Assessing the Performance of Statistical-structural and Geostatistical Methods in Estimating the 3D Distribution of the Uniaxial Compressive Strength Parameter in the Sarcheshmeh Porphyry Copper Deposit

The uniaxial compressive strength (UCS) of intact rocks is an important geotechnical parameter required for designing geotechnical and mining engineering projects. Obtaining accurate estimates of the rock mass UCS parameter throughout a 3D geological model of the deposit is vital for determining optimum rock slope stability, designing new exploratory and blast boreholes, mine planning, optimizing the production schedule and even designing the crusher’s feed size. The main objective of this paper is to select the preferred estimator of the UCS parameter based on accuracy performance using all the available geological-geotechnical data at the Sarcheshmeh copper deposit, located 160 km southwest of Kerman City, in south-eastern Iran. In this paper, an attempt is made to estimate the spatial distribution of the UCS parameter using commonly-used statistical-structural and geostatistical methods. In order to achieve the aim of the current study, the UCS parameter was measured along with other qualitative geological properties, including the rock type, weathering, alteration type and intensity of core samples taken from 647 boreholes. The 3D distribution of the UCS parameter is obtained using different algorithms including statistical-structural (the nearest-neighbour technique), linear (ordinary Kriging) and nonlinear (indicator Kriging) geostatistical methods. After estimating the UCS parameter at block centres using the above-mentioned methods, the performance of each method is compared and validated through 21 set aside borehole data. The assessment of selecting best estimator of UCS parameter is based on scatter plots of the observed versus estimated data plus the root mean square error (RMSE) statistics of the differences between observed and estimated values for 21 set aside borehole data. Finally, due to the special characteristics of the UCS spatial variability, it is concluded that the nearest-neighbour method is the most appropriate method for estimating the UCS parameter in porphyry copper deposits

The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression

Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced for both regression (support vector regression (SVR)) and classification (support vector classification (SVC)) problems. In the current study, to estimate the porosity of a carbonate reservoir in one of Iran south oil fields from well log data, the SVR model is firstly constructed; then the performance achieved is compared to that of an artificial neural network (ANN) model with a multilayer perceptron (MLP) architecture as a well-known method to account for the reliability of SVR or the possible improvement made by SVR over ANN models. The results of this study show that by considering correlation coefficient and some statistical errors the performance of the SVR model slightly improves the ANN porosity predictions

PRISMA hyperspectral imagery for mapping alteration zones associated with Kuhpanj porphyry copper deposit, Southern Iran

ABSTRACTHyperspectral images have been extensively employed to map alterations related to various ore deposits, particularly those associated with porphyry copper deposits. The present study aims to evaluate the efficiency of the PRISMA (Precursore Iper Spettraledella Missione Applicativa) satellite imagery, that is an Italian satellite, in identifying and discriminating alteration zones (Phyllic, Argillic, Propylitic) related to a well-known porphyry copper mineralization system in Iran. The chosen algorithms for performance evaluations involve selective principle component analysis (SPCA), spectral band ratios, mixture-tuned matched filtering (MTMF), and linear spectral unmixing (LSU). In this study, new band ratios are also proposed to improve the efficiency of alteration mapping. The results are validated by field data and geological-alteration map demonstrating the overall accuracy of detecting different alteration zones including the Phyllic, Argillic, and Propylitic zones equal to 71.87%, 87.50%, 81.25% and 71.87%, and kappa coefficients of 58.85%, 80.54%, 70.55% and 57.52% for SPCA, band ratio, MTMF, and LSU algorithms, respectively. In current research, it is found that using PRISMA data, the band ratio algorithm is superior when compared to other implemented algorithms in detecting hydrothermal alteration associated with porphyry copper deposits

Proposing New Methods to Enhance the Low-Resolution Simulated GPR Responses in the Frequency and Wavelet Domains

To date, a number of numerical methods, including the popular Finite-Difference Time Domain (FDTD) technique, have been proposed to simulate Ground-Penetrating Radar (GPR) responses. Despite having a number of advantages, the finite-difference method also has pitfalls such as being very time consuming in simulating the most common case of media with high dielectric permittivity, causing the forward modelling process to be very long lasting, even with modern high-speed computers. In the present study the well-known hyperbolic pattern response of horizontal cylinders, usually found in GPR B-Scan images, is used as a basic model to examine the possibility of reducing the forward modelling execution time. In general, the simulated GPR traces of common reflected objects are time shifted, as with the Normal Moveout (NMO) traces encountered in seismic reflection responses. This suggests the application of Fourier transform to the GPR traces, employing the time-shifting property of the transformation to interpolate the traces between the adjusted traces in the frequency domain (FD). Therefore, in the present study two post-processing algorithms have been adopted to increase the speed of forward modelling while maintaining the required precision. The first approach is based on linear interpolation in the Fourier domain, resulting in increasing lateral trace-to-trace interval of appropriate sampling frequency of the signal, preventing any aliasing. In the second approach, a super-resolution algorithm based on 2D-wavelet transform is developed to increase both vertical and horizontal resolution of the GPR B-Scan images through preserving scale and shape of hidden hyperbola features. Through comparing outputs from both methods with the corresponding actual high-resolution forward response, it is shown that both approaches can perform satisfactorily, although the wavelet-based approach outperforms the frequency-domain approach noticeably, both in amplitude and shape of the outputted hyperbola response

Presenting a mapping method based on fuzzy Logic and TOPSIS multi criteria decision-making methods to detect promising porphyry copper mineralization areas in the east of the Sarcheshmeh copper metallogenic district

Introduction The growing demand for base metals such as iron, copper, lead and zinc on the one hand and the diminishing of surficial and shallow resources of these elements on the other hand have forced explorationists to focus on detecting deep deposits of these metals. As a result, the discovery of such deep deposits requires more advanced and sophisticated methods in the course of preliminary prospecting stages. Since the discovery of new deposits is getting to be increasingly difficult, deploying new prospecting technologies that employ more deposit attributes in the course of combining exploratory evidence may reduce the exploration costs with lower uncertainties. In the past two decades, a number of new data mining and integrating approaches capable of incorporating direct and indirect mineralization indicators, based on expert knowledge, data, or a combination of both, have been proposed )Bonham-Carter, 1994(. In the first step, the input exploratory data layers are corrected and validated through applying some statistical pre-processing algorithms such as background and outlier removal methods. In order to detect a mineralization occurrence, it is necessary to find the proper exploratory geological, geochemical and geophysical data layers which have direct or indirect associations with the governing mineralization followed by constructing these models in an appropriate GIS platform (Malkzewski, 1999). Due to the imperfect available data and a number of unknown parameters affecting the mineralization process, the application of conventional GIS integration methods such as Boolean or weighted overlay or even fuzzy logic methods alone may not produce appropriate results, pointing to a need for deploying multi-criteria decision-making methods such as TOPSIS. In the present study, the pre-processed exploratory data including geological, remotely sensed geophysical and geochemical imagery were used to detect favorable mineralization zones through applying the multi-criteria decision-making method. Finally, the selected favorable areas in the metallogenic strip located at the south to the south-east of the Sarcheshmeh porphyry copper deposit are prioritized and introduced for further follow up ground exploration operations. Methodology In order to solve complex decision-making problems like the problem of mapping favorable porphyry copper mineralization zones under great uncertainties, the TOPSIS method is considered as an appropriate approach offering significant simplicity, flexibility and capability (Ataei., 2010). The TOPSIS method is considered to be an efficient method due to having very high accuracy, speed, sensitivity as well as being easy to implement and interpret the outputted results (Hwang and Yoon, 1981). It has found many applications in important areas of mining industry where there is a need to make decisions under risky conditions and data uncertainties. One basic issue in applying decision-making methods in the field of mineral exploration is to rank and propose the best possible candidates among all potentially favorable areas for the next stage of mineral exploration. In this regard, the best favorable areas are selected based on exploratory data layers including favorable lithologies, alterations, structures plus geochemical and geophysical anomalies (Pazand et al., 2012). Results and discussion In the first step, the area located south to the southeast of one the largest porphyry copper deposits in Iran known as Sarcheshmeh was investigated for favorable areas using all available exploratory data as mentioned in the previous section using fuzzy logic integration approach in the GIS environment. Evaluating the highly favorable areas presented by the fuzzy logic approach showed great consistency with the already known copper mineralization prospects. Next, the first 20 priorities obtained from the fuzzy logic approach were chosen as the best candidates to be ranked using the TOPSIS multi criteria decision-making method. Among these favorable prospects, the one with the highest coefficient close to the ideal solution of 0.796 was found to be coincident with the Darehzar area that is a well known porphyry copper deposit 12 kilometers south of the Sarcheshmeh deposit. The favorable areas numbered 5 and 8 that correspond to well known porphyry copper mineralization prospects called Sereydoon and North Sereydoon were ranked as the second and third priorities with scores of 0.721 and 0.604, respectively. Other favorable areas ranked by the TOPSIS method were also prioritized and presented for further follow up explorations. To assess the sensitivity of the results obtained by the TOPSIS method, an amount of 10% of the values of each of the criteria were added and the outputted ranking results were compared to that of the original TOPSIS results. It was concluded that a slight change in the values of the criteria would not have significant impact on the results. However, 10 percent change of each criteria weight would greatly affect the prospects priorities obtained by re-applying the TOPSIS method. References Ataei, M., 2010. Multi Criteria Decision Making. Shahrood University Publications, Shahrood, 333 pp Bonham–Carter, G.F., 1994. Geographic information system for geoscientists-molding with GIS. Geological Survey of Canada, Ontario, Canada, 398 pp. Hwang, C.L. and Yoon, K., 1981; Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Berlin, 228 pp. Malkzewski, J., 1999, GIS and Multi Criteria Decision Analysis. John Whily and Sons, Canada, 387 pp. Pazand, K., Hezarkhani, A. and Ataei, M., 2012. Using TOPSIS approaches for predictive porphyry Cu potential mapping: A case study in Ahar-Arasbaran area (NW, Iran). Computers & Geosciences, 49(1): 62-71