methods. − Among them, fractal and multifractal theory’s anomaly identification and extraction method is one of the most straightforward, most effective, fastest, and most reliable methods.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao,

Yin

et al. 2024

ACS Omega

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The focus of exploration geochemistry is an accurate interpretation of geochemical data and the precise extraction of anomaly information related to mineralization from complex geological information. However, geochemical data are component data and exhibit a closure effect. Thus, traditional statistical methods cannot adequately reveal and identify the distribution of deep-seated anomaly information. This paper focuses on the Sidaowanzi area in Inner Mongolia and uses multivariate component data analysis methods to process 1:50 000 soil geochemical data. Using the Exploratory Data Analysis (EDA) method, the spatial distribution and internal structure characteristics of raw, logarithmic, and isometric logarithmic ratio (ILR) transformed data were compared and, coupled with robust principal component analysis (RPCA) and elemental component biplots, the association between element combinations and mineralization indicated by these three types of data was revealed. The S-A method was used to decompose composite anomalies of the ILR transformed RPCA score data to extract the characteristics of elemental combination anomalies and background distribution, and the Fry analysis method was utilized to analyze the dominant mineralization direction within the area. The results show that (1) data transformed using the ILR eliminated the influence of the closure effect, making the data more uniform on a spatial scale and exhibiting characteristics of an approximately normal distribution. (2) The S-A method was further used to decompose the composite anomaly of the PC1 and PC2 principal component combinations. The screened-out anomaly and background fields can essentially reflect the ore-causing anomalies dominated by Au and Cu−Mo mineralization. Moreover, the extracted anomalies and background information closely align with known mineral deposits (prospects) and can effectively identify weakly retarded geochemical anomaly information. (3) Fry analysis based on geochemical anomalies indicates that the dominant mineralization directions, by an assemblage dominated by Au and Cu−Mo, predominantly occur in the NE, NW, and proximate EW orientations. The combined application of the aforementioned three methods for the quantitative analysis of geochemical data aims to explore a transferable methodological system, providing new insights and approaches for further prediction of mineralization potential.

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