Integration of Geophysical and Geological Data Using Evidential Belief Function

Abstract

Several methods are available for integrating geophysical, geological, and remote sensing data sets and also for integrating them with additional information such as newly observed geophysical and geological data. Several published reports discuss successful application of different types of spatial information integration techniques including the geographical information system (GIs). There have also been theoretical developments including Bayesian approach in updating old data sets with newly acquired information. However, weaknesses and problems still exist. Many geological and geophysical data sets often have only partial coverage and in almost all cases have very different spatial resolution. These cause serious difficulties in certain cases. In this research the partial belief function approach is examined as a means to integrate one set of airborne and/or ground geophysical data with other available geological and geophysical data sets, and to update the existing information successively with newly observed data over target areas. In theory, the Dempster-Shafer method appears to be the most suitable method, but in practice several difficulties arise that must be overcome. One of the major difficulties is the dependency of the partial belief function on exploration targets, which can only be defined, at present, in a case-by-case approach. Ground EM

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