The efficiency of logistic function and prediction-area plot in prospectivity analysis of mineral deposits.

Mineral Prospectivity Conference, France.In this work, we present logistic-based mineral prospectivity mapping (MPM) methods concerning with assigning weights of exploration indicators, without contribution of training sites as in supervised MPM and without using user-judged weights as in unsupervised MPM, to modulate the problems of stochastic and systemic errors. In addition, we discuss the ability of prediction-area plot as a tool to assess and compare evidential layers and prospectivity models

HEBF strategy: A hybrid evidential belief function in geospatial data analysis for mineral potential mapping

In integrating geospatial datasets for mineral potential mapping (MPM), the uncertainty model of MPM can be inferred from the Dempster – Shafer rules of combination. In addition to generating the uncertainty model, evidential belief functions (EBFs) present the belief, plausibility, and disbelief of MPM, whereby four models can be simultaneously utilized to facilitate the interpretation of mineral favourability output. To investigate the functionality and applicability of the EBFs, we selected the Naysian porphyry copper district located on the Urmia – Dokhtar magmatic belt in the northeast of Isfahan city, central Iran. Multidisciplinary datasets- that are geochemical and geophysical data, ASTER satellite images, Quickbird, and ground survey- were designed in a geospatial database to run MPM. Implementing the Dempster law through the intersection (And) and union (OR) operators led to different MPM performances. To amplify the accuracy of the generated favourability maps, a combinatory EBFs technique was applied in three ways: (1) just OR operator, (2) just And operator, and (3) combination of And and OR operators. The plausibility map (as mineral favourability map) was compared to Cu productivity values derived from drilled boreholes, where the MPM accuracy of the hybrid method was higher than each operator. Of note, the success rate of the hybrid method validated by 21 boreholes was about 84%, and it demarcates high favourability zones occupying 0.67 km2 of the studied area

Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran.

Peer review journal article. Geology.Diverse deposit-types or mineral systems form by diverse geological processes, so translation of knowledge about the controls of mineralization acquired from the 4D geological modeling into 2D spatial predictor maps is a major challenge for prospectivity analysis. In this regard, mathematical functions have been used to model the conceptual or perceived spatial relationships between geological variables and targeted type or system of mineralization. In this paper, due to the different models of spatial relationships between predictors and mineral deposits, we investigated the performance of different fuzzification functions to quantify the relationships. We demonstrated that various types of relationships between exploration features and a mineralization-type sought could be quantified using different fuzzification functions for prospectivity analysis. We illustrated the process of the prospectivity analysis by using a data set of orogenic gold deposits in Saqez-Sardasht Goldfield, Iran. Prospectivity modeling of orogenic gold mineralization in the study area showed that the NE-SW trending targets have priority for further prospecting of the deposits

An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping

The combination of complex, multiple minerogenic stages and mineral superposition during geological processes has resulted in dynamic spatial distributions and nonstationarity of geological variables. For example, geochemical elements exhibit clear spatial variability and trends with coverage type changes. Thus, bias is likely to occur under these conditions when general regression models are applied to mineral prospectivity mapping (MPM). In this study, we used a spatially weighted technique to improve general logistic regression and developed an improved model, i.e., the improved logistic regression model, based on a spatially weighted technique (ILRBSWT, version 1.0). The capabilities and advantages of ILRBSWT are as follows: (1) it is a geographically weighted regression (GWR) model, and thus it has all advantages of GWR when managing spatial trends and nonstationarity; (2) while the current software employed for GWR mainly applies linear regression, ILRBSWT is based on logistic regression, which is more suitable for MPM because mineralization is a binary event; (3) a missing data processing method borrowed from weights of evidence is included in ILRBSWT to extend its adaptability when managing multisource data; and (4) in addition to geographical distance, the differences in data quality or exploration level can be weighted in the new model

Application of Artificial Neural Networks to geological classification: porphyry prospectivity in British Columbia and oil reservoir properties in Iran

Seismic facies analysis aims to classify oil and gas reservoirs into geologically and petrophysically meaningful rock groups, or classes. An artificial neural network (ANN) is a versatile and efficient tool for classifying data or estimating subsurface properties from large geophysical datasets. This tool can provide critical information for oilfield development and reservoir characterization. This study includes application of artificial neural networks on two different datasets: 1) geophysical characterization of an oil reservoir in Iran and 2) geological prospectivity for porphyry in British Columbia, Canada. In the first case study, I utilize seismic attributes, well-log data, and core data analysis and use supervised machine learning techniques to efficiently estimate the acoustic impedance and porosity of the reservoir and to classify it into four lithological classes. Seismic attributes as inputs for our techniques capture the lithological patterns or structural characteristics in the seismic amplitude, phase, frequency, and other complex seismic properties that cannot be directly seen in the original seismic images. Selection of an optimal set of input features from the vast number of possible mathematical transformations of seismic data is a critical task for reservoir property prediction and classification. This selection is performed by standard as well as innovative procedures employing properties of the target classes. Three different supervised approaches to non-linear classification are used: 1) the so-called probabilistic neural network (PNN), 2) conventional ANN, and 3) an ANN with the new approach of optimal attribute selection. For each of these approaches, images of classification confidence levels and confidence-filtered class images are produced. Assessments of the robustness and accuracy of seismic facies classification is performed for each of these algorithms. The ANN classifiers are validated using validation and test data subsets. The proposed algorithm shows a higher performance, particularly in comparison with the PNN algorithm. Several visualization techniques are used to examine and illustrate the power of the ANN-based approaches to classify the seismic facies with high accuracy. However, the three approaches still provide significantly different levels of lateral continuity, frequency content, and classification accuracy. Therefore, some level of expert assessment is still required when using machine learning for reservoir interpretation. In the second case study, I use an ANN to explore the prospectivity for porphyry within the Quesnel Terrane, BC, Canada. A purely data-driven approach based on geophysical, structural, and volcanic-age data results in a predictive prospectivity map which correlates well with known mineral occurrences and suggests new areas for potential exploration

Contribution of Gravity Data for Structural Characterization of the Ifni Inlier, Western Anti-Atlas, Morocco: Hydrogeological Implications

The Sidi Ifni region in southwest Morocco is mainly composed of crystalline rocks with limited groundwater storage capacity. These water resources drain in particular fault zones with high fracture permeability. The main objective of this study is to describe the geological structure of the region to optimize future drilling locations. The gravity data were processed using various techniques, such as total horizontal gradient, tilt derivative, and Euler deconvolution, in conjunction with the interpretation of the geological data, to create a new structural map. This map confirms the presence of many previously identified or inferred faults and identifies significant new faults with their respective trends and depths. Analysis of this map shows that major faults are oriented NNESSW and NE-SW, while minor faults are oriented E-W, NW-SE, and NNW-SSE. The superposition of the hydrogeological data and the structural map reveals that the high groundwater flow values in the boreholes are located in the vicinity of the major faults and talwegs. The structures deduced from the filtering and interpretation of the gravity data suggest that the hydrogeological system of the Ifni Inlier is controlled by its structures. To confirm this impact, a high-resolution electrical resistivity map (7200 Hz) was used, with penetration depths ranging from 84 to 187 m. Negative boreholes, located in high resistivity ranges corresponding to sound basement formations without fault crossings, showed high resistivity values. The positive holes, located in anomalies with low linear resistivity, revealed the impact of fault crossings, which drain water and tend to decrease the resistivity values of the formations. Therefore, these new structural maps will assist in planning future hydrogeological studies in this area

A Fuzzy Entropy-Based Thematic Classification Method Aimed at Improving the Reliability of Thematic Maps in GIS Environments

Thematic maps of spatial data are constructed by using standard thematic classification methods that do not allow management of the uncertainty of classification and, consequently, eval uation of the reliability of the resulting thematic map. We propose a novel fuzzy-based thematic classification method applied to construct thematic maps in Geographical Information Systems. An initial fuzzy partition of the domain of the features of the spatial dataset is constructed using triangular fuzzy numbers; our method finds an optimal fuzzy partition evaluating the fuzziness of the fuzzy sets by using a fuzzy entropy measure. An assessment of the reliability of the final thematic map is performed according to the fuzziness of the fuzzy sets. We implement our method on a GIS framework, testing it on various vector and image spatial datasets. The results of these tests confirm that our thematic classification method provide thematic maps with a higher reliability with respect to that obtained through fuzzy partitions constructed by expert users

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

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies

Determination of an Ultimate Pit Limit Utilising Fractal Modelling to Optimise NPV

The speed and complexity of globalisation and reduction of natural resources on the one hand, and interests of large multinational corporations on the other, necessitates proper management of mineral resources and consumption. The need for scientific research and application of new methodologies and approaches to maximise Net Present Value (NPV) within mining operations is essential. In some cases, drill core logging in the field may result in an inadequate level of information and subsequent poor diagnosis of geological phenomenon which may undermine the delineation or separation of mineralised zones. This is because the interpretation of individual loggers is subjective. However, modelling based on logging data is absolutely essential to determine the architecture of an orebody including ore distribution and geomechanical features. For instance, ore grades, density and RQD values are not included in conventional geological models whilst variations in a mineral deposit are an obvious and salient feature. Given the problems mentioned above, a series of new mathematical methods have been developed, based on fractal modelling, which provide a more objective approach. These have been established and tested in a case study of the Kahang Cu-Mo porphyry deposit, central Iran. Recognition of different types of mineralised zone in an ore deposit is important for mine planning. As a result, it is felt that the most important outcome of this thesis is the development of an innovative approach to the delineation of major mineralised (supergene and hypogene) zones from ‘barren’ host rock. This is based on subsurface data and the utilisation of the Concentration-Volume (C-V) fractal model, proposed by Afzal et al. (2011), to optimise a Cu-Mo block model for better determination of an ultimate pit limit. Drawing on this, new approaches, referred to Density–Volume (D–V) and RQD-Volume (RQD-V) fractal modelling, have been developed and used to delineate rock characteristics in terms of density and RQD within the Kahang deposit (Yasrebi et al., 2013b; Yasrebi et al., 2014). From the results of this modelling, the density and RQD populations of rock types from the studied deposit showed a relationship between density and rock quality based on RQD values, which can be used to predict final pit slope. Finally, the study introduces a Present Value-Volume (PV-V) fractal model in order to identify an accurate excavation orientation with respect to economic principals and ore grades of all determined voxels within the obtained ultimate pit limit in order to achieve an earlier pay-back period.Institute of Materials, Minerals and Mining, the global network IOM3Cornish Institute of EngineersWhittle Consulting (Business Optimisation for the Mining Industry