On reviewing the characteristics of deep mineral exploration, this article elaborates on the necessity of employing quantitative prediction to reduce uncertainty. This is caused by complexity of mineral deposit formational environments and mineralization systems as increase of exploration depth and incompleteness of geo-information from limited direct observation. The authors wish to share the idea of "seeking difference" principle in addition to the "similar analogy" principle in deep mineral exploration, especially the focus is on the new ores in depth either in an area with discovered shallow mineral deposits or in new areas where there are no sufficient mineral deposit models to be compared. An on-going research project, involving Sn and Cu mineral deposit quantitative prediction in the Gejiu (个旧) area of Yunnan (云南) Province, China, was briefly introduced to demonstrate how the "three-component" (geoanomaly-mineralization diversity-mineral deposit spectrum) theory and non-linear methods series in conjunction with advanced GIS technology, can be applied in multi-scale and multi-task deep mineral prospecting and quantitative mineral resource assessment.
Spatial distribution patterns of element concentrations can reflect the information of the mineralization processes. Both the Hurst exponent calculated by R/S analysis and the generalized fractal dimension calculated by using the multifractal model are important parameters for describing the spatial distribution of elements. Five long drill holes, named as M1, S1, S2, S3, and S4, have been selected in the Shizishan (狮子山) skarn orefield in Tongling (铜陵), Anhui (安徽) Province, China. Marbles are well developed around M1 and skarn rocks are largely distributed along S1, S2, S3, and S4 drill holes. The drill holes were sampled evenly with an interval of 10 m and 16 trace elements have been measured. The mean of the ΔD (q) (the height of the generalized dimension spectrum) in the M1 drill hole is the lowest. In addition, the mean of the Hurst exponents of the 16 elements in the M1 drill hole is also much smaller than that of S1, S2, S3, S4 drill holes, which is in accordance with the analysis of the generalized dimension. It is indicated by the generalized dimension and Hurst exponent that the distribution of trace elements in the marbles is more random than that in the skarn. The result suggests that the mineralization process can change the randomness and persistence features of the element distribution.
The properties of feldspar and quartze are studied in this article from a fractal point of view using gray-scale micro-images of granite samples collected at the Fangshan (房山) granite body in Hebei (河北) Province, China, which can be regarded as an ideal granite in the sense of Vistelius. We found that there exist power-law relationships between the eigenvalues of the gray-scale matrices and their ranks for the feldspar and quartz. The fractal model used here is a λ-R model similar to the N-λ model proposed by Qiuming Cheng in 2005. Meanwhile, we found that average variances for the gray-scale matrices of feldspar are larger than those of quartz on the same sections, and this may be useful for auto-identification of feldspar and quartz as well as other minerals.
The separation of anomalies from geochemical background is an important part of data analysis because lack of such identifications might have profound influence on or even distort the final analysis results. In this article, 1 672 geochemical analytical data of 11 elements, including Cu, Mo, Ag, Sn, and others, from a region within Tibet, South China, are used as one example. Together with the traditional anomaly recognition method of using the iterative mean ±2σ, local multifractality theory has been utilized to delineate the ranges of geochemical anomalies of the elements. To different degrees, on the basis of original data mapping, C-A fractal analysis and singularity exponents, Sn differs from the other 10 elements. Moreover, geochemical mapping results based on values of the multifractal asymmetry index for all elements delineate the highly anomalous area. Similar to other 10 elements, the anomalous areas of Sn delineated by the asymmetry index distribute along the main structure orientations. According to the asymmetry indexes, the 11 elements could be classified into 3 groups: (1) Ag and Au, (2) As-Sb-Cu-Pb-Zn-Mo, and (3) Sn-Bi-W. This paragenetic association of elements can be used to interpret possible origins of mineralization, which is in agreement with petrological analysis and field survey results.
Dongguan (东莞) City, located in the Pearl River Delta, South China, is famous for its rapid industrialization in the past 30 years. A total of 90 topsoil samples have been collected from agricultural fields, including vegetable and orchard soils in the city, and eight heavy metals (As, Cu, Cd, Cr, Hg, Ni, Pb, and Zn) and other items (pH values and organic matter) have been analyzed, to evaluate the influence of anthropic activities on the environmental quality of agricultural soils and to identify the spatial distribution of trace elements and possible sources of trace elements. The elements Hg, Pb, and Cd have accumulated remarkably here, incomparison with the soil background content of elements in Guangdong (广东) Province. Pollution is more serious in the western plain and the central region, which are heavily distributed with industries and rivers. Multivariate and geostatistical methods have been applied to differentiate the influences of natural processes and human activities on the pollution of heavy metals in topsoils in the study area. The results of cluster analysis (CA) and factor analysis (FA) show that Ni, Cr, Cu, Zn, and As are grouped in factor F1, Pb in F2, and Cd and Hg in F3, respectively. The spatial pattern of the three factors may be well demonstrated by geostatistical analysis. It is shown that the first factor could be considered as a natural source controlled by parent rocks. The second factor could be referred to as "industrial and traffic pollution sources". The source of the third factor is mainly controlled by long-term anthropic activities, as a consequence of agricultural activities, fossil fuel consumption, and atmospheric deposition.
A series of geochemical anomalies of Pt and Pd were found in 1 358 recombined samples from a geochemical stream sediment survey in eastern Yunnan (云南) Province, China. Chemical optical emission spectroscopy, X-ray fluorescence analysis, and inductively coupled plasmas atomic emission spectrometry analyses of 22 elements and chemical compositions of 21 samples from coal-bearing strata from the Late Paleozoic, Mesozoic, and Cenozoic show Pt and Pd concentrated to some extent in coal rocks, with Pd/Pt < 1. As, Pt, B, Au, Pd, V, Sb, U, Pb, and W are enriched in the Lower Carboniferous coal-bearing strata of the Wanshoushan (万寿山) Formation; B, Mo, As, Pt, U, W, Pb, Pd, and V are enriched in the Lower Permian coal-bearing strata of the Liangshan (梁山) Formation; Pt, Cu, Mo, Pd, As, V, and Ag are enriched in the Upper Permian coal-bearing strata of the Xuanwei (宣威) Formation; As, B, Pb, Pt, Pd, U, W, Sb, Mo, Zn, and Ag are enriched in the Upper Triassic coal-bearing strata of the Xujiahe (须家河) Formation; and Pt, As, and Pb are enriched in the lignite of the Pliocene Ciying (茨营) Formation. Combining analyses of the sedimentary environment and local volcanic activity reveal that the coal- bearing strata in the Xuanwei Formation are possibly related to the Permian Emeishan (峨眉山) basalt.
The Gejiu (个旧) deposit is a superlarge tin-copper polymetallic ore-forming concentration area characterized by excellent metallogenic geological settings and advantageous ore-controlling factors. The deposit displays diverse mineralization properties due to different minerals and mineral deposit types. Based on the principal metallogenic factors, metallogenic mechanisms, mineralized components, and occurrence of mineral deposits or ore bodies, the Gejiu mineral district can be divided into 2 combinations of metallogenic series, 4 metallogenic series, 8 subseries, and 27 mineral deposit types. Spatial zonality is evident. The distribution regularity of the elements in both plane and section is Be-W, Sn (Cu, Mo, Bi, Be) -Sn, Pb, Ag-Pb, Zn around a granitic intrusion. The metallogenic epoch is mainly concentrated in the late Yanshanian. During this period, large-scale metallogenic processes related to movement caused by tectonics and magmatism occurred, and a series of magmatic hydrothermal deposits formed. The ore-forming processes can be divided into 4 stages: the silicate stage, the oxide stage, the sulphide stage, and the carbonate stage. Based on the orderliness and diversity (in terms of time, space, and genesis) of the mineralization, the authors have developed a comprehensive spectrum of ore deposits in the Gejiu area. This newly proposed diversity of mineralization and the spectrum developed in this work are useful not only for interpreting the genesis of the Gejiu deposit but also for improving mineral exploration in the area, and in particular, for finding large deposits.
The Pulang (普朗) porphyry copper deposit, located in the southern segment of the Yidun-Zhongdian (义敦-中甸) island arc ore-forming belt of the Tethys-Himalaya ore-forming domain, is a recently discovered large copper deposit. Compared with the composition of granodiorite in China, the porphyry rocks in this area are enriched in W, Mo, Cu, Au, As, Sb, F, V, and Na2O (K1≥1.2). Compared with the composition of fresh porphyry rocks in this district, the mineralized rocks are enriched in Cu, Au, Ag, Mo, Pb, Zn, W, As, Sb, and K2O (K2≥1.2). Some elements show clear anomalies, such as Zn, Ag, Cu, Au, W, and Mo, and can be regarded as pathfinders for prospecting new ore bodies in depth. It has been inferred from factor analysis that the Pulang porphyry copper deposit may have undergone the multiple stages of alteration and mineralization: (a) Cu-Au mineralization; (b) W-Mo mineralization; and (c) silicification and potassic metasomatism in the whole ore-forming process. A detailed zonation sequence of indicator elements is obtained using the variability index of indicator elements as follows: Zn→Ag→Cu→Au→W→Mo. According to this zonation, an index such as (Ag×Zn)D/ (Mo×W)D can be constructed and regarded as a significant criterion for predicting the Cu potential at a particular depth.
The recently discovered Damoqujia (大磨曲家) gold deposit is a large shear zone-hosted gold deposit of disseminated sulphides located in the north of the Zhaoping (招平) fault zone, Jiaodong (胶东) gold province, China. In order to distinguish the temperature range of cluster inclusions from different mineralization stages and measure their compositions, 16 fluid inclusions and 5 isotopic geochemistry samples were collected for this study. Corresponding to different mineralization stages, the multirange peaks of quartz decrepitation temperature (250-270, 310-360 and 380-430℃) indicate that the activity of ore-forming fluids is characterized by multistage. The ore-forming fluids were predominantly of high-temperature fluid system (HTFS) by CO2-rich, and SO42--K+ type magmatic fluid during the early stage of mineralization and were subsequently affected by low-temperature fluid system (LTFS) of CH4-rich, and Cl--Na+/Ca2+ type meteoric fluid during the late stage of mineralization. Gold is transferred by Au-HS- complex in the HTFS, and Au-Cl- complex can be more important in the LTFS. The transition of fluids from deeper to shallow environments results in mixing between the HTFS and LTFS, which might be one of the most key reasons for gold precipitation and large-scale mineralization. The ore-forming fluids are characterized by high-temperature, strong-activity, and superimposed mineralization, so that there is a great probability of forming large and rich ore deposit in the Damoqujia gold deposit. The main bodies are preserved and extend toward deeper parts, thereby suggesting a great potential in future.
Located in the Qinling (秦岭) molybdenum metallogenic belt on the southern margin of North China craton, the Nannihu (南泥湖) molybdenum (-tungsten) ore field, consisting of the Nannihu, Sandaozhuang (三道幢), and Shangfang (上房) deposits, represents a superlarge skarn-porphyry molybdenum (-tungsten) accumulation. Outside the ore field, there are some hydrothermal lead-zinc-silver deposits found in recent years, for example, the Lengshuibeigou (冷水北沟), Yindonggou (银涧沟), Yangshuwa (杨树凹), and Yinhegou (银河沟) deposits. Ore-forming fluid geochemistry indicates that these deposits belong to the same metallogenic system. The hydrothermal solutions were mainly derived from primary magmatic water in the early stage and from the mixture of the primary magmatic water and meteoric water in the later stage, with an obvious decreasing tendency in temperature, salinity and gas-liquid ratio of fluid inclusions. Sulfur and lead isotope data show that the ore-forming substances and related porphyries were mainly derived from the lower crust, and a hidden magmatic chamber is indicated by aeromagnetic anomaly and drill hole data indicate that the Nannihu granite body extends to being larger and larger with depth increasing. The large-scale mineralization was the consequence of lithospheric extension during the late stage of the tectonic regime when the main compressional stress changed from NS-trending to EW-trending.
Weights of evidence (WofE) is an artificial intelligent method for integration of information from diverse sources for predictive purpose in supporting decision making. This method has been commonly used to predict point events by integrating point training layer and binary or ternary evidential layers (multiclass evidence less commonly used). Omnibus weights of evidence integrates fuzzy training layer and diverse evidential layers. This method provides new features in comparison with the ordinary WofE method. This new method has been implemented in a geographic information system-geophysical data analysis system and the method includes the following contents: (1) dual fuzzy weights of evidence (DFWofE), in which training layer and evidential layers can be treated as fuzzy sets. DFWofE can be used to predict not only point events but also area or line events. In this model a fuzzy training layer can be defined based on point, line, and areas using fuzzy membership function; and (2) degree-of-exploration model for WofE is implemented through building a degree of exploration map. This method can be used to assess possible spatial correlations between the degree of exploration and potential evidential layers. Importantly, it would also make it possible to estimate undiscovered resources, if the degree of exploration map is combined with other models that predict where such resources are most likely to occur. These methods and relevant systems were validated using a case study of mineral potential prediction in Gejiu (个旧) mineral district, Yunnan (云南), China.
Void ratio measures compactness of ground soil in geotechnical engineering. When samples are collected in certain area for mapping void ratios, other relevant types of properties such as water content may be also analyzed. To map the spatial distribution of void ratio in the area based on these types of point, observation data interpolation is often needed. Owing to the variance of sampling density along the horizontal and vertical directions, special consideration is required to handle anisotropy of estimator. 3D property modeling aims at predicting the overall distribution of property values from limited samples, and geostatistical method can be employed naturally here because they help to minimize the mean square error of estimation. To construct 3D property model of void ratio, cokriging was used considering its mutual correlation with water content, which is another important soil parameter. Moreover, K-D tree was adopted to organize the samples to accelerate neighbor query in 3D space during the above modeling process. At last, spatial configuration of void ratio distribution in an engineering body was modeled through 3D visualization, which provides important information for civil engineering purpose.
Alteration is regarded as significant information for mineral exploration. In this study, ETM+ remote sensing data are used for recognizing and extracting alteration zones in northwestern Yunnan (云南), China. The principal component analysis (PCA) of ETM+ bands 1, 4, 5, and 7 was employed for OH- alteration extractions. The PCA of ETM+ bands 1, 3, 4, and 5 was used for extracting Fe2+ (Fe3+) alterations. Interfering factors, such as vegetation, snow, and shadows, were masked. Alteration components were defined in the principal components (PCs) by the contributions of their diagnostic spectral bands. The zones of alteration identified from remote sensing were analyzed in detail along with geological surveys and field verification. The results show that the OH- alteration is a main indicator of K-feldspar, phyllic, and prophilized alterations. These alterations are closely related to porphyry copper deposits. The Fe2+ (Fe3+) alteration indicates pyritization, which is mainly related to hydrothermal or skarn type polymetallic deposits.
Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information. Therefore, there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery. In the current article, a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification. The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese Yellow River delta on August 8, 1999. The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier (MLC).
Monte-Carlo method is used for estimating coalbed methane (CBM) resources in key coal mining areas of China. Monte-Carlo method is shown to be superior to the traditional volumetric method with constant parameters in the calculation of CBM resources. The focus of the article is to introduce the main algorithm and the realization of functions estimated by Monte-Carlo method, including selection of parameters, determination of distribution function, generation of pseudo-random numbers, and evaluation of the parameters corresponding to pseudo-random numbers. A specified software on the basis of Monte-Carlo method is developed using Visual C++ for the assessment of the CBM resources. A case study shows that calculation results using Monte-Carlo method have smaller error range in comparison with those using volumetric method.
Several structure sets (faults and folds) are characterized by their self-similarity properties. Herein, we discuss the degrees of complexity of fractures by introducing the box-counting fractal dimension of faults as a key criterion to be used in comprehensive fuzzy analysis model for evaluation of the complexity of structures. Totally, eight criteria including density, intensity, length of faults, types and box-counting fractal dimension of faults, the intersection angle between faults and coal beds, gradient coefficients, dip angles of the coal beds, and variation coefficients of dip angles of the coal seams, were used for the evaluation purpose. The grey fuzzy comprehensive assessment model was used to rank the relative importance of these criteria. Scores indicating the complexity of structure were calculated on the base of criteria values and their weights for each sub-area of the study area in the Pansan (潘三) coal mine district in the southern Anhui (安徽) Province, China. The result on the calculated complexity of structure is useful for mining planning in the study area.