首页|Studies from Monash University Add New Findings in the Area of Machine Learning (Unsupervised Machine Learning and Depth Clusters of Euler Deconvolution of Magnetic Data: a New Approach To Imaging Geological Structures)
Studies from Monash University Add New Findings in the Area of Machine Learning (Unsupervised Machine Learning and Depth Clusters of Euler Deconvolution of Magnetic Data: a New Approach To Imaging Geological Structures)
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NETL
NSTL
Taylor & Francis
Investigators discuss new findings in Machine Learning. According to news reporting out of Clayton, Australia, by NewsRx editors, research stated, “We present a novel approach that determines the location and dip of geologic structures by clustering Euler deconvolution depth solutions using DensityBased 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.” Financial supporters for this research include Australian Society of Exploration Geophysics Foundation Grant, Geological Survey Victoria, Australian Society of Exploration Geophysics Foundation, Monash-IITB scholarship. Our news journalists obtained a quote from the research from Monash University, “We applied our method to global magnetic and high-resolution aeromagnetic datasets over Phanerozoic-Precambrian zonebounding 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 similar to 22 km in central Victoria and at a shallower depth of similar to 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.”
ClaytonAustraliaAustralia and New ZealandCyborgsEmerging TechnologiesMachine LearningMonash University
NETL
NSTL
Taylor & Francis
