Ore deposits that contain valuable minerals are commonly formed within faults and fractures in structurally complex areas. Structural geology is a critical part of understanding the deformational histories of rock structures to locate the physical conditions where target minerals and metals will most likely be found.
Our team of geologists and geochemists have the technical expertise and experience to accurately map and model these formations and properties, providing clear drill target guidance, as well as a larger understanding of the stability and water in-flow implications that could impact further mine development.
We have a unique approach to using observations from existing geological data – including physical surface samples and a range of geophysics data – and reinterpreting that information to map deformations of the rock, specifically in areas where the rock has been broken or deformed in ways that are relevant to ore deposit exploration.
Field observations of this nature are often sparse, and the resultant data equally incomplete. We use our analytical processes to interpolate the available data to create a higher resolution structural continuity. Where these results would normally be interpreted manually, we automate the interpretation of the geophysics layers to produce structural geology data, avoiding the variability of interpretation introduced by individual geologists.
Once we have a full field structural interpretation, it can be analyzed for breaks and faults, to better understand and map the folding and offsets within the rock. This informs strategies for exploration, and can become input data for further machine learning.
Lineaments are critical to providing information about the orientation of geological features, whether they demarcate a discontinuity, a texture change or change in rock type, or crossing or junction of two structures. We use a broad range of lineament types to reveal a range of geological information, including stages of deformation, isolating margins of rock types, the locations of ore body offsetting faults and ore body constructive faults, in order to build more complete maps and models.
While much of this laborious process is executed manually by our team, we are beginning to improve the efficiency and cost effectiveness of the process by training deep learning algorithms to automate this task using image analytics. These lineaments can then be used as input information to machine learning stacks or simply to better guide the understanding of the geology of an area.
This structural analysis allows us to map areas of tectonic stress that occurred during the ore forming process, to hone in on the regions that were most likely to fracture and capture ore. We use numeric modeling to create robust 3D computer models that use multiple colours to map heat stretch zones and other areas that capture the complete dynamism of the area.