Age, source and stratigraphic implications of Pb isotope data for conformable, sediment-hosted, base metal deposits in the Proterozoic Aravalli-Delhi orogenic belt, northwestern India

Proterozoic tectonic evolution and metallogenesis in the Aravalli-Delhi orogenic complex, northwestern India

Mathematical geological models are being increasingly used by natural resources delineation and planning agencies for mapping areas of mineral potential in order to optimize land use in accordance with socio-economic needs of the society. However, a key problem in spatial-mathematical-model-based mineral potential mapping is the selection of appropriate functions that can effectively approximate the complex relationship between target mineral deposits and recognition criteria. This research evaluates a series of mathematical geological models based on different linear and non-linear functions by applying them to base-metal potential mapping of the Aravalli province, western India, where several significant base-metal deposits are already known. Linear models applied in this research are an extended weights-of-evidence model and a hybrid fuzzy weights-of-evidence model, while non-linear models are knowledge and data-driven fuzzy models, a neural network model, a hybrid neuro-fuzzy model and an augmented naive Bayesian classifier model. From a conceptual model of base-metal metallogenesis in the study area, host rock lithology, stratigraphic position, (palaeo-)sedimentary environment, association of mafic volcanic rocks and proximity to favorable structures are identified as recognition criteria for base-metal mineralization and, in the form of predictor maps, constitute the input for the models. The parameters of the knowledge-driven fuzzy model are estimated from the expert knowledge, while those of the neural network and Bayesian classifier model are estimated from the data. The two hybrid models use both expert knowledge and data for parameter estimation. As compared to the linear models, the non-linear models generally perform better in predicting the known base-metal deposits in the study area, including the known deposits that are characterized by unusual geological settings. This is attributed to the ability of non-linear functions to (a) better approximate the relation between mineral deposits and recognition criteria and (b) recognize and account for possible dependencies amongst recognition criteria. Although the linear models do not fit the data as efficiently as the non-linear models, they are easier to impleme nt using basic GIS functionalities and their parameters are more amenable to geoscientific interpretation. In addition, the linear models are less susceptible to the curse of dimensionality as compared to non-linear models, which makes them more suitable for applications to mineral potential mapping of the areas where there is a paucity of training mineral deposits. The hybrid models that conjunctively use both knowledge and data for parameter estimation generally perform better than purely knowledge-driven or purely data-driven models. This is attributed to the capability of the hybrid models to cross-compensate the deficiencies in either the knowledge-base or in the database. The output of various mathematical geological models indicate that about 10% of the study area has potential for base-metal deposits. The high-favorability zones tend to reflect a strong lithostratigraphic control on base-metal mineralization in the study area and the importance of crustal-scale faults in spatial localization of base-metal mineralizations in the study area. The low proportion of base-metal potential area delineated and the high prediction rates, which vary from 83% to 96% depending on the model applied, indicate not only (a) the efficiency of the mathematical geological models in capturing the complex relationship between the target mineral deposits and the deposit recognition criteria, but also (b) usefulness of the favorability maps as inputs to natural resources planning for optimizing land use in the study area.

Mineral potential mapping with mathematical geological models

Tectonostratigraphy and base-metal mineralization controls, Aravalli province (western India): New interpretations from geophysical data analysis

Tectonic evolution and base-metal mineralisation in the Aravalli–Delhi belt, India

The Dariba Main Lode is characterized by a complex assemblage of metallic sulphides and sulphosalts. Primary sedimentary fabric is evident by the occurrence of sphalerite, pyrite and occasionally galena, and rarely chalcopyrite, in the form of conformable layers, interbedded with the host rocks throughout the exposed length in the underground mine. Early diagenetic pyrite is found as tiny isolated grains as well as framboids, in which the first form is always dominating. This reflects two different pathways of pyrite formation. Distinct mesoscopic folds are found in the bedded ores which show their general trend congruous to the "major folds of the region. ' Healed' breccias and fractures often occur associated with the folded and conformable ores in which the sulphides appear mobilized and recrystallized. Folded pyrite-graphite schist laminites show excellently developed axial and bedding plane cleavages along which the pyrite has been recrystallized into aggregates of elongated crystals due to outlasting effect of shears. Pressure shadow structures showing elongated crystals of pyrite bordered by sphalerite and/or galena are also frequently observed along the bedding plane cleavages. Typical metablastic textures are exhibited by large euhedral crystals of pyrite and arsenopyrite and triple-point junctions shown by sphalerite and pyrite. The ore features suggest that the Rajpura-Dariba-Bethumni deposits are essentially syn-sedimentary.

D. S. Chauhan

Papers

The Dariba Main Lode of Rajpura-Dariba Zinc-Lead-Copper Belt, Udaipur District, Rajasthan