
%0 Journal Article
%A Anderson-Mayes, Ann-Marie 
%A J. Street, Gregory 
%T A conceptual framework for interpretation of airborne geophysical data
%J ASEG Extended Abstracts
%D 2003/08/01
%V 2003
%N 2
%P 1-5
%K 
%R https://doi.org/10.1071/ASEG2003AB002
%U https://www.publish.csiro.au/paper/ASEG2003AB002 
%X The new generation of high quality airborne geophysical data is rich in information reflecting the complexity and variability of the subsurface, yet methodological approaches to extracting this geological information have remained largely unchanged and poorly documented. The vast majority of literature either discusses computational interpretation techniques or reports the results of a geological interpretation of geophysical data. In this paper we address this problem by presenting a conceptual interpretation framework to represent the general transformation of geophysical data into geological knowledge. The framework is based on five fundamental tasks ? spatial pattern identification, spatial correlation, computational interpretation, geological inference and target identification. Our purpose in developing this framework is to enable the development of robust, repeatable computer-assisted interpretation methodologies. For a specific interpretation problem, the framework can be used to design an appropriate interpretation methodology that clearly identifies which tasks are assigned to which data set(s), and how these tasks are linked to produce the required outcomes of the interpretation. In particular, it enables the interpreter to consider using computer-based analysis to complete some aspects of the interpretation that might normally be completed using a conventional, manual approach. For example, in this paper we apply the framework to develop an interpretation methodology for a multivariate geophysical study of dryland salinity. This structured approach does not sacrifice the vital human-centred elements of the interpretation, but rather enables the interpreter to explore avenues to integrate those human-centred tasks with computer-assisted processes. 
