The application of geochemical pattern recognition to regional prospecting: A case study of the Sanandaj–Sirjan metallogenic zone,Iran

In regional exploration programs, the distribution of elements in known mineral deposits can be used as a guide for the classification of deposits, search for new prospects and modeling ore deposit patterns. The Sanandaj–Sirjan Zone (SSZ) is a major metallogenic zone in Iran, containing lead and zinc, iron, gold, copper deposits. In the central part of the SSZ, lead and zinc mineralization is widespread and hitherto exploration has been based on geological criteria. In this study, we used clustering techniques applied to element distribution for classification lead and zinc deposits in the central part of the SSZ. The hierarchical clustering technique was used to characterize the elemental pattern. Elements associated with lead and zinc deposits were separated into four clusters, encompassing both ore elements and their host rock-forming elements. It is shown that lead and zinc deposits in the central SSZ belong to two genetic groups: a MVT type hosted by limestone and dolomites and a SEDEX type hosted by shale, volcanic rocks and sandstone. The results of elemental clustering were used for pattern recognition by the K-means method and the respective deposits were classified into four distinct categories. K-means clustering also reveals that the elemental associations and spatial distribution of the lead and zinc deposits exhibit zoning in the central part of the SSZ. The ratios of ore-forming elements (Sb, Cd, and Zn) vs. (Pb and Ag) show zoning along an E–W trend, while host rock-forming elements (Mn, Ca, and Mg) vs. (Ba and Sr) show a zoning along a SE–NW trend. Large and medium deposits occur mainly in the center of the studied area, which justify further exploration around occurrences and abandoned mines in this area. The application of a pattern recognition method based on geochemical data from known mineralization in the central SSZ, and the classification derived from it, uncover elemental zoning, identify key elemental associations for further geochemical exploration and the potential to discover possible target areas for large to medium size ore deposits. This methodology can be applied in a similar way to search for new ore deposits in a wide range of known metallogenic zones.     

Strong ground motion record selection for the reliable prediction of the mean seismic collapse capacity of a structure group

How to select a limited number of strong ground motion records (SGMRs) is an important challenge for the seismic collapse capacity assessment of structures. The collapse capacity is considered as the ground motion intensity measure corresponding to the drift‐related dynamic instability in the structural system. The goal of this paper is to select, from a general set of SGMRs, a small number of subsets such that each can be used for the reliable prediction of the mean collapse capacity of a particular group of structures, i.e. of single degree‐of‐freedom systems with a typical behaviour range. In order to achieve this goal, multivariate statistical analysis is first applied, to determine what degree of similarity exists between each selected small subset and the general set of SGMRs. Principal Component analysis is applied to identify the best way to group structures, resulting in a minimum number of SGMRs in a proposed subset. The structures were classified into six groups, and for each group a subset of eight SGMRs has been proposed. The methodology has been validated by analysing a first‐mode‐dominated three‐storey‐reinforced concrete structure by means of the proposed subsets, as well as the general set of SGMRs. The results of this analysis show that the mean seismic collapse capacity can be predicted by the proposed subsets with less dispersion than by the recently developed improved approach, which is based on scaling the response spectra of the records to match the conditional mean spectrum. Copyright © 2010 John Wiley & Sons, Ltd.     

Assessment of future changes in the maximum temperature at selected stations in Iran based on HADCM3 and CGCM3 models

Identification and assessment of climate change in the next decades with the aim of appropriate environmental planning in order to adapt and mitigate its effects are quite necessary. In this study, maximum temperature changes of Iran were comparatively examined in two future periods (2041-2070 and 2071-2099) and based on the two general circulation model outputs (CGCM3 and HADCM3) and under existing emission scenarios (A2, A1B, B1 and B2). For this purpose, after examining the ability of statistical downscaling method of SDSM in simulation of the observational period (1981-2010), the daily maximum temperature of future decades was downscaled by considering the uncertainty in seven synoptic stations as representatives of climate in Iran. In uncertainty analysis related to model-scenarios, it was found that CGCM3 model under scenario B1 had the best performance about the simulation of future maximum temperature among all of the examined scenario-models. The findings also showed that the maximum temperature at study stations will be increased between 1°C and 2°C in the middle and the end of 21st century. Also this maximum temperature changes is more severe in the HADCM3 model than the CGCM3 model.     

Spatio-temporal analysis of crime by developing a method to detect critical distances for the Knox test

The present study examined and compared spatio–temporal interaction of the theft of car parts, shop burglary and motorcycle theft in the central business district (CBD) of the city of Zanjan in Iran. The Knox test was selected to detect spatio–temporal interaction. This test has been criticized as being subjective because the selection of critical distances is arbitrary; thus, a method is proposed to detect critical distances in the Knox test using the mean distance, natural breaks classification of nearest neighbour (NN) distance and Ripley’s k function. Results show obvious differences between the spatio-temporal clusters of the three sets of crimes. They also indicate that changing the spatial cut-offs within a cluster creates different temporal patterns. Of the three criteria for determining critical distances, NN classification based on natural breaks showed more interactions than the other methods.