Renguang Zuo

Mapping complexity of spatial distribution of faults using fractal and multifractal models: vectoring towards exploration targets

In this paper, fractal and multifractal analyses are demonstrated as effective tools for mapping complexity in the spatial distribution of faults. Faults within the eastern part of Gejiu mining area, Yunnan province, west southern China were chosen to demonstrate mapping of the complexity of their spatial distributions using fractal and multifractal models. The results show that (1) the fractal dimensions of the spatial distributions of all faults, NW-trending faults, and NE-trending faults are 1.68, 1.49, and 1.42, respectively, indicating differences in spatial distributions of different sets of faults; (2) the fractal dimensions of the spatial distributions of faults in the four Sn fields in the Gejiu mining district, namely Malage, Gaosong, Laochang, and Kafang (arranged in the order of increasing proportions of surface-projected areas of Sn orebodies) are 1.38, 1.57, 1.65, and 1.41, respectively; and (3) complexity of the spatial distributions of faults, represented by fractal dimension, correlates well with surface-projected areas of Sn orebodies, and lengths of faults satisfy the multifractal statistical and singularity index @a, which can be used to quantify the complexity of the spatial distributions of faults.

Recognition of geochemical anomalies using a deep autoencoder network

In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability distributions. During the training, small probability samples contribute little to the autoencoder network. These samples can be recognized by the trained model as anomalous samples due to their comparatively higher reconstructed errors. The southwestern Fujian district (China) is chosen as a case study area. A variety of learning rates, iterations, and the size of each hidden layer are constructing and training the deep autoencoder networks on all the geochemical samples. The reconstruction error (or, anomaly score) of each training sample is used to recognize multivariate geochemical anomalies associated with Fe polymetallic mineralization. By comparing the results obtained with a continuous restricted Boltzmann machine, we conclude that the autoencoder network can be trained to recognize multivariate geochemical anomalies. Most of the known skarn-type Fe deposits are located in areas with high reconstruction errors or anomaly scores in the anomaly map, indicating that these anomalies may be related to Fe mineralization. Recognition of geochemical anomalies using an autoencoder network.Recognition of geochemical anomalies using continuous restricted Boltzmann machines.Methods demonstrated in a case study southwestern Fujian district (China).

Identifying gravity anomalies caused by granitic intrusions in Nanling mineral district, China: a multifractal perspective

Several power-law relationships of geophysical potential fields have been discussed recently with renewed interests, including field value–distance ( f − r ) and power spectrum–wavenumber (P − ω) models. The singularity mapping technique based on the density/concentration–area (C–A) power-law model is applied to act as a highpass filter for extracting gravity and magnetic anomalies regardless of the background value and to detect the edges of gravity or magnetic sources with the advantage of scale invariance. This is demonstrated on a synthetic example and a case study from the Nanling mineral district, Southern China. Compared with the analytic signal amplitude and total horizontal gradient methods, the singularity mapping technique provides more distinct and less noisy boundaries of granites than traditional methods. Additionally, it is efficient for enhancing and outlining weak anomalies caused by concealed granitic intrusions, indicating that the singularity method based on multifractal analysis is a potential tool to process gravity and magnetic data.

A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity

Weights of evidence and logistic regression are two of the most popular methods for mapping mineral prospectivity. The logistic regression model always produces unbiased estimates, whether or not the evidence variables are conditionally independent with respect to the target variable, while the weights of evidence model features an easy to explain and implement modeling process. It has been shown that there exists a model combining weights of evidence and logistic regression that has both of these advantages. In this study, three models consisting of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression are compared with each other for mapping mineral prospectivity. The modified fuzzy weights of the evidence model retains the advantages of both the fuzzy weights of the evidence model and the logistic regression model; the advantages being (1) the predicted number of deposits estimated by the modified fuzzy weights of evidence model is nearly equal to that of the logistic regression model, and (2) it can deal with missing data. This method is shown to be an effective tool for mapping iron prospectivity in Fujian Province, China.

Spatial analysis of Fe deposits in Fujian Province, China: Implications for mineral exploration

Spatial point pattern statistics, fractal analysis and Fry analysis in support of GIS were applied to explore the spatial distribution characteristics of mineral deposits and the spatial relationships between mineralization and geological features in Fujian Province (China). The results of Ripley’s K(r) revealed a clustered distribution of Fe deposits in space with a fractal dimension of 1.38. Fry analysis showed that Fe deposits distributed mainly along a NNE-NE trend. Buffer analysis showed that most of the known Fe deposits developed within 4 km buffer zones of the NNE-NE-trending faults, Yanshanian intrusions, and Late Paleozoic marine sedimentary rocks and the carbonate formations (C–P Formation), indicating that they possibly control the spatial distribution of Fe mineralization. This is possibly because the NNE-NE-trending faults, Yanshanian intrusions, and C–P Formation provided pathways of fluids, energy and a part of metal, and zones of deposition for the Fe mineralization, respectively. The fractal relation of the number of Fe deposits occurring within the buffer zones of geological features was observed. The fractal dimension suggested that the significance of Yanshanian intrusions and C–P Formation are greater than that of NNE-NE-trending faults in controlling the formation of Fe mineralization. These findings are useful for better understanding the formation of the mineralization and provide significant information for further mineral exploration.

Spatial patterns of geochemical elements measured on rock surfaces by portable X-ray fluorescence: application to hand specimens and rock outcrops

Spatial distribution of geochemical elements at hand-specimen and outcrop scale provides significant evidence for the processes of formation and alteration of rocks. Portable X-ray fluorescence (pXRF) analyses is a non-destructive and cost-effective methodology to rapidly measure multi-elemental concentrations in-situ, and its application makes the study of the distribution of geochemical elements efficient. In this paper two pXRF instruments, the Tianrui EDX-P730S and the Niton XL3t 950, were employed for measuring geochemical data for two samples of Pb and Zn ore hand specimens and at outcrop scale in an area of skarn formation. The data are processed by GIS and principal component analysis (PCA) for interpreting element associations involved in mineralization processes. The first three principal components obtained by PCA for the data measured on the rock surface of the two hand specimens may represent early stage Zn-dominated mineralization, superimposed Zn and Pb mineralization and late stage Pb-dominated mineralization. These three zones of mineralization can be delineated by the scores of multiple elements on the first three principal components. The first four principal components obtained from the outcrop surface data were found to be related to the marble, diorite, and two stages of skarn-type mineralization (Cu and Zn mineralization and Pb, Zn and Fe mineralization).

Identification and mapping of lithogeochemical signatures using staged factor analysis and fractal/multifractal models

To identify geochemical signatures related to deposit-type sought, staged factor analysis (SFA), fractal and multifractal methodologies were utilized to process the lithogeochemical data in the Laochang district, Yunnan province, SE China. In this study, 13 elements, including W, Mo, Pb, Zn, Ag, As, Bi, Hg, Sb, Sn, Cu, Cd and Mn, were analyzed in 825 rock samples. A centred logratio (clr) transformation was applied to address the closure problem of geochemical data. The results clearly demonstrate that SFA greatly assisted with not only interpretation of geochemical zonation patterns genetically related to intrusions, but also extraction of multi-element associations signature of the mineral deposit-type sought. The geochemical distribution patterns can be characterized by multifractal parameters such as singular exponent α, as well as τ″(1) and Δα, which are effective for depicting the spatial heterogeneity signatures of geochemical data. Through geochemical mineralization prospectivity indices (GPMI) and C-A model, the results illustrate that element distribution signatures are greatly influenced by fault density. The integrated methods are useful for identifying significant multi-element anomalous signature and generating reliable target areas for further prospecting.

Mapping spatial distribution characteristics of lineaments extracted from remote sensing image using fractal and multifractal models

Mapping mineral prospectivity in vegetated areas is a challenge. For this reason, we aimed to map spatial distribution characteristics of linear structures detected in remote sensing images using fractal and multifractal models. The selected study area was the Pinghe District of the Fujian Province (China), located in the Shanghang-Yunxiao polymetallic and alunite ore belt (within the Wuyishan polymetallic belt), where mineral resources such as copper, molybdenum, gold, silver, iron, lead, zinc, alunite and pyrophyllite have been discovered. The results of our study showed that: (1) the values of fractal dimension for all lineaments, NW-trending lineaments, and NE-trending lineaments, are 1.36, 1.32, and 1.23, respectively, indicating that these lineaments are statistically self-similar; (2) the fractal dimensions of the spatial distribution of the linear structures in the four selected hydrothermal-type ore deposits of the Pinghe District, named Zhongteng, Panchi, Xiaofanshan and Fanshan, are 1.43, 1.52, 1.37 and 1.37, respectively, which are higher than the mean value in South China; (3) the spatial distribution of the linear structures extracted from the remote sensing image and displayed by the contour map of fractal dimensions, correlates well with the known hydrothermal ore deposits; and (4) the results of the anomaly map decomposed by the spectrum-area (S-A) multifractal model is much better than the original fractal dimension contour map, which showed most of the known hydrothermal-type deposits occur in the high anomalous area. It is suggested that a high step tendency possibly matches with the boundary of the volcanic edifice and the deep fault controlling the development of the rock mass and the volcanic edifice. The complexity of the spatial distribution of mapped lineations (faults) in the Pinghe District, characterized by high values in the anomaly map, may be associated with the hydrothermal polymetallic ore mineralization in the study area.

Identification of geochemical anomalies through combined sequential Gaussian simulation and grid-based local singularity analysis

Abstract Local singularity analysis (LSA) has been proven to be an effective tool for identifying weak geochemical anomalies. The common practice of grid-based LSA is to firstly interpolate irregularly distributed observations onto a raster map by using either kriging or inverse distance weighting (IDW). The inherent nature of the weighted moving averaging of these methods typically subjects the interpolated map to a smoothing effect. Additionally, the traditional procedure did not allow for uncertainties on the values of geochemical attributes at unsampled locations. As such, these two aspects might affect LSA results. This paper presents a hybrid method, which combines sequential Gaussian simulation and grid-based LSA to identify geochemical anomalies. A case study of processing soil samples collected from the Jilinbaolige district, Inner Mongolia, China, further illustrates the hybrid method and helps compare the results with those from kriging-based LSA. The findings indicate that (1) the uncertainties of values at unsampled locations could affect the results of grid-based LSA, and (2) singularity exponents from kriging-based LSA roughly represent the trend (median) of singularity exponent distributions from simulation-based LSA, but the latter can also provide a measure of uncertainty of singularity exponent propagated from the uncertain values at unsampled locations, and (3) the procedure combining simulation-based LSA and analysis of distance is a feasible way for identifying geochemical anomalies with uncertainty being considered. The anomaly probability map obtained can provide a more generalized perspective than interpolation-based LSA to delineate anomalous areas.

GIS-based rare events logistic regression for mineral prospectivity mapping

Mineralization is a special type of singularity event, and can be considered as a rare event, because within a specific study area the number of prospective locations (1s) are considerably fewer than the number of non-prospective locations (0s). In this study, GIS-based rare events logistic regression (RELR) was used to map the mineral prospectivity in the southwestern Fujian Province, China. An odds ratio was used to measure the relative importance of the evidence variables with respect to mineralization. The results suggest that formations, granites, and skarn alterations, followed by faults and aeromagnetic anomaly are the most important indicators for the formation of Fe-related mineralization in the study area. The prediction rate and the area under the curve (AUC) values show that areas with higher probability have a strong spatial relationship with the known mineral deposits. Comparing the results with original logistic regression (OLR) demonstrates that the GIS-based RELR performs better than OLR. The prospectivity map obtained in this study benefits the search for skarn Fe-related mineralization in the study area. GIS-based rare events logistic regression was used to map mineral prospectivity.The importance of evidence variables can be measured by regression coefficient.A case study from southwestern Fujian district of China was conducted.