Abstarct: Mineral potential mapping (MPM) deals with the analysis and integration of spatial evidence laxyers derived from individual multi-source datasets to map and rank prospective areas for further exploration of the mineral deposit-type sought. To assign weights to classes of spatial evidence laxyers in order to create predictor maps, either knowledge- or data-driven methods, as two common approaches for MPM are used. In knowledge-driven methods, which are appropriate in greenfield (or poorly explored) areas, subjective judgment of an expert analyst is used in assigning weights to classes of a spatial evidence laxyer. In the result of data-driven MPM methods, the positions of generated target areas in final prospectivity models are affected by the spatial positions of known mineral occurrences that is, in fact, a systematic error in data-driven MPM. Maps of geochemical anomalies are among the common spatial evidence laxyers used in MPM, which can be derived independent of methods for MPM. In this regard, the way of generating geochemical evidential map is a challenging issue. The main topic of the present research thesis is (a) analysis of stream sediment geochemical data to derive a multi-element anomalous signature of the mineral deposit-type sought, (b) objective assignment of fuzzy weights to classes of derivative geochemical values for integration with other spatial evidence laxyers in MPM and (c) mapping stream sediment geochemical variables. In this regard, three new approaches including staged factor analysis (SFA) to derive best multi-element anomalous signatures of the mineral deposit-type sought, geochemical mineralization probability index (GMPI) to assign weights to classes of geochemical variables objectively, and weighted drainage catchment basin approach (WDCB) to map stream sediment geochemical variables, have been developed. Beside the foregoing approaches, a new concept, called discrimination index (DI) has been developed to evaluate the methods of anomaly separation in geochemical exploration. Furthermore, two approaches for assigning objective weights to fault density maps, and also maps of distances from geological features have been developed. As a result, the present thesis highlights the following findings in an attempt to improve existing methods for mineral prospectivity mapping especially, in representation of geochemical evidence: (a) Staged factor analysis increases the percentage of total explained data variance by recognition and removal of non-indicator components and elements. (b) Anomaly intensity, especially near and around known mineral deposit occurrences, is enhanced in the GMPI approach compared to ordinary factor analysis. (c) The GMPI approach can be used in knowledge-driven mineral potential mapping as a new exploratory data analysis tool to generate a weighted evidential map. (d) Anomalies mapped in WDCB models of stream sediment geochemical landscapes exhibits stronger positive spatial associations with other indicative geological features of the mineral deposit-type sought, and a map of WDCB can directly be used in fuzzy logic MPM as a weighted geochemical evidence laxyer. (e) Using the WDCB approach, the disadvantage of mapping anomalies through SCB modeling or contouring in terms of strong dependence on sample locations and sampling density is avoided. Finally, using the new approaches, developed in this thesis, the prediction rate and the probability of exploration success in mineral potential mapping have been enhanced and improved in comparison with previous methods.