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A contribution to aeromagnetic deculturing in populated areas
Author(s) -
Beamish David
Publication year - 2017
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.12394
Subject(s) - context (archaeology) , regional geology , component (thermodynamics) , process (computing) , computer science , submarine pipeline , geology , automation , grid , environmental geology , data mining , noise (video) , remote sensing , seismology , artificial intelligence , geodesy , telmatology , tectonics , image (mathematics) , paleontology , engineering , mechanical engineering , physics , geotechnical engineering , operating system , thermodynamics
Modern regional airborne magnetic datasets, when acquired in populated areas, are inevitably degraded by cultural interference. In the United Kingdom context, the spatial densities of interfering structures and their complex spatial form severely limit our ability to successfully process and interpret the data. Deculturing procedures previously adopted have used semi‐automatic methods that incorporate additional geographical databases that guide manual assessment and refinement of the acquired database. Here we present an improved component of that procedure that guides the detection of localized responses associated with non‐geological perturbations. The procedure derives from a well‐established technique for the detection of kimberlite pipes and is a form of moving‐window correlation using grid‐based data. The procedure lends itself to automatic removal of perturbed data, although manual intervention to accept/reject outputs of the procedure is wise. The technique is evaluated using recently acquired regional United Kingdom survey data, which benefits from having an offshore component and areas of largely non‐magnetic granitic response. The methodology is effective at identifying (and hence removing) the isolated perturbations that form a persistent spatial noise background to the entire dataset. Probably in common with all such methods, the technique fails to isolate and remove amalgamated responses due to complex superimposed effects. The procedure forms an improved component of partial automation in the context of a wider deculturing procedure applied to United Kingdom aeromagnetic data.