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Known to be triggered and controlled by tectonic systems, magmatism plays a significant role in the deposition and emplacement of hydrothermal mineral systems. It is, therefore, of paramount importance to recognize the tectonic processes that are genetically associated with hydrothermal mineral systems. This study seeks to address this research gap by recognizing the main tectonic processes that have controlled the distribution of porphyry copper (Cu) deposits in Dehaj Area on the Urmia-Dokhtar magmatic belt, central Iran. For this purpose, spatial associations of 31 known porphyry Copper deposits, faults, and fractures were evaluated by multiple numerical techniques including fry, fractal, (box-counting and radial-density methods), and distance-distribution analyses to investigate structural controls of the porphyry Copper mineralization. Results of the fry analysis revealed three mineralization trends, namely NS, NE, and NW, clearly following the existing fault systems in the area. Application of the fractal method demonstrated that structural controls on mineralization have operated on two different scales, regional and local scales. Distance-Distribution analysis was further used to spatially correlate known porphyry Copper deposits to fault traces, supplementing the results of fry and fractal analyses by quantitative measurements. Compiling the results of these three methods, it was shown that NW-trending faults have plausibly controlled the magmatism on a regional scale. On a local scale, however, NE- and N-trending faults have probably acted to control the channelling and emplacement of mineral-bearing fluids. [ABSTRACT FROM AUTHOR]

It is much easier to measure solid phase data or particle size distribution (PSD) than pore size distribution, therefore it is useful to determine relationship between fractal dimension of the soil particle size distributions (DPSD) and soil water retention curve ( D S W R C ). The present research aims to firstly measure fractal dimension of the soil PSD using three radii 1 (clay), 25 (silt) and 1000 (sand) µm (DPSD3) and subsequently compare it with fractal dimension obtained from DPSD, and secondly develop a relationship between D P S D 3 and D S W R C . The results showed that the maximum and minimum values for D P S D were 2.93 and 2.65 and for D P S D 3 were 2.91 and 2.63 and there was no significant difference between D P S D 3 and D P S D . A linear relationship between D P S D 3 and D S W R C was developed for some of soils UNSODA databases. The relationship developed here was compared with three present methods to calculate D S W R C from soil texture including (1) non-linear relationship with clay, (2) logarithmic relationship with clay, and (3) percent of sand, silt and clay for some of the northern soil of Iran. The results indicated that the proposed method in this study, especially in fine-grained soils, can efficiently predict D S W R C with appropriate precision. [ABSTRACT FROM AUTHOR]

Earthquakes in Iran and neighbouring regions are closely connected to their position within the geologically active Alpine-Himalayan belt. Modern tectonic activity is forced by the convergent movements between two plates: The Arabian plate, including Saudi Arabia, the Persian Gulf and the Zagros Ranges of Iran, and the Eurasian plate. The intensive seismic activity in this region is recorded with shallow focal depth and magnitude rising as high as Mw = 7.8. The study region can be attributed to a highly complex geodynamic process and therefore is well suited for multifractal seismicity analysis. Multifractal analysis of earthquakes (mb ≥ 3) occurring during 1973 – 2006 led to the detection of a clustering pattern in the narrow time span prior to all the large earthquakes: Mw = 7.8 on 16.9.1978; Mw = 6.8 on 26.12.2003; Mw = 7.7 on 10.5.97. Based on the spatio-temporal clustering pattern of events, the potential for future large events can be assessed. Spatio-temporal clustering of events apparently indicates a highly stressed region, an asperity or weak zone from which the rupture propagation eventually nucleates, causing large earthquakes. This clustering pattern analysis done on a well-constrained catalogue for most of the fault systems of known seismicity may eventually aid in the preparedness and earthquake disaster mitigation. [ABSTRACT FROM AUTHOR]