
Citation: | Pengda ZHAO, Qiuming CHENG, Qinglin XIA. Quantitative Prediction for Deep Mineral Exploration. Journal of Earth Science, 2008, 19(4): 309-318. |
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.
Quantitative prediction of mineral resources and delineation of undiscovered mineral deposits on the basis of regularity of mineralization and spatialtemporal distribution of mineral deposits at differen scales is an essential task in mineral exploration There have been numerous researchers who have attempted to develop theories, methods and techniques to support mineral resource assessment and prediction of undiscovered mineral deposits.
The main questions of mineral deposit prediction can be summarized as follows: (1) Are there any industrially valuable mineral deposits in a specific location? (2) If valuable mineral deposits exist in a specific location, what could be their sizes? (3) Wha are their likelihood locations?From quantitative mineral resource prediction studies, these questions can be associated with the following tasks: (1) prediction of mineral resource potential; (2) prediction of economic value of mineral resources; and (3) prediction of location of mineral deposits.These three aspects of prediction involve uncertainty so that the processes of these predictions aim for"optimal decision making under uncertain conditions"or for"choosing the best solution among various possible options to reach the optimal possible results".The objective of using quantitative approaches in prediction is to balance the choice of smaller area for exploration with minimum chance of missing undiscovered mineral deposits, in other words, to maximize the success and profits while minimize the failure and loss.Owing to its uncertainty, mineral deposit prediction is a double probabilistic optimization method seeking the maximum probability of positive results and minimum probability of negative results.For such double optimization issue, researchers have been seeking various mathematical methods and approaches for objective prediction with optimal results and quantitative accuracy.
By analyzing the approach proposed by Sartrebayev, a scientist in Kazakhstan Academy of Science in 1950s, the authors suggested predicting mineral potentials and permissive areas by compiling geological map showing metallogenetic information and spatial distribution of mineral deposits.Since then, this idea has achieved various successes and it has been improved through practice by many others in mineral deposit prediction.However, due to the diversity of mineral deposit types, the complexity of mineral deposit genesis, the obscurity of mineral controlling factors, and generally lack of unique solution for mineral exploration information, it is still difficult to understand mineralization regularity and to build unique mineral deposit models.Therefore, it is sometimes arbitrary for mineral prediction.Some researchers believe that mineral deposit prediction is one kind of art rather than science.This view reflects the complexity of mineral deposit prediction.
A Russian geologist, Harchinkov (1987)published an article entitled"Principles and Methods for Mineral Resources Prediction".In this article the author analyzed the differences between minera deposit prediction being considered as a geologica system and other more technical and economical systems and concluded that the mineral deposi prediction has the following characteristics: (1) in the mineral deposit prediction system, multiple elements have different or conflicting functionalities; (2) many elements not only have their own structures and functions in a system, but also become systems a other scales.For example, a rock is not only the element of a mineralization system, but also a system containing minerals and elements; (3) the elements in the system are correlated to various degrees from each other, for example, from completely correlated to totally independent; (4) geological systems can be controlled experimentally, rather than a static state tha can be observed in the final phase; (5) many systems can be divided into sub-systems, each of which can be analyzed separately.The transition between differen sub-systems can be continuous or discrete; and (6) while the geological processes are the same, the status of similar geological systems can be different.When the intensity of geological processes is different from one geological system to another, the transition from one system to another is usually gradual rather than abrupt.Harchinkov also stated that currently the mos widely used prediction method or the most appropriate method to analyze a system is the dynamical analysis method.For example, researchers can systemize the mineral deposits with different formation types and formation factors according to descending or ascending sequence of a parameter value.Due to the interactions of multi-levels, multi-scales, multielements, multi-types, and multi-subsystems of mineral formational systems, it is necessary to conduc quantitative studies on these systems.In a mineralization system, recognition of the connectivity variability, self-organized criticality, self-similarity and singularity of mineralization systems has achieved great progresses in recent years.This was done for the application of nonlinear theories and methods in characterization of mineralization processes and mineral deposit prediction.A summary of non-linear theory and models applied in mineral resource assessment and characterization of mineralization is referred to Cheng (2008).
According to Harchinkov's study, the first attempt for resource prediction in the world was done for prediction of petroleum reserves in the American continent by the U.S.Geological Survey (USGS) (U.S.Department of the Interior, U.S.Geological Survey, 2005) and the American Association of Petroleum Geologists (AAPG) in 1921.A few years later, in 1924, a first prediction for global petroleum resources was proposed during a Petroleum Workshop held in London.In former U.S.S.R., academician Gubukin predicted the petroleum resources on the basis of the contrast of continents.He used the U.S.petroleum fields as references and predicted the petroleum resource amounts in some regions in U.S.S.R.using structural average reserve method.Meanwhile, he also predicted the coal resource amounts in some basins.Smirov made the first attempt to predict the resource amounts of metallic mineral deposits.He noted that the known mineral deposit reserve and quality are the initial elements in prediction.He also proposed the concepts of "mineralization extensity"and"mineralization intensity".The former refers the chance of occurrence of industrially valuable medium or larger ore deposits in the potential areas and the latter refers the percentage of ores with industrial values out of all ore bodies.He suggested that the"mineralization extensity"can be delineated and represented in geological maps whereas the"mineralized intensity"can be determined by comparing the area to be evaluated with those with adequate mineralization information and having similar type of ores found.These two concepts are integrated resulting in a concept"coefficient of mineral content in a geological formation".The research of modern quantitative prediction methods began in 1950s.Since then, several publications improved the research of mineral resource quantitative prediction and evaluation.For example, Multi-variables Prediction Function in Geology (Agterberg, 1970), Mathematical Methods for Mineral Prediction (Bugayechi and Dujinko, 1976), and Evaluation of Mineral Resources (Harris, 1984) al generally discussed the mineral quantitative prediction and evaluation.In 1990's, GIS and GIS-based methods have rapidly developed and spatial data (map data) have been intensively used in the minera resource assessment.With the aid of GIS, qualitative features of maps or images can be digitized and represented in vectors or raster formats.These qualitative data can be included in quantitative modeling which has significantly improved the applicability of geo-mathematical models.For example, Bonham-Carter (1994)published a book entitled"GIS for Geologists: Modeling with GIS"where various quantitative models, especially logistic models such as the weights of evidence and logistic regression methods were introduced.These methods are implemented in various GIS platforms.An excellent example for solving double probability optimization problem is to determine the optimum threshold value.This is done to delineate target area so that the contrast of the posterior probability of mineral deposits in the target area and posterior probability of mineral deposits missing from the targe area or outside of the target area, is maximized (Bonham-Carter, 1994; Agterberg, 1989).Other developments include generalization of the logistic model and taking into consideration the uncertainty of evidence definition and cluster distribution of minera deposits.Furthercomputer-basednumerica techniques, such as artificial neural network, have been adopted for optimum purposes in building prediction models.Since the 1990s, non-linear theories and methods of resource prediction applications improved the quantitative prediction research into a new area where mineral exploration has improved.In this new area, Frits Agterberg and Qiuming Cheng's works of fractal simulations of mineral deposits, multifractal and spatial statistics GIS-based multifractal anomaly analysis of mineralization and mineral deposit prediction, and singularity theory and method have importan theoretical and practical significance.The core principle of their work is the concept of singularity and singular geo-processes including mineralization These processes have fractal or multifractal properties which can be described by power-law distribution The power-law (fractal or multifractal) distributions of mineral deposit density and mineral deposit related geoanomalies have been accepted and applied for characterizing mineralization and also for mineral deposit prediction (Cheng, 2008; Raines, 2008; Singer, 2008).
Harchinkov (1987)also pointed out that in spite of the fact that majority of mineral deposits may follow general regulations, during the evolution of any kind of geological structural environment, it might generate anomalous individuals, such as, giant deposits due to some special conditions.If these types of anomalies are hidden or not too likely to occur, it is generally difficult to predict especially by using the ordinary similarity comparison method or ordinary statistical approaches.
The book"Statistical Prediction of Mineral Deposit"shows three principles for mineral deposit prediction; "similarity comparison", "anomaly seeking", and"quantitative combination of mineral deposit controlling factors", these were proposed by Zhao et al. (1983).Since 1990, Zhao et al. (2005)have proposed the theory and the method of"comprehensive geological anomalies controlling mineralization"and stated that mineral deposit is an economically valuable geological anomaly of ore element and ore mineral or mineral assemblages.Zhao (2001)proposed a digital mineral exploration theory named"three-component (geoanomaly-mineralization diversity-mineral deposit spectrum) mineral resource quantitative prediction and evaluation".
Recently, the"geological anomaly mineral resource prediction"theory has been applied for various types of mineral deposits (Au, Cu, Pb, Zn, and Sn) in Xinjiang, Yunnan and Shandong, China.These studies show that most of these mineral deposits are associated with some types of geoanomalies either in composition, texture, structure or genesis.
Mineral resource project (MRP) of USGS pointed out that it is important to ensure that mineral resource potentials of undiscovered mineral deposits are evaluated using the latest quantitative methods.According to this project, modern evaluation should be quantitative including assessment of economic feasibility and uncertainty related to the number of undiscovered mineral deposits, values, and their locations.USGS also noted that the next innovative direction for quantitative mineral resource evaluation is to use novel and accurate methods to reduce uncertainties of estimation.Therefore, quantitative prediction and evaluation of mineral deposits is one of the most important issues of research for the international community.
Mineral exploration is currently facing three issues: "identification, discovery and usage".The main reasons for these issues are due to the fact that most mineral deposits near earth surface have being discovered and used and mineral resources that are related to these shallow mineral deposits become rare, particularly for large and super large ore deposits.Mineral exploration in depth and in surrounding areas of existing mineral deposits has become the practical strategy for further mineral exploration.For the past half century, mineral exploration in China has been primarily focused near surface with exploration depth no more than 300 to 500 m."Deeper deposits"located below 700 m have not been normally explored.In order to guarantee sustainable development of economics and society of China, it is imperative to perform deep mineral explorations.
According to Narxieev (1989), there is no generally accepted definition for"deep mineral exploration".The"deep mineral exploration"areas could be the areas below the ore bodies with estimated reserves where there have been no adequate exploration and relevant geological studies.The depth can be from 500 m or 1 000 m.In some mining areas, the depth of the structure reconnaissance drilling is ranging from 800 to 1 000 m and sometimes it reaches1 200–1 500 m for the purpose of evaluating the favorability of deep mineralization environment and finding the most possible mineral exploration zones.In recent years, new ideas such as"the second mineralization space"and"the second mineral exploration space"are proposed in China, yet there has been no clear definition of these ideas.Teng (2007)proposed"second deep space of crust interior"and suggested to perform mineral exploration in the space with depth range of 500–2 000 m.Chang and Zhai (cited in Lü, 2006) suggested that tectonophysicochemical methods can be used to explore the second metal mineralization and enrichment zone in the deeper part of metallogenic belts along the circum-Pacific belt of eastern China.Lü (2006) suggested to conduct comparative studies in Jiaodong mineral district for deep mineral deposit exploration.These authors discussed the possibility of occurrence of"the second enriched mineralization zone", yet they did not mention the details of the depth.
No matter how"the second mineralization exploration space"is defined, the potential for mineral exploration in the vertical space of the crust is believed to be significant.It has been verified by many successful examples of deep geophysical exploration, ultra-deep drilling and deep mineral exploration and mining practices.Since the depth of mineral exploration and exploitation in China has normally been small, it is essential to explore the second metallogenic and mineral exploration space.Ultimately, the objectives of exploring"the second mineralization exploration space"are to find new ores of similar or different types or to reveal favorable mineralization environments in the deep part or surroundings of known deposits.This can be simply interpreted as"prospecting areas below or surrounding known ores".Of course the depth to be explored should vary according to the crustal structures.Emphases should be also given to find new ores or new deposit types in the deep geological environment which may be different from the shallow or near surface geological environments.This could be the more positive significance of exploring"second mineralization exploration space".Several situations need to be considered in deep mineral exploration.One is that new ores to be explored can be either as new ore bodies or extension of the ore bodies discovered in a shallow depth.For example, the ongoing mineral exploration program in most of so called"crisis mines"sponsored by China Geological Survey for new resources may belong to this category.In this situation, since the upper known ore bodies can be used as similar analogy references, this generally makes deep mineral exploration a lot easier and efficient.However, due to changes of structural environment with increase of depth, the mineralization types and even ore bodies could be different in depth from shallow level.Therefore, one must pay close attention to the application of different strategies to analyze geological anomalies related to new types of ores or new deposit types.Another way to explore deep minerals is by exploring deep space in areas where no ores were found even near the surface.In these areas, the ores were either formed in depth and never lifted close to the earth surface, or ores are covered by loose sediments or sedimentary rock formations.In either case, such kind of mineral exploration is generally difficult, costly and inefficient Therefore, it is essential to conduct in-depth research on the spatial regularities of mineralization including analyzing structural, lithological and magmatic factors and characteristics of covers such as loose sediments, sedimentary formations and non-mineralization related litho-structural layers.Such analysis is useful for the division of typical mining areas, and designing research methods to analyze deep geological structures.Other types of factors are also needed to be considered, for example, economic factors, long-term potentials of resources, technology, equipment and financial investments in different conditions.The exploration conditions should also be considered in order to develop an exploration system adapted to local conditions.
Recently, some researchers and companies paid more attention to studying deep crust evolution, crust-mantle interaction, crustal structures and component types, interaction of magma and mineralization, and deep crustal dynamics and mineralization to support deep mineral exploration.In2003, the Russian Voronezh National University published a book entitled"Mineral Deposit Prediction and Mineral Exploration at the Beginning of the New Century", this book includes a series of papers dealing with deep mineralization and exploration.In this bookMishinin et al. (2003)described a potential crosscontinent super-large deep ore-control structures in Siberian platform, named"West Yakut Reefs".This also includes"lower combination"mineralization produced in the former Proterozoic crystalline basement: accumulation of chromites, Cu-Ni sulphide deposit with Pt, and the layered polymetallic mineralization dense silicification phosphate rocks and hydrocarbon deposits produced in the sedimentary cover of"upper combination".The"oblique group"mineralization with diamond and Cu-Ni-Pt produced in the sedimentary cover intruded by intrusive bodies rooted in crystallization basement and even reached the upper mantle.Pospieeva (2003)pointed out that in East Siberia there are general correlations between metallogenic province, mining area, mining and ore-field and different types and levels of uneven distribution of the lithosphere.The transporting layers of the asthenosphere and the lithosphere played a decisive role in the mineralization system and formation of different combinations of mineralization for different mines.The transporting layers of the asthenosphere and the lithosphere serve as the supplier and penetrating zone of metallogenic materials (hydrothermal mineralization, kimberley magma, etc.), that is, mineral transport corridor and the accumulation zone of ore material in the upper crust.Waluov (2003)revealed the close relationship between the distribution of ore-fieldsof diamond-bearing kimberlites in Russia Yakuta and the thickness of the lower crust.
Pacekin's paper"The Deep Structure of TiemanUral Region Related with the Primary Diamond", the paper by Qieerneilov"Prediction of Diamond-Bearing kimberlites in the BKM Region Based on Deep Geological and Geophysical Research"and other papers showed that in the exploration processes for concealed diamond deposit, oil and metal deposit.For example, Kerrich et al. (2005)emphasized the relationship between characteristics of metallogenetic provinces and specified classes of mineral deposits and the geodynamic setting.The new discoveries are often dependent on understanding of the deep structures and the uneven physical parameters of the crust and mantle, which are the fundamental causes of mineralization, magma activities and structural activities.
Cohen et al. (2007)reviewed the major progress in exploration geochemistry in 1998–2007.This study pointed out that metal prices are rising and advances in the exploration technology expand exploration activities to under-explored areas, including areas with thick covers of loose sediments.The authors also pointed out that there are yet challenges to perform surface geochemical exploration in those areas with very thick layer of sediments.These areas include, for example, the northern Tasmania of eastern Australia which is covered by thick-bedded alluviums and the sedimentary sequences from Mesozoic to Tertiary.In these areas, there is a great gap between the exploration effort and the mineral potential.Newly adopted and improved element dispersion model and sampling and analytical techniques improved the development of geochemical exploration for anomaly identification and verification; it improved the way of measuring parameters of soil and underground water, such as conventional measurement of pH value, and detecting geochemical anomaly of hidden mineral deposit.The new rock geochemical methods can be used in early stage to choose area with mineral deposits potential.According to the articles published for the past 10 years in three journals of applied geochemistry: "AppliedGeochemistry", "Geochemistry: Exploration, Environment and Analysis", and"Journal of Geochemical Exploration", the top issues covered in most articles are: (1) data processing, (2) weathered-horizon geochemistry, (3) petrologic geochemistry and (4) geochemistry in covered-areas.It can be seen that geochemical prospecting plays an important role in finding hidden deposits.
In practice, the depth of deep ore exploration and forecasting is far greater than the depth of exploitation Exploitation gradually goes further into depth as time goes on.Some old mines reach 1.5–2 km in exploitation depth for 40–60 years.The exploitation depth goes down 40–50 m on average per year, sometimes up to 80 m.Therefore it is necessary to advance the prospecting/exploration task continuously for securing reserves for mine production.
In 2005, the"Program of Superseding Resources Prospecting in Crisis Mines in China"started with 40billion RMB from the central government, and recently in 2007"A National Deep Prospecting Symposium"was held in China.This indicated that deep mineral prospecting has entered a new stage in China, a stage of deep exploration not only in crisis mines, but also for promoting multiple mineral deposit types, multiple targets and comprehensive mineral exploration technologies.
Since deep mineral prospecting process is difficult, costly and inefficient, the"return rate"and"conversion rate"in deep prospecting need to be emphasized."Return rate"refers to the results of discovery of industrially valuable large and super large deposits.Discovery of these large scales deposits is often significant economically and socially.Niekelasov (2006)used gold mines as an example to explain the significance of finding key industrial deposits.For example, in early and middle 20th century, the main gold production came from auriferous conglomerate deposits and placer deposits, and till now the Witwatersrand gold mine of South Africa is still the world's leading gold producer.According to a survey in 2003, auriferous conglomerate deposits produced 390 t, comprising of15.4%of gold.
Hall and Wall (2007)proposed the issue of"conversion rate"to explore targets.According to this study, there are five phases of mineral exploration: (1) target generation phase, (2) drill testing phase, (3) resource delineation phase, (4) pre-feasibility phase and (5) feasibility phase.In their research of western Australia, from the first to the second phases there is normally a low conversion rate (6:1) associated with high cost, which is 70 thousand AUD per project.However, once the drilling is successful, conversion rates of the following phases will be high.Many targets are chosen during target generation phase, few of which can become efficient drilling target areas.It eventually results in low discovery rate, high investment and low profits.Therefore, during the whole processes of deep ore prospecting, especially during the early phase of target generation, comprehensive research is required to improve the"conversion rate"or"success rate".
It should be noted that finding deep deposits is of great difficulty, while the difficulty of proving the true value of deep deposit is even greater.
Xie (1997)introduced in detail the herculean task of finding the great deposit in Olympic dam in Australia, which took 20 years, costed more than 30million AUD and involved advanced methods.The discovery histories of uranium deposits in Russia and Mongolia show similar results.Shumilin (2007)introduced the discovery history of a giant uranium deposit in Streltzov, southern Erguna, although the geological survey started in 1954, the biggest deposi of Erguna was found 14 years later through multiple turns of negation and affirmation.In Mongolia, the uranium deposits of Dornot and Qurban bucra were discovered in 1972, drilled numerously afterwards and finally proven to be industrial type deposits in1978.Later it was also found that earlier drillings didn't actually reach the depth of industrial deposits In Caglieri, Russia, it took 30 years from the firs discovery of abnormal emissive activity to fina discovery of the super large, hidden and vanadiumrich uranium deposit.Therefore, exploring and finding the true scale of mineral deposits can not be rushed even in areas of super large mineral deposits.This shows that, deep prospecting is a process with high uncertainty, high risk, and high cost.It often needs repetitive processes of recognition, and, therefore needs greater scientific spirit and perseverance.In order to achieve success of deep prospecting, synthetic integration of geology, geo-technology, economy and policy is required.Quantitative modeling processes can play significant roles in the regards.
Several sources of research funding of the mining industry, China Geological Survey and the Nationa Natural Science Foundation of China (NSFC) enabled the authors'research group to conduct several research projects involving deep mineral prospecting and 3D mineral resource assessment for tin and copper in Gejiu, Yunnan for the past few years.Gejiu is one of the most important tin and copper mineral districts and is well-known for its world class tin deposits and tin production.With over half century of minera exploration and mining, the company has made significant contribution to tin supplies in China and the world.Meanwhile, the reserves known in the mine were reduced and the mine has been considered as one of the mines with the greatest crisis where known reserve is not sufficient for more than seven years of operation.To find new reserves maintaining the operation of the mine is essential, not only for the mine itself, but also for the economic and socia development of the local community, the province and the nation.The current projects being conducted in the area, aim to use new theories, methods and techniques to explore deep space (a range of 500 to 1 500 m in depth) as well as the surrounding area of the existing ore deposits.Considering the mineralization systems, ore types and ore controlling environments, the research tasks were designed according to the"threecomponent" (geological anomaly-mineralization diversity-mineral deposit spectrum) mineral deposit prediction and mineral resource assessment theory (Zhao, 2001).Due to the multiscale and multitask nature of the research, we explore and employ modern non-linear principles and models for data processing and anomaly recognition.Modern 2D and 3D specialized GIS technologies are applied for data management, information processing and potential mapping.The main tasks include: recognition of geoanomalies at various scales including microanomalies, macro-anomalies and regional anomalies, construction of mineralization diversities and mineral deposit spectrum (e.g., Zhang et al., 2008; Cheng, 2007; Xia et al., 2007); mapping anomalies and posterior probability on the basis of diverse geoscientifc data at 1:200 000, 1:50 000 and 1:10 000scales for delineating target areas and their favorability for occurrences of mineral deposits of Sn and Cu.The areas chosen for these three-scale minera prospecting are shown in Fig.1.Prediction of regiona scale mineral resource potential is carried out by covering the entire study area on the basis of reconnaissance diverse geodatbase including geological, geochemical, geophysical, remote sensing and mineral deposits inventory data.Ordinary weights of evidence method and logistic regression methods as well as more sophisticated non-linear methods and GIS technologies (GeoDAS and MORPAS) are employed.Further information of non-linear theories and methods and some preliminary results can be found in Cheng (2008, 2007).The ore prospecting a1:50 000 scale was designed for two areas located in the eastern and western Gejiu as labeled in Fig.1.At this scale the mineral prospecting is based upon more detailed geo-information with limited overages in the area, for example, soil geochemical data, geochemica data from rock samples collected along structures and geophysical electromagnetic data.The geo-anomalies delineated in eastern Gejiu can be found inZhao (2007).The target areas delineated at this scale can be validated by field investigation which is an important integral task of the project.The third scale prospecting focuses on deep prospecting in depth around the exiting ore bodies in the areas, normally with detailed drilling and mapping data at 1:10 000.The main objective of prospecting in this scale is to delineate target areas for drilling.The results can be ultimately considered by the company planning for drilling program.Further results of the project will be published in the future.
As the depth of mineral exploration and exploitation increases currently in China, building feasible plans for various depth divisions for deep exploration/prospecting, and suitable deep exploration systems and prospecting models will ensure the achievements of new discovery of mineral resources.Due to the complexity of deep structural environment and mineralization systems, limited availability of direct observations and incomplete geoscientific data, how to utilize the ordinary quantitative mineral resource assessment methods for this challenge work does require innovative research and development and this has no doubt opened a new research direction.New theories such as geo-anomaly principle and methods, especially quantitative modeling and simulation techniques such as non-linear methods and techniques and specialized GIS systems should play an important role in interpretation and integration of geo-information for deep mineral exploration.
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