| Title | Remote predictive surficial materials and surficial geology mapping: Marian River, NTS 85-N, NWT |
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| Author | Ednie, M; Kerr, D E; Olthof, I; Wolfe, S A ; Eagles, S |
| Source | Northwest Territories Geoscience Office, Yellowknife Geoscience Forum Abstracts Volume 2013, 2013 p. 72-73  Open Access |
| Links | Online - En ligne
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| Image |  |
| Year | 2013 |
| Alt Series | Earth Sciences Sector, Contribution Series 20130263 |
| Publisher | Northwest Territories Geoscience Office |
| Meeting | 2013 Yellowknife Geoscience Forum; Yellowknife; CA; November 19-21, 2013 |
| Document | serial |
| Lang. | English |
| Media | paper |
| Related | This publication is related to the following
publications |
| Province | Northwest Territories |
| NTS | 85N |
| Area | Marian River |
| Lat/Long WENS | -118.0000 -116.0000 64.0000 63.0000 |
| Subjects | surficial geology/geomorphology; remote sensing; mapping techniques; computer mapping; permafrost; glacial deposits; Landsat 7; SPOT5; Cenozoic; Quaternary |
| Program | GEM: Geo-mapping for Energy and Minerals GEM Tri-Territorial Information management & databases (Tri-Territorial Surficial Framework) |
| Released | 2013 01 01 |
| Abstract | Despite the relatively detailed knowledge of bedrock geology in the high mineral potential southern Bear-Slave region, knowledge of surficial sediments, permafrost extent, and geotechnical conditions is
still limited in many areas. This lack of basic geoscience information hinders the understanding of present and future terrain risks to roads and other infrastructure, which are vital to sustainable northern economic development.
The preliminary
remote predictive surficial materials map for the Marian River NTS Map Sheet 85-N is derived using Landsat 7 imagery (normalized bands 2,3,4,5 and 7), a digital elevation model and forest fire history. The spectral signatures associated with bedrock,
silty clay, diamicton, sand, modern lacustrine and organic units were established using ¿training areas¿ determined from traditional airphoto interpretation and limited field validation data. A high level of statistical separation between the
training area classes indicates that spectral differences exist for each surficial unit and a reasonable model can be built to map this region.
The central and eastern parts of the preliminary map indicate silty clay infilling bedrock depressions
and topographic lows between 157 m (current elevation of Great Slave Lake) and about 220 m asl. At elevations above 220 m, silty clay is less extensive, and isolated occurrences of diamicton in the form of reworked till veneer exist, as well as till
blanket. The high spatial density of silty clay generally below 220 m contributes to the reconstruction of glacial Lake McConnell (estimated maximum elevation of 280-300 m) and identifies the distribution of thaw-sensitive silty clay terrain. Both
exposed and vegetated outcrops in the Bear and Slave terrains were also identified, although distinguishing potential bedrock of the Interior Platform was more problematical.
Remote predictive materials maps provide a first order assessment of
surficial sediments, which can guide traditional surficial geology mapping efforts and offer regional information for geological interpretations and decision making processes related to infrastructure. From these predictive maps, together with field
data, surficial geology maps can be derived as an aid to mineral exploration. The methodology used here builds on the success of the predictive surficial geology maps for the Yellowknife area, NTS 85-J (Geological Survey of Canada Open File 7108) and
the Hearne Lake area, NTS 85-I (Geological Survey of Canada Open File 7233), and will integrate SPOT5 satellite imagery and topographic characteristics calculated from CDED data to improve mapping capabilities and accuracy. |
| Summary | (Plain Language Summary, not published)
Remote predictive materials maps provide a first order assessment of surficial sediments, which can guide traditional surficial geology mapping efforts
and offer regional information for geological interpretations and decision making processes related to infrastructure. From these predictive maps, together with field data, surficial geology maps can be derived as an aid to mineral
exploration. |
| GEOSCAN ID | 293111 |
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