Mineral exploration modeling and singularity analysis for geological feature recognition and mineral potential mapping in southeastern Yunnan mineral district, China

Nowadays, with the development in construction of geo-exploratory datasets and data processing techniques, mineral exploration modeling for recognition of mineralization associated geological features and mapping of mineral potentials become more dependent on GIS-based analysis and geo-information from multi-source datasets. Geological, geochemical and geophysical data as three main sources of geo-information in support of mineral exploration have long been employed in many researches. Spatial distributions of geological bodies or controlling factors associated with mineralization were frequently interpreted from these datasets. However, former characterizations of the controlling factors were simply focused on their location information; concerns on spatial variations of their geological signatures and controlling effects on mineralization were not sufficiently emphasized. Therefore, through a series of newly developed GIS-based manipulations, current study intends to demonstrate a comprehensive mineral exploration modeling process for more explicit recognition of controlling factors and their interactions on mineralization and delineation of hydrothermal mineral potentials in southeastern Yunnan mineral district, China. The hydrothermal mineralization as a nonlinear geo-process is accompanied with anomalous energy release and material accumulation in a narrow spatial-temporal interval. Simultaneously, it is a cascade process associated with various geological activities (e.g., magmatism, tectonism, etc.). Knowledge of these associated geo-activities is consequently beneficial to the exploration of hydrothermal mineralization. As the key point of this study, the singularity index mapping method in the context of fractal/multifractal efficient in separating geo-anomalies from both strong and weak background is applied to characterize variations of geological signatures of three controlling factors (i.e., granitic intrusions, faults and the Gejiu formation). With the guidance of multidisciplinary approaches, these geo-information derived from multi-source datasets is further integrated to produce the potential map. In comparison with traditionally used methods, the newly depicted predictor maps enhance weak geo-anomalies hidden within a strong variance of background. In addition, three geo-information integration methods including RGB composition, the principal component analysis and the weights of evidence method are implemented. By the weights of evidence method, the qualitatively and quantitatively interpretable result possessing advantages of the other two methods, simultaneously, is accepted as the final result of currently proposed mineral exploration modeling. Summarized experiences through this study will not only support future exploration in the study area, but also benefit the work in other areas

Geo-information identification for exploring non-stationary relationships between volcanic sedimentary Fe mineralization and controlling factors in an area with overburden in eastern Tianshan region, China

GIS-based spatial analysis has been a common practice in mineral exploration, by which mineral potentials can be delineated to support following sequences of exploration. Mineral potential mapping is generally composed of geo-information extraction and integration. Geological anomalies frequently indicate mineralization. Volcanic sedimentary Fe deposits in eastern Tianshan mineral district, China provide an example of such an indication. However, mineral exploration in this area has been impeded by the desert coverage and geo-anomalies indicative to the presence of mineralization are often weak and may not be efficiently identified by traditional exploring methods. Furthermore, geological guidance regarding to spatially non-stationary relationships between Fe mineralization and its controlling factors were not sufficiently concerned in former studies, which limited the application of proper statistics in mineral exploration. In this dissertation, geochemical distributions associated with controlling factors of the Fe mineralization are characterized by various GIS-based spatial analysis methods. The singularity index mapping technique is attempted to separate geochemical anomalies from background, especially in the desert covered areas. Principal component analysis is further used in integrating the geochemical anomalies to identify geo-information of geological bodies or geological activities associated with Fe mineralization. In order to delineate mineral potentials, spatially weighted principal component analysis with more geological guidance is tried to integrate these identified controlling factors. At the end, as the first time been introduced to mineral exploration, a geographically weighted regression method is currently attempted investigate spatially non-stationary interrelationships presented across the space. Based on the results, superimposition of these controlling factors can be qualitatively and quantitatively summarized that provides a constructive geo-information to Fe mineral exploration in this area. From the practices in this dissertation, GIS-based mineral exploration will not only be efficient in mapping mineral potentials but also be supportive to strategies making of following mineral exploration. All of these experiences can be suggested to future mineral exploration in the other regions

Environmental geochemistry of Potentially Toxic Elements (PTEs) and Persistent Organic Pollutants (POPs) as a tool of exposure evaluation and chemical risk assessment

Environmental pollution is one of the most challenging environmental issues to tackle due to its impact to human health and the ecosystem. One of the main objectives of environmental geochemistry is to investigate, characterise, and reveal the patterns of organic compounds and inorganic elements and further unveil their possible sources. Geogenic features and anthropogenic activities are the main sources of environmental contamination which are likely to release these contaminants into atmospheric, soil and water media. Moreover, anthropogenic activities let out chemicals produced from industrial activities, domestic, livestock and municipal wastes (including wastewater), agrochemicals, and petroleum-derived products. Organic pollutants cover a large group of synthetized pollutants and Persistent Organic Pollutants (POPs) have received a specific attention due to their physico-chemical properties, high toxicity, and subject to long-range atmospheric transfer. Polychlorinated biphenyls (PCBs), Polycyclic Aromatic Hydrocarbons (PAHs) and Organochlorines Pesticides (OCPs) are the main POPs that are subject to different regulation schemes to their irreversible adverse effects to both human and wildlife health. Stockholm Convention, Rotterdam and Basel, World Health organisation (WHO) and United Nations Economic Commission for Europe POPs Protocol have so far addressed, threated and introduced legislation which ban or fix threshold’s values of these POPs into environment. Potentially Toxic Elements (PTEs) are widespread metals/metalloids related to geogenic and/or anthropogenic activities. PTEs are one of the major concerns in the environment because their concentrations are increasing due to accelerated population growth rate, higher level of urbanisation and industrialisation providing a great variety of anthropogenic contamination/pollution sources. They have often been given special emphasis because their accumulation in different matrices can cause soil and land degradation and they can be transferred into the human body as a consequence of dermal contact, inhalation and ingestion through food chain and drinking water. PTEs are generally non-biodegradable having long biological half-lives and tend to accumulate in soils being absorbed to clay minerals and organic matter. However, their bioavailability is influenced by different physicochemical processes (e.g. pH, Eh) and physiological adaptation. PTEs and POPs can be observed in different environmental media but soil is considered an important reservoir due to its physico-chemical properties which confer high retention capacity of these pollutants. Soil contamination has been increasing worldwide and has become the focus of attention in recent years. Several soil parent materials are natural sources of certain organic contaminants, elements, and these can pose a risk to the environment and human health at elevated concentrations. For that, various geostatistical computations have been used to identify source patterns of different pollutants related to underlying geological features and/or anthropogenic activities, and to further distinguish mineralisation from contamination. Several single and complex contamination/mineralisation indices such as Enrichment Factor, Geo-accumulation Index or Single Pollution Index have been elaborated to quantify the contamination or mineralisation status of different PTEs. They are generally based on intervention limits (thresholds) or background/baseline values of a single element based on National Legislation, as a reference. Indices based on intervention limits (thresholds) are easily interpretable and comparable, but they disregard the compositional nature of geochemical data; hence they can be biased and/or spurious. This PhD research project reveals novel geostatistical computations that will lay out sources patterns of Potentially Toxic Elements (PTEs) and Persistent Organic Pollutants (POPs), and assess the soils contamination levels in the central-southern Italy. Series of follow up studies have provided an invaluable baseline for these contaminants distribution in Italy to push towards an institutional response for more adequate regulation of these pollutants worldwide. A further ongoing research project is currently investigating the content and bioavailability of mercury and Potentially Toxic Elements (PTEs) in artisanal and small-scale gold mining (ASGM) districts of Kedougou (Senegal). This study in particular will represent a fundamental stepping stone to build a baseline review of PTEs in ASGM of Kedougou (Senegal) and evaluate human health risks from exposure of PTEs. It is envisaged that the results of this study should trigger more detailed surveys in contaminated areas as well as ad-hoc risk-based studies, which in the long-term will constitute a strong argument to cause an adequate institutional response by the Senegalese regulating authorities for a full application the Minamata convention

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution

Development and evaluation of models for assessing geochemical pollution sources with multiple reactive chemical species for sustainable use of aquifer systems: source characterization and monitoring network design

Michael designed a groundwater flow and reactive transport optimization model. He applied this model to characterize contaminant sources in Australia's first large scale uranium mine site in the Northern Territory. He identified the contamination sources to the groundwater system in the area. His findings will assist planning actions and steps needed to implement the mitigation strategy of this contaminated aquifer

Geochemical and Hydrothermal Alteration Patterns of the Abrisham-Rud Porphyry Copper District, Semnan Province, Iran

In this study, the zonality method has been used to separate geochemical anomalies and to calculate erosional levels in the regional scale for porphyry-Cu deposit, Abrisham-Rud (Semnan province, East of Iran). In geochemical maps of multiplicative haloes, the co-existence of both the supra-ore elements and sub-ore elements local maxima implied blind mineralization in the northwest of the study area. Moreover, considering the calculated zonality indices and two previously presented geochemical models, E and NW of the study have been introduced as ZDM and BM, respectively. For comparison, the geological layer has been created by combining rock units, faults, and alterations utilizing the K-nearest neighbor (KNN) algorithm. The rock units and faults have been identified from the geological map; moreover, alterations have been detected by using remote sensing and ASTER images. In the geological layer map related to E of the study area, many parts have been detected as high potential areas; in addition, both geochemical and geological layer maps only confirmed each other at the south of this area and suggested this part as high potential mineralization. Therefore, high potential areas in the geological layer map could be related to the mineralization or not. Due to the incapability of the geological layer in identifying erosional levels, mineralogy investigation could be used to recognize this level; however, because of the high cost, mineralogy is not recommended for application on a regional scale. The findings demonstrated that the zonality method has successfully distinguished geochemical anomalies including BM and ZDM without dependent on alteration and was able to predict erosional levels. Therefore, this method is more powerful than the geological layer

Development and Application of Structural Equation Modeling Method For Geochemical Data Analysis

A new Structural Equation Modeling (SEM) approach was proposed and the corresponding algorithms were designed and implemented for model estimation and evaluation in this research. By way of contrast to traditional SEM methods which focus on confirmatory analysis, the new SEM approach is mainly designed for exploratory analysis, which has plenty of applications in geoscience data processing and interpretation. In order to generate an initial model for the new SEM analysis, a constrained variable clustering method was proposed based on a new index representing a type of conditional correlation, which was defined and calculated through SEM. Differently from the conventional conditional correlation coefficient, the new index was designed for measuring the quantity/percentage of the variance existing in two variables related to a response variable, rather than the level of independency of the two variables conditioned by a response variable. It can be used in Principal Component Analysis (PCA) and Factor Analysis (FA) for extracting factors restricted by a response variable. Thereby, these PCA and FA can be considered as constrained PCA and FA. The programs designed for the new SEM are model parameters estimation, conditional correlation coefficient calculation, clustering analysis, and the SEM-based Weights of Evidence (WofE) modeling. The new SEM technology was applied to a lake sediment geochemical dataset to assist for identification of multiple geochemical factors related to gold mineralization in a study area located in Southern Nova Scotia, Canada. The model was further applied in conjunction with the WofE method to integrate geochemical and geological information in mapping mineral potential in the same study area. The results showed that the application of the new SEM method could reduce the effect of the conditional dependency of the evidences involved in WofE

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences