Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

3-D Kernel Density Imaging Based on the Euler Deconvolution of Tensor Gravity Data

Version 1 : Received: 2 October 2020 / Approved: 5 October 2020 / Online: 5 October 2020 (10:36:52 CEST)

How to cite: Cao, S.; Yang, B.; Lu, G.; Hu, X.; Mao, Y.; Hu, S.; Zhu, Z. 3-D Kernel Density Imaging Based on the Euler Deconvolution of Tensor Gravity Data. Preprints 2020, 2020100059 (doi: 10.20944/preprints202010.0059.v1). Cao, S.; Yang, B.; Lu, G.; Hu, X.; Mao, Y.; Hu, S.; Zhu, Z. 3-D Kernel Density Imaging Based on the Euler Deconvolution of Tensor Gravity Data. Preprints 2020, 2020100059 (doi: 10.20944/preprints202010.0059.v1).

Abstract

Traditional discrimination techniques for Euler deconvolution use only the color spectrums of structural indexes, without considering the spatial distribution characteristics and inherent relationships among the Euler solutions to separate adjacent causative sources. In the present study, a new approach was developed for discriminating uncorrelated Euler solutions from coherent solutions based on the focusing levels indicated by the probability density distributions generated using multivariate kernel density estimations (KDE). A novel multiple coverage technique was proposed by using a series of different sized moving windows over gridded gravity data, which formed tight clusters of Euler solutions for different sized causative sources. The results of the probability density distributions were obtained using a 3-D KDE method for the Euler solution subsets {x, y, z} of synthetical models, and real data from a survey conducted in British Columbia (Canada) which had successfully established more credible and meaningful geological models when compared with three other subsets.

Subject Areas

full tensor gradient gravity; kernel density imaging; color spectrums; multiple-sources; Euler deconvolution; tight clusters

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