Forced vertical and horizontal movements of a rectangular rigid foundation on a transversely isotropic half‐space

An analytical investigation of a half‐space containing transversely isotropic material under forced vertical and horizontal displacements applied on a rectangular rigid foundation is presented in this paper. With the goal of a rigorous solution to the shape‐ and rigidity‐ induced singular mixed boundary value problem, the formulation employs scalar potential representation, the Fourier expansion and the Hankel integral transforms method to obtain the surface arbitrary point‐load solution in cylindrical coordinate system. The obtained Green's functions are rewritten in rectangular coordinate system, allowing the response of the half‐space because of an arbitrary distributed load on a rectangular surface area be given in terms of a double integral. The numerical evaluations of stresses are done with the use of an element, which is singular at the edge and the corner of the rectangle. Upon the imposition of the rigidity displacement boundary condition for a rigid foundation and the use of a set of two‐dimensional adaptive‐gradient elements, which can capture the singular behavior in the contact stress effectively, a set of new numerical results are presented to illustrate the effect of transverse isotropy on the foundation response. Copyright © 2012 John Wiley & Sons, Ltd.     

Application of K-means and PCA approaches to estimation of gold grade in Khooni district (central Iran)

Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits. To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis (PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60 km northeast of the Anarak city and 270 km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold, arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.     

Proposed algorithm for optimization of directional additional exploratory drill holes and computer coding

Block model estimated based on the drill hole information gathered during exploration stage is not appropriate for short- and medium-term mine planning. Additional information prior to mining is required for various purposes, that the source of this information is drill holes. In most situations, reducing the overall uncertainty on the estimation is the major objective of additional drilling and collecting samples from them. The topic of selecting the optimum location for additional exploratory drill holes has been of interest to mining engineers and geostatisticians in the second half of last century, but all of them are not able to optimize directional drill holes because they are based on the 2D space and do not consider the 3D extension of drill holes and block model. This study introduces a methodology to optimize the dip and location of additional exploratory drill holes based on the defined objective function with considering 3D extension of ore body and drill holes. Direct search simulated annealing used that is efficient and robust method for solving the nonlinear unconstrained global optimization problems. This method is very useful in the case that drilling the drill holes with different dips has been obligated due to topography condition and ore body situation. A case study in gold resource illustrates the method.     

Integration multisource data for mineral exploration by using fuzzy logic,case study: Taknar deposit,NE of Iran

The Taknar Zone is located at the northern margin of the eastern Iranian continental microplate, and it is host to the Taknar massive sulfide deposit. This study was conducted to find new exploration targets. We used multiple data sources (e.g., litho-geochemical and magnetic surveys) to produce more effective predictive maps. Principal component analysis and hierarchical cluster analysis methods were used to organize the new information into favorability maps and to determine multi-element correlations. We then employed fuzzy logic modeling to create favorability maps from geochemical and magnetic data. A concentration–area multifractal method was used to evaluate the final integrated favorability map for massive sulfide exploration. Our new map identifies previously unexploited sites in the eastern part of the study area, near the boundary of the Taknar formation, with intrusive and subvolcanic rocks, with potential for mineral exploration. The newly defined targets are attractive because old mined ore bodies are also identified in the favorability map.     

Hydrothermal evolution of Darrehzar porphyry copper deposit,Iran: evidence from fluid inclusions

The Darrehzar porphyry Cu-Mo deposit is located in Southwestern Iran (～70 km southwest of Kerman City). The porphyries occur as Tertiary quartz-monzonite stocks and dikes, ranging in composition from microdiorite to diorite and granodiorite. The Darrehzar stock is highly altered, and even in the outermost part of the intrusion, it is not possible to find completely fresh rock. Surface weathering was developing ferrous Fe-rich lithologic units in leached zone and concentrated copper minerals in supergene zone. Unlike eastern areas which do not account for deep faults, the supergene zone is well developed in western areas with maximum of 118 m thickness. Hydrothermal alteration and mineralization at Darrehzar are centered on the stock and were broadly synchronous with its emplacement. Early hydrothermal alteration was dominantly potassic and propylitic, and was followed by later phyllic and argillic alteration. The hydrothermal system involved both magmatic and meteoric water and boiled extensively. Copper mineralization was accompanied by both potassic and phyllic alteration. Four main vein groups have been identified: (I) quartz?+?pyrite?±?molybdenite?±?anhydrite?±?K-feldspar?±?chalcopyrite?±?bornite?±?Cu and Fe oxidic minerals (peripheral); (II) quartz?+?chalcopyrite?+?pyrite?+?molybdenite; (III) quartz?+?pyrite?±?calcite?±?chalcopyrite?±?anhydrite (gypsum); and (IV) quartz or calcite, gypsum or ± pyrite. Based on abundance, nature, and phases number observed at room temperature, three types of fluid inclusions are typically observed in these veins: (1) vapor-rich, (2) liquid-rich, and (3) multi-phase. Early hydrothermal alteration was caused by high temperature, high salinity orthomagmatic fluid and produced a potassic assemblage. Phyllitic alteration was caused by high salinity and lower temperature orthomagmatic fluid. Magmatic and meteoric water mixture was developed in the peripheral part of the stock and caused propylitic alteration which is attributed to a liquid-rich, lower temperature.     

Application of a Modular Feedforward Neural Network for Grade Estimation

This article presents new neural network (NN) architecture to improve its ability for grade estimation. The main aim of this study is to use a specific NN which has a simpler architecture and consequently achieve a better solution. Most of the commonly used NNs have a fully established connection among their nodes, which necessitates a multivariable objective function to be optimized. Therefore, the more the number of variables in the objective function, the more the complexity of the NN. This leads the NN to trap in local minima. In this study, a new NN, in which the connections based on the final performance are eliminated, is used. Toward this aim, several network architectures were tested, and finally a network which yielded the minimum error was selected. This selected network has low complexity and connection among nodes which help the learning algorithm to converge rapidly and more accurately. Furthermore, this network has this ability to deal with the small number of data sets. For testing and evaluating this new method, a case study of an iron deposit was performed. Also, to compare the obtained results, some common techniques for grade estimation, e.g., geostatistics and multilayer perceptron (MLP) were used. According to the obtained results, this new NN architecture shows a better performance for grade estimation.     

Application of multifractal modeling technique in systematic geochemical stream sediment survey to identify copper anomalies: A case study from Ahar,Azarbaijan, Northwest Iran

This research is based on the application of stream sediments to mineral exploration. Identifying the geochemical anomalies from background is a fundamental task in exploration geochemistry. This paper applied the element concentration–area (C–A) model, to separating the geochemical anomalies from background based on a fractal approach and for the compilation of geochemical mapping from stream sediment samples (n = 620) of the Ahar region (Iran), where some Cu mineralization occurs. Comparisons of the known copper occurrences against the anomalous area created using thresholds from C–A method illustrate these hits. All of known Cu mineralizations and moreover defines two extra Cu anomaly districts. Additional sampling (n = 186) around new Cu anomaly confirms this anomaly within the district.     

The application of lithogeochemical and alteration index for copper mineralization in the Sonajil area, NW Iran

The Sonajil intrusive is located about 60 km southeast of Ahar, in Azarbaijan province in northwestern Iran, and is intruded into by andesitic trachytic Eocene pyroclastics and agglomerate. To the center, the stock is hydrothermally altered, that contains sub-economic copper mineralization. The 1,248 cells with 100?×?100 m grid were sampled, and measurement of elements by ICP-MS method and major oxides included by X-ray fluorescence method was performed. After statistical interpretations, geochemical anomalies and indices alteration (sericitization, alkali, Spitz–Darling, chloritization, Hashimoto, Hashiguchi) were calculated, and their map distribution was prepared. Based on these maps and other interpretations of data, our study indicated that despite the anomaly of copper and other elements such as gold, molybdenum, lead, arsenic and antimony of not being quite consistent, almost all of them are within the alteration zones.     

Multiple-point geostatistical modeling based on the cross-correlation functions

An important issue in reservoir modeling is accurate generation of complex structures. The problem is difficult because the connectivity of the flow paths must be preserved. Multiple-point geostatistics is one of the most effective methods that can model the spatial patterns of geological structures, which is based on an informative geological training image that contains the variability, connectivity, and structural properties of a reservoir. Several pixel- and pattern-based methods have been developed in the past. In particular, pattern-based algorithms have become popular due to their ability for honoring the connectivity and geological features of a reservoir. But a shortcoming of such methods is that they require a massive data base, which make them highly memory- and CPU-intensive. In this paper, we propose a novel methodology for which there is no need to construct pattern data base and small data event. A new function for the similarity of the generated pattern and the training image, based on a cross-correlation (CC) function, is proposed that can be used with both categorical and continuous training images. We combine the CC function with an overlap strategy and a new approach, adaptive recursive template splitting along a raster path, in order to develop an algorithm, which we call cross-correlation simulation (CCSIM), for generation of the realizations of a reservoir with accurate conditioning and continuity. The performance of CCSIM is tested for a variety of training images. The results, when compared with those of the previous methods, indicate significant improvement in the CPU and memory requirements.