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Recognition of ore-induced geochemical anomaly by combined factor and fractal analysis in Heiyingshan, Inner Mongolia |
Jing-Jing GONG, Jian-Zhou YANG, Sheng-Ming MA, Lei SU |
Key Laboratory of Geochemical Exploration, Institute of Geophysical and Geochemical Exploration, CAGS, Langfang 065000, China |
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Abstract Factor analysis and fractal modeling were combined to study the 1:50000 geochemical data of Heiyingshan, Inner Mongolia, so as to recognize ore-induced geochemical anomaly. First, sample factor analysis of 10 elements mainly includes three factors: F1 consists of Zn-Cd-(Cu), F2 consists of Ag-Mo-Cu-Bi, and F3 consists of Sn-As-Pb-Sb. Second, the F2 factor score is further modeled by Concentration-Area (C-A) factor model. The fractal fitting lines of four segments were obtained from the log-log graph of Area-F2 factor score, which represent "low background area without prospecting potential", "background area", "high background area" and "high prospecting potential area", respectively. In the subsequent prospecting work, several mineralizations and alterations were found in the "high prospecting potential area". Some conclusions have been reached: F2 factor reflects the hydrothermal metallogenic process and can be used as a comprehensive index for the spatial aggregation of major metallogenic elements in the study area. The C-A fractal model can accurately delineate the boundary of F2 factor "high metallogenic potential area". The "high metallogenic potential area" can be used as the basis for delineating the prospecting target.
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Received: 20 March 2019
Published: 03 March 2020
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Geological setting of Heiyingshan area(a) and location of samples(b)
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Geochemical map(a), Histogram(b) and concentration-quantity log-log plot of Cu in Heiyingshan area
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因子 | 初始特征值 | 提取载荷平方和 | 旋转载荷平方和 | 特征值 | 方差 百分比/% | 累积/% | 特征值 | 方差 百分比/% | 累积/% | 特征值 | 方差 百分比/% | 累积/% | 1 | 3.256 | 32.555 | 32.555 | 3.256 | 32.555 | 32.555 | 2.253 | 22.526 | 22.526 | 2 | 1.456 | 14.559 | 47.114 | 1.456 | 14.559 | 47.114 | 1.848 | 18.480 | 41.006 | 3 | 1.146 | 11.458 | 58.572 | 1.146 | 11.458 | 58.572 | 1.757 | 17.566 | 58.572 | 4 | 0.918 | 9.179 | 67.752 | | | | | | | 5 | 0.787 | 7.873 | 75.625 | | | | | | | 6 | 0.689 | 6.89 | 82.516 | | | | | | | 7 | 0.539 | 5.391 | 87.907 | | | | | | | 8 | 0.487 | 4.871 | 92.778 | | | | | | | 9 | 0.474 | 4.743 | 97.521 | | | | | | | 10 | 0.248 | 2.479 | 100 | | | | | | |
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Characteristic roots and total variance explained of R-factor analysis
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变量 | 因子 | F1 | F2 | F3 | Zn | 0.876 | 0.211 | 0.035 | Cd | 0.705 | 0.116 | 0.074 | Ag | 0.07 | 0.781 | 0.063 | Mo | 0.04 | 0.740 | 0.384 | Cu | 0.580 | 0.583 | 0.004 | Bi | 0.231 | 0.415 | -0.153 | Sn | 0.199 | 0.080 | 0.774 | As | 0.172 | -0.038 | 0.717 | Pb | -0.321 | -0.097 | 0.685 | Sb | 0.128 | 0.319 | 0.630 |
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Orthometric rotating factor loading matrix for the R-factor analysis
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Geochemistry subdivisions map of factor score in Heiyingshan area
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Histogram of F2 score
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Log-log plot of the cumulative area versus F2 factor score
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F2 factor score based on C-A fractal model
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Fig.6 a—geology map; b—samples symbol anomaly map of Ag; c—samples symbol anomaly map of Mo; d—samples symbol anomaly map of Cu; e—samples symbol anomaly map of Bi; f—map of F2 socre;the lower limit of the anomaly is determined by taking the outlier value of the whole work area and taking the confidence coefficient of 1.6; the 4 levels of Ag, Mo, Cu, and Bi represent the background, anomalous outer band, anomalous middle band, and anomalous inner band ">
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Geochemical analysis of area b in Fig.6 a—geology map; b—samples symbol anomaly map of Ag; c—samples symbol anomaly map of Mo; d—samples symbol anomaly map of Cu; e—samples symbol anomaly map of Bi; f—map of F2 socre;the lower limit of the anomaly is determined by taking the outlier value of the whole work area and taking the confidence coefficient of 1.6; the 4 levels of Ag, Mo, Cu, and Bi represent the background, anomalous outer band, anomalous middle band, and anomalous inner band
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Fig. 6 ">
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Photograph of mineralized alteration outcrop in area b in Fig. 6
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