TY - JOUR
T1 - The application of evidential belief function in the integration of regional geochemical and geological data over the Ife-Ilesha goldfield, Nigeria
AU - Likkason, Othniel K.
AU - Shemang, Elisha M.
AU - Suh, Cheo E.
PY - 1997/10
Y1 - 1997/10
N2 - There are several methods available for the integration of geological, geophysical and even remotely sensed data sets. Several published reports have discussed the successful application of these integration techniques, including geologically orientated geographical information systems (GIS), to non-renewable resource exploration. Many geoscience data sets often have only partial coverage, and in most cases have different spatial resolution. These shortcomings plague statistical integration schemes employed in resource evaluation. In this paper, the evidential belief function approach is examined and applied to the integration of geological and geochemical data sets over the Ife-Ilesha goldfield, southwest Nigeria. In using this approach, nine maps of trace element concentrations (whole rock) and that of the bedrock geology of the test area were digitised. The characteristic grid values of the digitised maps were converted to degrees of belief, with a Au bearing mineral deposit as the target proposition. These resulting spatial data sets were pooled using the Dempster-Shafer combination rule. The resulting maps define portions of the study area with known auriferous mineral occurrences and even outline areas of past mining operations. There are additional areas with high interest that are not linked with known mineralisation and could therefore represent viable exploration targets. The Dempster-Shafer method of evidential reasoning appears to be suitable in identifying areas of known Au mineralisation in the test area. One of the major difficulties of this method is that the evidential belief function depends on the exploration target, which can only be defined, at present, by the interpreter's intuitive and qualitative knowledge of the environmental proposition being explored.
AB - There are several methods available for the integration of geological, geophysical and even remotely sensed data sets. Several published reports have discussed the successful application of these integration techniques, including geologically orientated geographical information systems (GIS), to non-renewable resource exploration. Many geoscience data sets often have only partial coverage, and in most cases have different spatial resolution. These shortcomings plague statistical integration schemes employed in resource evaluation. In this paper, the evidential belief function approach is examined and applied to the integration of geological and geochemical data sets over the Ife-Ilesha goldfield, southwest Nigeria. In using this approach, nine maps of trace element concentrations (whole rock) and that of the bedrock geology of the test area were digitised. The characteristic grid values of the digitised maps were converted to degrees of belief, with a Au bearing mineral deposit as the target proposition. These resulting spatial data sets were pooled using the Dempster-Shafer combination rule. The resulting maps define portions of the study area with known auriferous mineral occurrences and even outline areas of past mining operations. There are additional areas with high interest that are not linked with known mineralisation and could therefore represent viable exploration targets. The Dempster-Shafer method of evidential reasoning appears to be suitable in identifying areas of known Au mineralisation in the test area. One of the major difficulties of this method is that the evidential belief function depends on the exploration target, which can only be defined, at present, by the interpreter's intuitive and qualitative knowledge of the environmental proposition being explored.
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U2 - 10.1016/S0899-5362(97)00119-X
DO - 10.1016/S0899-5362(97)00119-X
M3 - Article
AN - SCOPUS:0031411944
SN - 0899-5362
VL - 25
SP - 491
EP - 501
JO - Journal of African Earth Sciences
JF - Journal of African Earth Sciences
IS - 3
ER -