1887
Volume 55, Issue 3
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533
PDF

Abstract

We present a novel approach that determines the location and dip of geologic structures by clustering Euler deconvolution depth solutions using Density-Based Spatial Clustering Applications with Noise (DBSCAN). This method and workflow rely on the association of changes in the location and relationships between Euler depth clusters and cluster boundaries with changes in rock susceptibility. We applied our method to global magnetic and high-resolution aeromagnetic datasets over Phanerozoic-Precambrian zone-bounding faults in west and central Victoria. The architecture of these structures at different scales from this imaging technique is comparable to interpreted 2D seismic reflection data. The results from the global magnetic data resolved the architecture of these structures below 5 km, while the aeromagnetic data used were limited to structural information of faults above 2 km depth. Therefore, this method shows the structural relationship of the west-dipping Avoca Fault that soles into the east-dipping Moyston Fault at a depth of ∼22 km in central Victoria and at a shallower depth of ∼15 km southward beneath the Quaternary basaltic rocks of the Newer Volcanic Province. In the vicinity of the Heathcote Zone, the method resolves the location, dip, and overprinting relationship between faults and extrusive rocks, such as the relationship between the Heathcote and Mount William Faults and the granitic Cobaw Batholith. We show how combining magnetic data at various scales can track faults from the near-surface to deeper roots while avoiding possible over-interpretation. We demonstrate how to optimise the DBSCAN parameters and a sensitivity analysis of how to determine clusters and cluster boundaries that are geologically relevant in the absence of geological constraints. Our technique provides an effective and rapid tool for imaging structures and can supplement complex and expensive imaging techniques to resolve the architecture of structures in complex geologic terrains.

© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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/content/journals/10.1080/08123985.2023.2299475
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Unsupervised machine learning and depth clusters of Euler deconvolution of magnetic data: a new approach to imaging geological structures
Exploration Geophysics 55, 223 (2024); https://doi.org/10.1080/08123985.2023.2299475
/content/journals/10.1080/08123985.2023.2299475
/content/journals/10.1080/08123985.2023.2299475
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  • Article Type: Research Article
Keyword(s): Euler deconvolution; Fault architecture; magnetic data; unsupervised machine learning

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