Geological control in 3D stratigraphic modeling, Oak Ridges Moraine, southern Ontario. Logan, C., Russell, H. A. J., and Sharpe, D. R.

Transcription

1 Geological control in 3D stratigraphic modeling, Oak Ridges Moraine, southern Ontario Logan, C., Russell, H. A. J., and Sharpe, D. R.

2 Rationale Increasing urbanization in the Greater Toronto Area is creating a larger demand for water resources and difficult decisions for land use planners and all levels of government. Many site-specific studies have been completed, but a basin scale framework in which to better interpret the results was lacking. Oak Ridges Moraine National Mapping Program (NATMAP) project initiated in 1993 to develop a regional geological understanding and provide a basis for regional hydrogeological flow modeling.

3 Data sources Existing archival data Geological mapping sites (e.g. Ontario Geological Survey, University of Guelph Geotechnical boreholes (e.g. Consumers Gas, Ontario Ministry of Transportation, UGAIS) Hydrogeological boreholes (e.g. Eldorado Nuclear, Interim Waste Authority, Ministry of Natural Resources, LLRWMO, Siting Task Force Secretariat, Ontario Ministry of Environment, municipal test wells, consultant reports) New data Geological Survey of Canada & Ontario Geological Survey mapping sites, river and bluff sections Deep continuously-cored boreholes Seismic reflection data 1: scale surficial geological maps

4 Modeling in the ORM Advantages Large amounts of existing borehole data Continuous geological map coverage New continuous core and reflection seismic data Conceptual model of stratigraphic relationships Disadvantages Large, geologically complex study area Archival borehole records are of variable quality Archival boreholes are clustered Archival boreholes lacked standardized geological description Archival boreholes were not interpreted stratigraphically

5 ORM Model Strategy 1. Use existing literature and geological mapping techniques to formulate and refine a conceptual model of stratigraphic relationships

6 ORM Model Strategy 2. Assembled database of new and archival borehole records with standardized textural codes. MOE water wells require errorvetting and de-clustering and are handled separately from higher quality data at this point DEM MOE water wells High quality Boreholes

7 ORM Model Strategy 2. Assembled database of new and archival borehole records with standardized textural codes. MOE water wells require errorvetting and de-clustering and are handled separately from higher quality data at this point DEM High quality Boreholes

8 ORM Model Strategy 3. High quality boreholes were interpreted and stratigraphic coding was applied using geological knowledge and expertise gained through field mapping program. Surficial Geology Polygons Textural coding

9 ORM Model Strategy 3. High quality boreholes were interpreted and stratigraphic coding was applied using geological knowledge and expertise gained through field mapping program. Surficial Geology Polygons Preliminary stratigraphic surface TIN Stratigraphic coding

10 ORM Model Strategy 4. Location verified and declustered MOE water wells were interpreted and coded automatically with the aid of preliminary training surfaces and mapped geology. Stratigraphic coding

11 ORM Model Strategy 4. Location verified and declustered MOE water wells were interpreted and coded automatically with the aid of preliminary training surfaces and mapped geology. Stratigraphic coding

12 ORM Model Strategy 5. High quality control boreholes, surface geological map polygons and MOE water wells combined to interpolate final surfaces.

13 Spatial Data Model - Structure Based on: Law of Superposition i.e., in the absence of deformation, younger strata overlie older strata Model must account for areas of nondeposition

14 Spatial Data Model - Structure Based on: Law of Superposition i.e., in the absence of deformation, younger strata overlie older strata Model must account for areas of nondeposition and erosion

15 Spatial Data Model - Structure Model is a layered sequence of continuous surfaces that represent stratigraphic unit top elevation Model surface is continuous

16 Spatial Data Model - Structure Stratigraphic unit thickness is derived by subtracting one surface from the next

17 Spatial Data Model - Structure Stratigraphic unit thickness is derived by subtracting one surface from the next

18 Database Setup Missing stratigraphy needs to be accounted for in all boreholes Interval types are flagged to determine how they will be used A Unit Interval Type <null> normal normal normal bottom Interval Type B Borehole ID Strat Unit Depth to Unit Unit top Interval Type 5 normal 5 normal 5 normal A unit - 4 null C D unit normal 4 normal 4 normal A 3 <null> 4 3 bottom0 3 normal 15 norm A 2 bottom 2 <null> 2 normal unit <null> 3 1 <null> 15 1 bottom 25 norm unit - 2 A bottom Unit Interval Type Depth to bottom Interval type A norm unit - 1

19 Database Setup Missing stratigraphy needs to be accounted for in all boreholes Interval types are flagged to determine how they will be used A B C D Unit Interval Type Unit Interval Type Unit Interval Type Unit Interval Type unit <null> normal 5 4 normal normal 5 4 normal normal 5 4 normal normal unit normal 3 <null> 3 bottom 3 normal 2 normal 2 bottom 2 <null> 2 normal unit bottom 1 <null> 1 <null> 1 bottom This elevation point is used to define both This elevation point is unit 2 and unit 3 used to correct or This elevation point is surfaces push down the NOT unit 2 used to correct surface (depth > or unit push 2 down the surface) unit 1surface (depth < unit 1 surface) unit - 2 unit - 1

20 Surface Geology Control Mapped geology polygons were converted into a series of points Polygon points were tagged with the oldest adjacent unit code

21 Surface Geology Control Map polygon points are combined with high quality borehole elevation points to produce control datasets Topographic DEM provides a common elevation standard for all data Control Boreholes added

22 Spatial Data Model - Construction 1 Training surfaces Control datasets are triangulated

23 Spatial Data Model - Construction 1 Training surfaces Control datasets are triangulated

24 Spatial Data Model - Construction 1 Training surfaces 1.2 Identify push-down points Bottom elevation of younger unit is less than elevation of older surfaces

25 Spatial Data Model - Construction 1 Training surfaces 1.3 original control data plus push-down points are triangulated Older surfaces are corrected

26 Spatial Data Model - Construction 1 Training surfaces Unit outcrops are stamped with topographic DEM Surface overlaps are corrected from the top down DEM

27 Spatial Data Model - Construction 1 Training surfaces Unit outcrops are stamped with topographic DEM Surface overlaps are corrected from the top down

28 Spatial Data Model - Construction 1 Training surfaces Unit outcrops are stamped with topographic DEM Surface overlaps are corrected from the top down

29 Spatial Data Model - Construction 2 MOE water well automated stratigraphic coding Training surface elevations and distance to nearest control point are tabulated for each well

30 Spatial Data Model - Construction 2 MOE water well automated stratigraphic coding Coding program 1 st interprets textural intervals and assigns a preliminary stratigraphic code. Map polygons serve as the stratigraphic starting position

31 Spatial Data Model - Construction 2 MOE water well automated stratigraphic coding Coding program 1 st interprets textural intervals and assigns a preliminary stratigraphic code. Based on material attributes, the remainder of the well is coded as either the same or as an older stratigraphic unit

32 Spatial Data Model - Construction 2 MOE water well automated stratigraphic coding Program next checks wells within Maximum range of influence buffer against training surfaces. Max. range of influence (E.g. 1-2 km)

33 Spatial Data Model - Construction 2 MOE water well automated stratigraphic coding If the contact is outside the vertical tolerance range, then a suitable contact within the tolerance range is located Max. tolerance range for contact increases linearly from +/-1 to +/-10 m at max. range of influence

34 Spatial Data Model - Construction 2 MOE water well automated stratigraphic coding If the contact is outside the vertical tolerance range, then a suitable contact within the tolerance range is located Note: no changes are made to contacts based on sand / gravel intervals

35 Spatial Data Model - Construction 2 MOE water well automated stratigraphic coding Global rules based on expert knowledge are verified as a final check. Max. unit thickness Max. interbed thickness Max. elevation Misc. spatial rules

36 Spatial Data Model - Construction 3 Final surfaces Process is repeated with MOE water wells included Checked by contributing geologists to ensure validity

37 Spatial Data Model - Construction 4 Error surfaces Reliability of the model depends on quality of and proximity to data points Kilometers

38 Spatial Data Model - Construction 4 Error surfaces Distance buffers are produced for each data point. Buffers are converted to values ranging from 1 to 0 (high to low confidence) Buffers are reduced to half the range for water wells Kilometers and to a quarter for push-down points

39 Model Limitations 1 Lack of complete stratigraphic penetration in archival records therefore deep, buried units are not well defined in some areas 2 Data gaps and clustering results in a limited ability to fully define detailed features (E.g. drumlins and tunnel channels) 3 Geological mapping and spatial constraints derived through expert knowledge form a basis for the structural model, therefore the accuracy of the conceptual geological model affects the structural model

40 The regional stratigraphic model provides: Regional perspective for small-scale land use studies A means of distilling geological knowledge and data into a distributable form A basis for hydrogeological modeling A means for testing conceptual geological model hypotheses

41 Preliminary surfaces: Examples Lower deposits isopach Thickness (meters)

42 Preliminary surfaces: Examples Newmarket isopach Thickness (meters)

43 Preliminary surfaces: Examples Oak Ridges Moraine isopach Thickness (meters)

44 Preliminary surfaces: Examples Halton Till isopach Thickness (meters)