What are fault and geological interpretation vectors?
Fault and geological interpretation vectors represent the interpreted geometry and spatial distribution of faults, folds, shear zones, and other geological features, such as lithologies, within the subsurface.
These vectors are created by interpreting geological, geophysical, drilling data, and age dating to infer the position, age, and extent of subsurface structures. These vectors (along with structural data points) are components of a geological map which aid to unravel the evolutionary history of the rocks.
These vectors are essential for understanding the structural framework that controls mineral deposition, timing, and the movement of mineralizing fluids. They also help at understanding the current geometry of the different geological domains, giving information on spatial continuity of the lithologies and/or mineralization.
How is the data collected?
This data is typically generated through a combination of geological mapping, geophysical surveys (e.g., seismic, magnetic, gravity), drill core logging, and age dating. Field observations and geophysical data provide insights into the orientation and scale of faults and other structures, while drilling provides more detailed subsurface information. Age dating (geochronology) of rocks is achieved by choosing rocks with specific minerals which retain radioactive isotopes which can be used to establish relative dates through radiometric dating.
Advanced modeling software is then used to combine these datasets and generate 2D or 3D vector interpretations of geological features, such as fault 2D lines or 3D surfaces and geological 2D polygons or 3D solids.
What is the support of the data?
Fault and other geological interpretation vectors such as geological units are typically represented as vectors, where each line or polygon represents the inferred or interpreted location of a geological structure. These vectors are generated often through interpretation between locations of direct measurement of in-situ (in place) rock outcrops. These points of ‘high confidence’ measurements allow geologists to interpret across areas without direct measurements to other points of high confidence.
Similarly for drill data, you can collect measurements of vein orientations or faulting at a specific x,y,z location, but must interpret across the space between drill holes using geologic inference to build a subsurface model. In 3D models, these vectors are extruded to represent their orientation in three-dimensional space.
How is this data typically displayed in geoscientific software?
In software like ArcGIS, Leapfrog, Geoscience Analyst, or QGIS, these vectors are typically displayed as lines or surfaces in both 2D maps and 3D models.
Modifiable display elements:
Color and thickness of lines to represent fault types or confidence levels.
In 3D viewers, the dip and strike of faults can be visualized as a plane in their correct spatial context, which can be extended or shortened as needed based on confidence of surrounding supporting data.
Cross-sections and depth slices, which are plane slices through 3D objects, can be generated to better understand the relationship between faults and the geological layers they displace.
The transparency, scale, or labeling of features to highlight key structures and their relative importance to the mineral system can be adjusted.
What does it mean for geologists targeting mineral systems?
Faults and other geological structures play a crucial role in controlling the movement and deposition of mineralizing fluids:
Conduits for fluids: Faults often act as pathways for hydrothermal fluids, by increasing rock mass porosity, faults allow metals to be transported and deposited in certain zones. Interpreted faults and shear zones can guide geologists to areas where fluid flow may have been focused.
Traps and barriers: In addition to guiding fluid flow, faults or lithologies can also act as traps or barriers, depending on their associated permeability, leading to mineral deposition when fluids are blocked or slowed down. Understanding the geometry of these structures is critical for predicting where minerals might be concentrated. The hardness of different lithologies (rheological contrast) can also act as a trap or a focus point for mineralizing fluids.
Deposit offset due to faulting: Faults, both dip-slip (vertical movement), strike slip (lateral movement), or a combination of both can lead to a mineral deposit being offset or split into discrete lenses. Understanding the orientation and movement of faults such as this is crucial to delineating the faulted ore bodies.
This dataset provides a critical layer in the exploration process, allowing geologists to better understand the structural controls on mineralization as well as the age of the mineralization, aiding in targeting regions most likely to host economically viable deposits. It is important to note that these layers of information are heavily biased by prior knowledge and mineral deposit genetic models.
How is this used in the VRIFY AI targeting workflow?
Fault and geological interpretation vectors are compiled into shapefiles composed of either polylines (faults, folds, veins) or polygons (geological units, alteration maps). They are then processed by the VRIFY team in a preliminary Feature Engineering step to generate distance factor raster maps for features such as fault types, dykes, or rock units that are known to host mineralization.
These distance factors to structures are important for specific deposit styles as these are often the structural conduits which can focus mineralization. If you are far away from the fault, there is a lower likelihood of finding mineralization there. The geological map polygons can also be used for embedding with geochemical rasters to aid in extracting relationships between the lithology and elemental abundance.
These rasters are then incorporated into the Data Stack which the Predictive Modelling step will utilize to generate the VPS (VRIFY Prospectivity Score).
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