Geoanomaly-based Mineral Resources Quantitative Prediction And Uncertainty Evaluation

Given the increasing difficulties in exploring for mineral resources, improving exploration efficiency has become a major objective. The traditional exploration theories and methods are useful but also a major factor impeding mineral resources exploration. Therefore, we need to develop new geological exploration theory and methods to gain a better understanding of the processes that control ore body formation and mineralization, spatial and temporal rules of deposit distributions, and the economic properties of deposits. We also need to improve prospecting techniques so as to identify various indicators of mineralization, as well as software packages that can effectively identify, manipulate, analyze, and interpret massive mineralization information. Identification of deep and hidden information is not only a technical issue, but also an important scientific concern, such as the fundamental theory, identification of weak and complex information, and separation of hybrid data. Undoubtedly, these problems are worldwide and constrain the development of today's breakthroughs in mineral resources exploration. The geological anomaly theory proposed by Zhao (1991) breaks through the bound of the "similar analogy" theory, and has gradually led to a new theory and method of "Three Components" , which consists of geological anomaly, diversity of mineralization, and the spectrum of mineral deposits (Zhao et al., 2001) . The theory and method have been widely used for predicting solid mineral resources and oil and gas exploration, and have gained wide application to information from ore fields in Shandong and Yunnan Province, China.In recent years, mineral resources prospecting has encountered two new situations: (1) There is a shortage of traditional mineral resources, especially for strategic and support commodities such as oil, iron, manganese, chromium, and potassium, resulting in a group of crisis mines and crisis mine cities; and (2) many undiscovered mineral deposits are difficult to identify, detect, and use. At present, we focus mainly on finding the deep and difficult-to-identify mineral deposits, especially in better explored areas such as in mid-China and eastern China. The identification, extraction and delineation of new, hidden, and deep ore-forming information play an important role in the prediction and assessment of mineral resources. It is important therefore to develop an effective tool to identify, extract, and delineate geological anomalies based on GIS under the guidance of "Three Components Prediction Theory and Methods". In addition, mineral resources prediction and assessment entail high risk and uncertainty because of the complexity and variability of geological objects, such as the diversity of mineral deposit types, the complexity of mineral deposit genesis, the implicitness of mineral deposit controlling factors, and the non-unique understanding of exploration information and because most mineral deposits are located subsurface at variable depths. How to identify the sources of uncertainty and how to improve the efficiency of exploration is not only a goal of all geologists but also a major scientific issue. Only through a comprehensive analysis of the sources of uncertainty in mineral resources prediction and through the quantitative evaluation of uncertainty can we find the approach and method to reduce risks and increase efficiency in exploration.This research is supported by national mineral resources potential prediction and assessment. Using "Three Components Prediction Theory and Methods" and geological anomaly as a guide, I conduct research on the geological and mathematical foundation of geological anomaly, the technology and method to identify, extract, and delineate the new, hidden, and deep ore-forming information, and the uncertainty in mineral resources prediction and assessment; I also construct a mineral resources prediction and assessment model based on geological anomalies. For demonstration purposes, the Gangdese porphyry copper belt will be studied as an example.The following conclusions can be obtained from these studies:(1) Discussion of the geological and mathematical foundation of geological anomalyGeological anomaly is the product of the evolution of and the interaction between processes affecting the Earth's geological layers during different geological periods. The features of any geological anomaly and the type and size of mineral resources are determined by rock age, tectonic setting, geological environment, and rock type. With the evolution of geological history, the early formation of the geological anomalies will evolve. Therefore, the geological anomalies have an evolutionary sequence in space and time. All the geological characteristics related to mineralization, including the conditions of mineralization and spatial and temporal ore-controlling factors, are represented as the geological anomaly events in the process of geological evolution. Therefore, the foundation of geological anomaly is the geological event, and the geological anomaly is the result of the succession of geological events.Extreme value and anomaly value in geology can be regarded as geological anomalies, and their values often occur in the tail of the frequency distribution. Extreme analysis is used to quantify random characters at either ultra-large or small scale, and to estimate the probability of extreme events. Therefore, geological anomaly falls within the scope of extreme value theory.(2) Research on the method to identify, extract, and delineate geological anomaly, and development of the geoanomaly-based mineral resources prediction and assessment systemSome indicators of ore, such as space morphology and spatial configuration features, are presented to identify geological anomalies; some methods, including extreme value theory (EVT, Fuzzy mathematic, and concentration-area(C-A), are used to delineate the geological anomalies; and some methods, such as evidence of weights and fuzzy logic, are used to integrate multi-variables. Then the GIS-based mineral resources prediction and evaluation are constructed. The system, comprising eight modules, for geological anomaly identification and extraction, variables transformation, mineral resources prediction and assessment, evaluation of uncertainty, etc., has paved the main information course in mineral resources prediction and assessment. These are undoubtedly helpful to extract and delineate the new, hidden, and deep-mineralization information and evaluate the uncertainty.(3) Set up the main flow of uncertainty evaluation in the mineral resources prospectingThe main sources of uncertainty arise from two factors. The first is known as the geological uncertainty, including the variability and complexity of natural phenomena. The second known as the process of mineral resources prediction and assessment. The uncertainty is transformed from the previous stage to the next stage, resulting in a considerable amount of uncertainty accumulation and dissemination. The uncertainty can be classified as the uncertainty in mineral resources location prediction and the uncertainty in mineral resources potential prediction. Both of the uncertainties have been categorized into the uncertainty of spatial data, the uncertainty of prediction model, the uncertainty of undiscovered deposits, and the uncertainty of grade and tonnage. All these uncertainties are introduced in details and evaluated by fuzzy sets, where the fuzzy numbers are used to express the reliability, probability, and variance of the results. Then the expression and propagation model of uncertainty, and some methods to reduce the uncertainty in mineral resources exploration, are proposed. Based on previous research, the main flow of the uncertainty evaluation in the mineral resources prediction was set up, including the sources and classification of uncertainty, evaluation of uncertainty, the expression and propagation of uncertainty, and how to reduce the uncertainty.(4) Preliminary study of uncertainty evaluation in geological qualitative dataTaking the "National mineral resources database" as an example, the types of data fields and storage space are calculated. The results show that the qualitative data are still dominant in the massive data. Therefore, how to evaluate uncertainty in qualitative data is vital for mineral resources exploration. The qualitative data can be classified into two types. The first relates to description type, which is evaluated by using operators of the linguistic variables. The second is codes type, which is evaluated by qualitative sorting and quantitative transformation. Two examples, permissive strata and faults, demonstrate these methods.(5) Mineral resources prospecting, and evaluating its uncertainty for Gangdese porphyry copper depositsBasic spatial databases, including geology database, ore deposit database, geophysics database, geochemistry database, and remote sensing database, were established at the scale of 1:500,000, and the geoanomaly-based mineral resources prediction and assessment system was used to identify and extract geological anomalies, and to integrate multi-geovariables and evaluate the uncertainty. The results demonstrate that (1) the mapping singularity technique is a useful tool to separate weak anomalies from complex background; (2) asymmetric fuzzy association analysis can uncover both direct and indirect relations between variables, which generally more closely reflects the real relationships between geosciences variables, and can lead to better results; (3) the multilevel fuzzy comprehensive evaluation can efficiently efficiently integrate multilevel and multi-sources variables and handle uncertainty due to vagueness of classification in mineral prospectivity mapping.In a word, this dissertation mainly focuses on the theroy and method of both geoanomaly-based and uncertainty evaluation in mineral resources prediction and assessment. For the theory and method of geological anomaly, this paper (1) discusses the geological and mathematical foundation of geological anomaly; (2) develope of the methods to identify, extract, and delineate the new, hidden, and deep ore-forming information; and (3) develop geoanomaly-based mineral resources prospecting system. For the theory and method of uncertainty evaluation, this paper (1) sets up the main information flow for the evaluation of uncertainty in mineral resources prospecting and (2) discusses how to evaluate the uncertainty. The Gangdese porphyry copper belt in Tibet is then chosen as a study area to demonstrate that the geoanomaly-based mineral resources prospecting system is a practical and operational tool to map prospectivity and evaluate its uncertainty.