Publication Type:
Journal ArticleSource:
Geophysical ProspectingGeophysical Prospecting, Blackwell on behalf of the European Association of Geoscientists & Engineers (EAGE), Houten, Netherlands, Volume 65, Number 6, p.1662-1679 (2017)ISBN:
0016-8025Abstract:
Geologically constrained inversion of gravity and magnetic field data of the Victoria property (located in Sudbury, Canada) was undertaken in order to update the present three-dimensional geological model. The initial and reference model was constructed based on geological information from over 950 drillholes to constrain the inversion. In addition, downhole density and magnetic susceptibility measured in six holes were statistically analysed to derive lower and upper bounds on the physical properties attributed to the lithological units in the reference model. Constrained inversion of the ground gravity and the airborne magnetic data collected at the Victoria property were performed using GRAV3D and MAG3D, respectively. A neural network was trained to predict lithological units from the physical properties measured in six holes. Then, the trained network was applied on the three-dimensional distribution of physical properties derived from the inversion models to produce a three-dimensional litho-prediction model. Some of the features evident in the lithological model are remnants of the constraints, where the data did not demand a significant change in the model from the initial constraining model (e.g., the thin pair of diabase dykes). However, some important changes away from the initial model are evident; for example, a larger body was predicted for quartz diorite, which may be related to the prospective offset dykes; a new zone was predicted as sulfide, which may represent potential mineralisation; and a geophysical subcategory of metabasalt was identified with high magnetic susceptibility and high density. The litho-prediction model agrees with the geological expectation for the three-dimensional structure at Victoria and is consistent with the geophysical data, which results in a more holistic understanding of the subsurface lithology. Abstract Copyright (2017), European Association of Geoscientists & Engineers.
Notes:
GeoRef, Copyright 2018, American Geological Institute.<br/>809535-17