Fuzzy inference systems for prospectivity modeling of mineral systems and a case-study for prospectivity mapping of surficial Uranium in Yeelirrie Area, Western Australia

Imaging sedimentary basins from high-resolution aeromagnetics and texture analysis

“…This can be used in identifying palaeo-surfaces and palaeo-drainage systems that are important for targeting placer deposit mineralization (Anand and Butt, 2010). The current approaches for modeling prospective palaeochannels for these surficial mineral systems from remote sensing and satellite data utilise radiometric, ASTER, surface geochemical maps and digital elevation models as input data (Porwal et al, 2015), but in general do not use magnetic data grids as spatial proxies. This limits the ability of these prospectivity models being applied under cover which we see as a promising possible application of automated aeromagnetic data grid texture domaining.…”

Modelagem de Dados Geofísico-espaciais por Lógica Fuzzy Aplicada ao Distrito Ferrífero Nova Aurora, Norte de Minas Gerais

“…Como o problema focado (ou seja, pesquisar minério de ferro oculto sob as chapadas amplamente cobertas por laterita) dificulta a aplicação de limites rígidos entre as classes de dados, a lógica fuzzy foi aplicada a fim de produzir modelos orientados pelo conhecimento (An, Moon, W.M. & Rencz 1991;Bonham-Carter 1994;Porwal et al 2015). O modelo utilizado para este estudo de caso foi aquele aplicado por Voll, Silva & Pedrosa-Soares (2020) em escala regional, com o objetico de rastrear rochas ricas em ferro.…”

Rare earth elements associated with carbonatite–alkaline complexes in western Rajasthan, India: exploration targeting at regional scale

“…The outputs of both data-driven and knowledge-driven prospectivity models are subject to two types of uncertainties (Porwal et al, 2003;Lisitsin et al, 2014), namely, systemic (or epistemic) and stochastic (or aleatory). Systemic uncertainties arise from the incomplete understanding of the geological process involved in the formation of the mineral deposit, leading to imperfect models.…”

“…Stochastic uncertainties arise from the limitations of the primary and derivative processed datasets, including the algorithms used to derive them. These uncertainties are the results of inaccuracy or imprecision in measurements and observations, data interpolations, and inconsistent data coverage (Porwal et al, 2003;McCuaig et al, 2009;Lisitsin et al, 2014). However, most published prospectivity modelling studies do not specifically deal with uncertainties in model outputs.…”