Abstract
Digital datasets were used to build a three-dimensional (3D) GOCAD model that estimates the thickness of unconsolidated Quaternary
glacial and non-glacial sediments covering bedrock in the Ootsa Lake area of west-central British Columbia. The area hosts several porphyry
Cu±Mo±Au deposits and the past-producing Huckleberry Cu-Mo mine. Estimates of drift thickness in the depth-to-bedrock model were based
on interpolation between bedrock surfaces identifi ed in diamond drillholes, outcrop, and surface topography derived from aerial Light Detection
and Ranging survey (LiDAR) data. Where combined with geophysical data, structural (fault) data, and geochemical anomalies identifi ed in soils
from Regional Geochemical Survey (RGS) data, the depth-to-bedrock model is a useful aid in ranking exploration targets and understanding
subsurface paleotopography. Most simply, geochemical anomalies in areas of shallow drift may be ranked higher than similar geochemical
anomalies in areas of thicker drift because drilling is less costly and drillholes are easier to complete. However, not all areas of thick drift are
necessarily low ranking targets in the present study. For example, anomalies that abruptly disappear or show decreased intensity upon crossing
known post-mineralization faults and entering areas of thick drift may refl ect transitions to grabens that contain deeper mineralized stratigraphy.
Such geological interpretations, supported by geophysical data, may allow for a high-rank assignment to such grabens. The depth-to-bedrock
map also may help identify previously undocumented faults, such as where abrupt changes in drift thickness defi ne a strong linear feature with
signifi cant strike length. The construction of a 3D depth-to-bedrock GOCAD model on a mineral exploration property with suffi cient existing
digital data from drilling, mapping, and geochemical surveying is an inexpensive way to further interrogate the geology of a property and assess
its exploration potential.
glacial and non-glacial sediments covering bedrock in the Ootsa Lake area of west-central British Columbia. The area hosts several porphyry
Cu±Mo±Au deposits and the past-producing Huckleberry Cu-Mo mine. Estimates of drift thickness in the depth-to-bedrock model were based
on interpolation between bedrock surfaces identifi ed in diamond drillholes, outcrop, and surface topography derived from aerial Light Detection
and Ranging survey (LiDAR) data. Where combined with geophysical data, structural (fault) data, and geochemical anomalies identifi ed in soils
from Regional Geochemical Survey (RGS) data, the depth-to-bedrock model is a useful aid in ranking exploration targets and understanding
subsurface paleotopography. Most simply, geochemical anomalies in areas of shallow drift may be ranked higher than similar geochemical
anomalies in areas of thicker drift because drilling is less costly and drillholes are easier to complete. However, not all areas of thick drift are
necessarily low ranking targets in the present study. For example, anomalies that abruptly disappear or show decreased intensity upon crossing
known post-mineralization faults and entering areas of thick drift may refl ect transitions to grabens that contain deeper mineralized stratigraphy.
Such geological interpretations, supported by geophysical data, may allow for a high-rank assignment to such grabens. The depth-to-bedrock
map also may help identify previously undocumented faults, such as where abrupt changes in drift thickness defi ne a strong linear feature with
signifi cant strike length. The construction of a 3D depth-to-bedrock GOCAD model on a mineral exploration property with suffi cient existing
digital data from drilling, mapping, and geochemical surveying is an inexpensive way to further interrogate the geology of a property and assess
its exploration potential.
| Original language | English |
|---|---|
| Pages (from-to) | 217-227 |
| Number of pages | 11 |
| Journal | Geological Fieldwork, British Columbia Ministry of Energy, Mines, and Petroleum Resources |
| Volume | 2018 |
| Issue number | 1 |
| Publication status | Published - 2018 |
| Externally published | Yes |