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The Lut Block is a potential porphyry-style mineralized region in Iran including the well-explored Shadan porphyry gold-copper deposit, which has an extensive zone of gold- and copper-bearing stockwork-like sheeted veins. The structural setting of this deposit is a key to understanding the genesis of the mineralization. Our field studies show that the mineralization occurs as steeply dipping ore bodies along NW–SE transpressional faults. The zones with a high frequency of veins and volume of veins have a NW–SE trend, which is closely related to transpressional faults. We propose that such a compressional regime inhibits focused ore-forming fluid flow to higher levels in the crust. After a local change in stress field, fluid depressurization and channeling along transpressional faults generated the sheeted veins. This indicates an important role of these transpressional faults in focusing and controlling mineralization within this porphyry deposit, which has important implications for the exploration of porphyry deposits on a regional scale. The spatial distribution of sheeted veins is used to examine gold anomalies from lithogeochemical data extracted by fractal models. The results show that a combination of high vein density and high vein volume areas with gold anomalies could result in identifying areas with greater potential at the deposit to regional scale.

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The aim of this study was to delineate copper mineralization controllers in Nohkouhi volcanogenic massive sulfide (VMS) deposit by using geostatistical and fractal simulation. In this study, concentration-volume (C-V) fractal model has been used to indicate various copper populations related to different host rocks and copper minerals. Accordingly, uncertainty-volume (U-V) fractal model was applied to probability values achieved through sequential indicator simulation (SIS). Copper ores of Nohkouhi deposit including chalcopyrite and malachite were simulated in 30 realizations. The U-V fractal model obtained by using a probability map was divided into four probability zones (high, moderate, low, and very low) for copper minerals. Furthermore, copper grades were simulated for 10 times by sequential Gaussian simulation (SGS). Combination of C-V and U-V fractal modeling resulted in a hybrid method which could be properly employed to determinate various mineralization zones based on the relationship between quantitative (e.g. copper grade) and qualitative (e.g. copper minerals) variables. Moreover, integrating the results of C-V and U-V fractal modeling with the most frequent occurrence of rock type modeling helps identify copper mineralization controllers in a VMS deposit.

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