Uncertainty in Geological Mapping - Lachlan Grose (Monash Uni.)
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This presentation was delivered at the June 10 (2014) 3D Interest Group Meeting at the Centre for Exploration Targeting, UWA.
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Uncertainty in Geological Mapping - Lachlan Grose (Monash Uni.)
- 1. School of Geosciences Investigating uncertainty in geological maps using geological variability and geodiversity Lachlan Grose, Laurent Ailleres, Gautier Laurent and Peter Betts
- 2. What are geological maps? how do we create them? • A topological representation of geological interactions eg. Lithological boundaries, structures. • Essentially a 2D model • Human interpolation between outcrops guided by knowledge and experience • Interpolation is similar to how implicit 3D models are built • Studies looking into uncertainty in 3D models have used implicit scheme, we used 40 students 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 2
- 3. This study Geological variability as a proxy for geological uncertainty 40 geological maps of the Eldee Structure, Broken Hill Locate and quantifying geological variability between maps Identify how maps vary geologically and geometrically due to variability Classifies geological maps into “species” using the concept of biodiversity 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 3
- 4. Three types of uncertainty (Mann, 1993) 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 4 a.Error, bias and imprecision b.Inherent randomness c.Imprecise knowledge
- 5. A sample map of the Eldee structure 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 5 Metasediments – Clast-Bearing-Biotite-Gneiss (CBBG) – Interbedded Pelite and psamopelitic schist (IPP) Large intrusive – Pegmatite Smaller intrusive – Felsic gneiss – Amphibolite
- 6. Visualising variability Stratigraphic variability (Lindsay et al. 2012) – Maximal when most common lithology is least well known – Assesses the quality of the average map Information entropy (Shannon, 1958; Wellmann et al., 2012) – Determines amount of information missing from a system – Maximal when all lithologies are equally likely to occur 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 6
- 7. Stratigraphic variability (P) All maps2011 2012+ = Stratigraphic variability 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 7
- 8. All maps2011 2012+ = Confidence map 70% cutoff 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 8 CBBG IPP PEG FELS AMPH
- 9. Entropy (H) All maps2011 2012+ = Information entropy Information entropy (H) 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 9
- 10. Probability Felsic gneiss 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 10 All maps2011 2012+ =
- 11. Probability Amphibolite 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 11 All maps2011 2012+ =
- 12. Lawrence, 2008; Lindsay et al., 2013 Comparing maps using geodiversity • Biodiversity allows for; • Diversity of species described by a number of metrics • Trends in dataset to be identified • Geodiversity • Built on the concept of biodiversity • Geometrical and geological metrics can be used to analyse the diversity of a set of geological maps/models • Trends between maps can be found highlighting different “species” of maps/models 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 12
- 13. Analysing geodiversity • Principal Component Analysis (PCA) • Used by Lindsay et al. 2013 • Identifies linear trends in the dataset • A common dimension reduction technique • However many of the geodiversity metrics don’t follow standard distributions - which can introduce error! • May bias towards modal map (modal geodiversity values) • Self Organising Maps (SOMs) (Kohonen, 1982) • Fits a deformable mesh to the dataset capturing more details about the distribution of the original dataset • Also provides dimension reduction • Can be used to categorise groups of similar maps 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 13
- 14. Sml Med Lge Legs Feath ers Hair Hoove s Mane Run Swim Hunt Fly dove 1 0 0 2 1 0 0 0 0 0 0 1 hen 1 0 0 2 1 0 0 0 0 0 0 0 duck 1 0 0 2 1 0 0 0 0 1 0 0 goose 1 0 0 2 1 0 0 0 0 1 0 1 owl 1 0 0 2 1 0 0 0 0 0 1 1 hawk 1 0 0 2 1 0 0 0 0 0 1 1 eagle 0 1 0 2 1 0 0 0 0 0 1 1 fox 0 1 0 4 0 1 0 0 0 0 1 0 dog 0 1 0 4 0 1 0 0 1 0 1 0 wolf 0 1 0 4 0 1 0 1 1 0 1 0 cat 1 0 0 4 0 1 0 0 0 0 1 0 tiger 0 0 1 4 0 1 0 0 1 0 1 0 lion 0 0 1 4 0 1 0 1 1 0 1 0 horse 0 0 1 4 0 1 1 1 1 0 0 0 zebra 0 0 1 4 0 1 1 1 1 0 0 0 cow 0 0 1 4 0 1 1 0 0 0 0 0 A quick example of SOMs 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 14
- 15. SOMs component maps 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 15
- 16. Animal groups 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 16
- 17. Geodiversity metrics 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 17 Aspect ratio Geological Complexity Contact relationships Surface area and number of regions Orientation
- 18. SOMs geodiversity results 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 18 Number of regions PEGMATITE Number of regions FELSIC GNEISS Contact between INTERBEDDED AND FELSIC GNEISS Maximum surface area PEGMATITE Number of regions INTERBEDDED
- 19. Map “species” 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 19 Species 1 Species 2 Species 3
- 20. Probability The modal map 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 20 All maps2011 2012+ =
- 21. Conclusion 6th Febuary 2014Geological uncertainty in geological maps using variability and geodiversity 21 Variability observed between students interpretation of the geometry of lithological domains The felsic gneiss and amphibolite were rarely mapped consistently in the one location ◦ We identified three distinct clusters highlight different mapping styles; lumpers, blobbers and the outliers! ◦ Self Organising Maps should be considered for further geodiversity analysis
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- A quick overview of what a geological map is and the basic process for creating them. Maps are a topological representaion of geological interactions; lithology boundaries/domains, structural elements etc We should consider them as a 2D model, because that is what they essentially are Are usually interpolation between outcrops guided by knowledge and expertise. The geologist interpolates in a similar way to how 3D interpolators work
- A is related to measurements e.g. structural measuremtns, locatingg contacts, ect B is associated with not knowing what is between observations C is uncertainty as a result of not seeing datapoints
- Add in a real map
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- Even at the high points, it is only recorded in the same location in a quarter of the maps. Obviously in the north east the felsic gneiss is recorded a lot more frequently. This is most likely a result of this area having more exposure and generally being visited during the beginning of the field trip with demonstrator help. As a result this area should provide a good indication of where students are recording and interpreting similar observations
- High regions are visible on the aerial images other areas are not clearly visible but the amphibolite is quite distinctive in the field. Which suggests that students probably miss the outcrops and/or don’t understand the structures meaning they are not predicting where the lithology should be
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- Now for looking at maps – adapting the geodiversity metrics used by Lindsay et al. 2013 to 2D models.
- Umatrix is the distance between the nodes. As I said earlier, SOMs fits a deformable mesh to the samples so the “distance” between them is not necessarily even as such a measure of how close each BMU is can describe the overall variability in the dataset. Black dots represent where maps are and are scaled with size depending on how many maps are represented by the BMU. Note the higher internal distance between maps within the 1st cluster and separating this cluster from the other maps!
- High variability observed between how students map the geometry of lithological domains Suggests the contact may be hard to identify Different mapping styles Is the lithology mapped in different locations? Hard to identify the lithology Rock unit size smaller than error in locating the unit on the map? The variabilities observed in the variability maps are evident when looking the geodiversity species. The advantage of using geodiversity results over variability maps is it is possible to maintain an understanding of how each map represents the topological interactions between lithologies, not only looking at which locations show the most variabilities.