Local singularity and S–A methods for analyzing ore-producing anomalies in the Jianbiannongchang area of Heilongjiang, China

Zhong-hai, Zhao; Qiao, Kai; Liu, Yiwen; Cui, Xiaomeng; Cheng, Binbin; Liang, Shuai; Li, Chenglu

doi:10.1007/s11631-022-00579-2

Cited by 1 publication

(5 citation statements)

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“…This study employs the S-A model was used to decompose the composite anomalies of the PC1 and PC2 principal component combination, thereby obtaining the principal component background field and anomaly field in the study area (Figures and ). Among them, the background field obtained by S-A decomposition mainly reflects the background composition of elemental mass fractions; high-background areas may be favorable for polymetallic mineral exploration; variations in the background strength reflect the presence of elements in a favorable geological context for mineralization, and the anomaly field mainly reflects local anomalous mass molecules of elements and noise generated during data processing. , …”

Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

“…Since the discovery in 1992 that geochemical elemental distributions can be portrayed using power-law relationships, the fractal characterization of geochemical fields has gradually been widely recognized. Various fractal and multifractal models have been generated accordingly, such as the C-A fractal mode, − the S-A fractal model, , and local singularity analysis methods where the S-A fractal model is a generalization of the C-A fractal model concept. It employs Fourier transformation to convert the elemental geochemical field from the spatial domain to the frequency domain and, based on the anisotropy of elements in the background, anomaly, and interference fields, summarizes their generalized self-similarity.…”

Section: Methodsmentioning

confidence: 99%

“…Among them, the background field obtained by S-A decomposition mainly reflects the background composition of elemental mass fractions; high-background areas may be favorable for polymetallic mineral exploration; variations in the background strength reflect the presence of elements in a favorable geological context for mineralization, and the anomaly field mainly reflects local anomalous mass molecules of elements and noise generated during data processing. 23,38 Combining the geological characteristics of the study area (Figure 1) with the decomposed PC1 and PC2 principal component background fields (Figure 9b, 10b), it was found that the spatial positions of high-background areas roughly coincide with the primary ore-hosting strata in the area. Specifically, the high-background regions in the PC1 are predominantly located in the central, northern, and western parts of the study area.…”

Section: Spectrum−area Fractal Model Analysismentioning

confidence: 99%

“…The aforementioned multifractal methods differ from traditional methods based on frequency distribution. It not only considers the distribution of deep information in geochemical fields but also takes into account spatial correlation, geometric patterns, and scale invariance, allowing complicated to identify effectively can be extraction of weakly retarded geochemical anomalies under complex geological conditions. ,, On the basis of the elemental anomaly distribution patterns, the Fry analysis method is introduced and integrated to analyze the characteristics and distribution law of geochemical anomaly deeply, enhance the comprehensiveness of geochemical anomaly information, and establish a transferable method system. This method quantitatively describes the migration and enrichment of elements, thereby determining the dominant mineralization direction of geochemical anomalies.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%