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GIS IN GEOLOGY Miloš Marjanović Lesson 6 11.11.2010.

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Presentation on theme: "GIS IN GEOLOGY Miloš Marjanović Lesson 6 11.11.2010."— Presentation transcript:

1 GIS IN GEOLOGY Miloš Marjanović Lesson 6 11.11.2010.

2 GIS in Mineral Deposit Exploration 1. Selecting the construction material (sand and gravel) excavation site by Boolean logic method. Fictive case study for prescriptive modeling (refer to related excercise). 2. Mineral potential mapping by fuzzy logic (knowledge-driven) multi- criteria analysis. The case study of Gold mineral potential map (from the textbook GIS for Geoscientists, Meguma terrain, Nova Scotia) for the predictive modeling.  Accent on GIS implementation within heuristic and statistical modeling

3 Geostatistics Database Management Systems (DBMS) Image Processing (IP) Computer Aided Drawing (CAD) Artificial Intelligence(AI) General statistics Spread- sheets Desktop and Web publishing Desktop mapping Contouring and surface modeling Geographic Information System (GIS) GIS in Mineral Deposit Exploration

4 1. Construction material site selection Conceptual model example: Limestone rock is necessary for a pile-dam construction There are no explicit physically or mathematically based models (they are not necessary) Locate suitable excavation sites (concerning material quality, cost, ecology and so forth) The model cuts down to a logistic problem solved with Boolean model Modeling the necessary inputs: Geological model (limestone units disposition) Distance to the construction site (proximity to the construction site for lowering the cost) Distance to road network (proximity to road for easier and cheaper loading) Slope angle (steeper limestone outcrops are easier for excavation and loading) Land use or vegetation cover map (to avoid the disruption of ecosystem e.g. forest, agricultural or urban terrain units)

5 GIS in Mineral Deposit Exploration 1. Construction material site selection * Simplified geological map showing the class of suitable material (limestone units) Distance from the construction site (float raster) with threshold of e.g. > 5km from the construction site (5km in range) Distance from road network (float raster) with threshold of e.g. >2 km of the road Slope (float raster from DEM) with threshold <10° of the slope angle Vegetation cover map – raster of disposition of forestland vs. grassland * the exercise example

6 GIS in Mineral Deposit Exploration 1. Construction material site selection Simple Boolean model: Conditional statements: 1. the site must be within limestone units AND 2. must be less than 5 km away fro the dam AND 3. must be less than 2km off the road AND 4. must be steeper than 10° AND 5. must be off the forestall area To wrap-up: Simple but conservative method It is not likely that all the inputs are equally important Decision trees could be created and expert systems applied

7 2. Mineral potential mapping Case study – Nova Scotia, Meguma Conceptual model & deposit prediction: Conceptual model governs the input layers selection and model (weighting method) selection Mineral deposits are usually to complex to be modeled by deterministic approach. Heuristic (knowledge-driven), statistical (data-driven) or hybrid models are used instead. The objective is to locate the zones of certain favorability of mineralization Version of fuzzy logics method (heuristic or knowledge-driven) will be presented Deposit type model : Hydrothermal phase of the post-volcanic processes (<250°C) Crystallization in quartz veins Association with Arsenopyrite (As-S) and Stibnite/Antimonite (Sb-S) occurrences GIS in Mineral Deposit Exploration

8 2. Mineral potential mapping Deposit model principle facts according to the existing exploitation sites: Pz units of meta-sediments and slates are rich in Au content (not in granite) Contacts between those two formations appear to be Au productive Anticline regions of those unites are particularly typical for mineralization (due to clivage) Association with sulphidic minerals of As and Sb Reactivation of stresses in SW-NE directions controlled the migration of gold- bearing fluids and therefore, NW-SE structures could be interesting for gold deposits

9 2. Mineral potential mapping – inputs: Geological map (GEOL) Digitized after a existing hard-copy Goldville formation (Pz – sand, silt) Halifax formation (Pz - slate) Granite (D) GIS in Mineral Deposit Exploration

10 2. Mineral potential mapping – inputs: Distance to anticlines (ANTI) Buffer from anticline vector (created after LANDSAT satellite image analysis) with 1km intervals

11 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – inputs: Distance to Goldenville-Halifax contact (GOLDHAL) Buffer from geological boundaries vector, with 1 km intervals

12 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – inputs: Distance to NW-SE structures (NWLINS) Buffer from structural analysis vector (created by LANDSAT imagery analysis)

13 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – inputs: Lake sediments Au (LSAU) Geochemical concentrations in lake sediments

14 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – inputs: Lake sediments Sb (LSSB) Geochemical concentrations in lake sediments

15 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – inputs: Lake sediments As (LSAS) Geochemical concentrations in lake sediments

16 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – inputs: Lake sediments W (LS0W)* Geochemical concentrations in lake sediments * Geochemical layers are suitable for fuzzy OR combination or by fuzzy AND in relation with SO 4

17 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – inputs: Au in vegetation (BIOAV) Concentrations in coniferous vegetation (Fir Balsam)

18 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – inputs: As in vegetation (BIOAS) Concentrations in coniferous vegetation (Fir Balsam)

19 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – Fuzzy logic modeling Fuzzy membership functions Membership range instead of TRUE or FALSE values Per each class of the layer and per each layer a membership function is assigned Any algebraic function is possible (non-linearity is encouraged)

20 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – Fuzzy logic modeling IMAGE Combining fuzzy membership functions Fuzzy AND Fuzzy OR Fuzzy Algebraic Product Fuzzy Algebraic Sum Gamma Operation γ=0,1

21 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – Fuzzy logic modeling scheme

22 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – output: Fuzzy logic AND/OR – intermediate model Fuzzy gamma operation – final model

23 GIS in Mineral Deposit Exploration 2. Mineral potential mapping – wrap-up: Method comparison: Boolean logic Bivariate statistics Multivariate statistics Fuzzy logic


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