One of the most important stages of mining exploration is modeling of mineral prospectivity and identifying favorable areas. For this manner, iecting and developing new methods in mineral modeling is so important due to increasing the probability or reduction of exploration risk. The so-called methods reduce exploratory costs simply and efficiently. In this study, the methods of separating background values and anomalies such as median absolute deviation (MAD) and concentration-area fractal analysis were used to map uni-element geochemical anomalies. Also, multivariate statistical methods such as staged factor analysis and cluster analysis were used to determine important elements and association between them. Due to the geological and alteration evidences in the study area, including the presence of minerals such as alunite, barite, malachite, iron oxide and arsenic (scrudite) and vuggy silica-breccia veins, as well as important elements including Au, Ag, As, Ba, Bi, Cu, Pb and Sb, mineralization type in the exploration area of "Kuh-e Lakht" was considered as high sulfidation epithermal deposit. The new method of Cell Based Associations (CBA) has been used to distinguish the high potential mineralization area via geochemical and geological modeling. CBA is an innovative mineral favorability procedure designed to answer special needs of the mining industry in data wise critical situations where usual favorability methods may not yield satisfactory results. Those situations relate to input data quality. The principle of CBA consists in replacing polygons of geological units with a square cell grid (hence the ‘cell-based’). Each cell contains a range of units (‘association’) that are binary coded in terms of their presence (1) or absence (0) with in study area. Lithological associations are considered as binary spectra and as such are dir=rtl align=center
