Fast modelling of Airborne EM data using "Smart Interpretation"
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When using airborne geophysical measurements in e.g. groundwater mapping, an overwhelming amount of data is collected. Increasingly larger survey areas, denser data collection and limited resources, combines to an increasing problem of building geological models that use all the available data in a manner that is consistent with the geologists knowledge about the geology of the survey area. In the ERGO project, funded by The Danish National Advanced Technology Foundation, we address this problem, by developing new, usable tools, enabling the geologist utilize her geological knowledge directly in the interpretation of the AEM data, and thereby handle the large amount of data, In the project we have developed the mathematical basis for capturing geological expertise in a statistical model. Based on this, we have implemented new algorithms that have been operationalized and embedded in user friendly software. In this software, the machine learning algorithm, Smart Interpretation, enables the geologist to use the system as an assistant in the geological modelling process. As the software ‘learns’ the geology from the geologist, the system suggest from new features in the data. In this presentation we demonstrate the application of the results from the ERGO project, including the proposed modelling workflow utilized on a data example from Gotland, Sweden.
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Fast modelling of Airborne EM data using "Smart Interpretation"
- 1. …by I•GIS Presented at the 2015 AGU meeting in San Fransisco Smart Interpretation – application of machine learning in geological interpretation of AEM data Torben Bach 1, Rikke Jakobsen1, Tom Martlev Pallesen1, Mats Lundh Gulbrandsen2, Thomas Mejer Hansen2, Anne- Sophie Høyer3, Flemming Jørgensen3 1. GeoScene3D Team, I-GIS, Risskov, Denmark 2. Niels-Bohr Institute, Computational Geoscience, University of Copenhagen, Denmark 3. Geological Survey of Denmark and Greenland (GEUS), Denmark The ERGO project: Effective High-Resolution Geological Modeling
- 2. …by I•GIS Outline Presentation outline • Motivation behind and Introduction to “Smart Interpretation” • Workflow when modelling with “Smart Interpretation” • Case Example, Gotland, Sweden • Summary and outlook Introduction Workflow Test Case Summing Up
- 3. …by I•GIS Motivation Motivation for Smart Interpretation (SI) • Observations: • Large AEM surveys - enormous amount of data. • One the one hand - manual interpretation is time consuming • On the other hand - geophysical resistivity is not necessarily linked to geological formation or lithology • A Geological expert is needed. • Inspiration: Seismic Auto-picker, used daily as a standard part of modelling of seismic data in O&G • Goal: Develop a practical and usable tool for assisting the Geologist Introduction Workflow Test Case Summing Up Autumn Spring 20 50 ohmm Sand and Clay have overlapping resistivitiesSeasonal variation is reflected in resistivities
- 4. …by I•GIS SI - Theory Steps • Infer a statistical model h(d|M) • Solve the problem: d = f (M). • Perform predictions dpred with uncertainty Mpred dpred f(M) h(dpred|Mpred) +/- 1 std. M d Our Toolbox • Standard Gaussian based inversion theory – with a twist…** Benefits compared to other Machine Learning techniques: • Tools for analysing parametric covariances and interdependencies • A measure of uncertainty on the estimates • Very fast ! **See ”Smart Interpretation - Automatic geological interpretations based on supervised statistical models” by Gulbrandsen, Cordua , Bach and Hansen, currently subitted and in review for ”Computational Geosciences” Introduction Workflow Test Case Summing Up
- 5. …by I•GIS SI - Theory M Geophysical Data (M) Introduction Workflow Test Case Summing Up
- 6. …by I•GIS SI - Theory M d Geophysical Data (M) Geological Knowledge (d) Introduction Workflow Test Case Summing Up
- 7. …by I•GIS SI - Theory M d h(d,M) Geophysical Data (M) Statistical Model h(d,M) Geological Knowledge (d) Introduction Workflow Test Case Summing Up
- 8. …by I•GIS SI - Theory M d h(d,M) Mpred dpred Geophysical Data (M) Statistical Model h(d,M) Geophysical Data Elsewhere Mpred Predicted Geology with uncertainty h(dpred|Mpred) Geological Knowledge (d) Introduction Workflow Test Case Summing Up
- 9. …by I•GIS 1:Add manual interpretation 2:Run SI Locally 3:Apply algorithm globally 4:Evalute and QC result Introduction Workflow Test Case Summing Up Workflow in Production
- 10. …by I•GIS Groundwater mapping on the Island of Gotland Courtesy Peter Dahlquist, SGU Test Case Introduction Workflow Test Case Summing Up
- 11. …by I•GIS Test Setup Introduction Workflow Test Case Summing Up The Geologists • Geologist 1: Using normal manual modelling • Geologist 2: Using SI assisted manual modelling Limestone Marlstone Clay- and marlstone The Geology Sharp boundary Diffuse Zone The Test • Compare ”Manual Model” to ”Model generated using 10% as input to SI” • Compare ”Manual Model” to ”SI assisted Model”
- 12. …by I•GIS Reference Model The manual model
- 13. …by I•GIS Test: Manual Model Introduction Workflow Test Case Summing Up Surface 2Surface 1 Geologist 1 – a standard manual model • Evenly distributed mesh of manual interpretation points • Surfaces dipping trend towards the south-east • Abrupt high in north-west
- 14. …by I•GIS Test: Manual Model Introduction Workflow Test Case Summing Up The Geologist avoids couplings and artifacts in data Difuse Zone Interpreted The Geologist models the ”pinch out” of the ”diffuse” layer Geologist 1 – a standard manual model
- 15. …by I•GIS TEST 1 Throw away 90% of the Geologists input – and run Smart Interpretation
- 16. …by I•GIS Test: SI using 10% of Manual Model Introduction Workflow Test Case Summing Up • General trend in surfaces is reproduced • Higher small scale variation due to the increased amount of interpretation points Surface 2Surface 1 ManualManual MANUAL
- 17. …by I•GIS Test: SI using 10% of Manual Model Introduction Workflow Test Case Summing Up • General trend in surfaces is reproduced • Higher small scale variation due to the increased amount of interpretation points Surface 2Surface 1 Manual10% of manual points, 1688 SI points generated Manual10% of manual points, 1653 SI points generated Smart Interpretation
- 18. …by I•GIS Test: Reduced Model 10% Introduction Workflow Test Case Summing Up Geologist 1 Remove 90% of interpretation points – and run SI 10% Manual + SI 26 man.points, 1653 SI.points Difference Surface 1 264 points 343 points Surface 2 Manual Model +/- 10 m 26 man.points, 1688 SI.points
- 19. …by I•GIS Manual Test: SI using 10% of Manual Model Introduction Workflow Test Case Summing Up Manual MANUAL
- 20. …by I•GIS Manual Test: SI using 10% of Manual Model Introduction Workflow Test Case Summing Up 10% of manual points Manual10% of manual points Couplings only partly managed Difuse Zone Is managed Pinch Out is managed Smart Interpretation
- 21. …by I•GIS TEST 2 A model build using Smart Interpretation
- 22. …by I•GIS Test: SI Assisted Model Introduction Workflow Test Case Summing Up • General trend in surfaces is reproduced • Higher small scale variation due to the increased amount of interpretation points Surface 2Surface 1 Manual Model Manual Model MANUAL
- 23. …by I•GIS Test: SI Assisted Model Introduction Workflow Test Case Summing Up • General trend in surfaces is reproduced • Higher small scale variation due to the increased amount of interpretation points Surface 2Surface 1 Manual Model Manual ModelSI Assisted Model SI Assisted Model Smart Interpretation
- 24. …by I•GIS Manual Manual Test: SI Assisted Model Introduction Workflow Test Case Summing Up MANUAL
- 25. …by I•GIS Manual Manual Test: SI Assisted Model Introduction Workflow Test Case Summing Up SI Assisted Model SI Assisted Model Couplings are managed Difuse Zone Is managed Pinch Out is managed Smart Interpretation
- 26. …by I•GIS Test: SI Assisted Model Introduction Workflow Test Case Summing Up Summary • The theoretical framework derived from Gaussian based inversion techniques • It is very fast • calculation uncertainty • Test case shows ability to map couplings and diffuse geological boundaries • More interpretation points -> more variation in the generated surfaces • Implemented in production software GeoScene3D Looking ahead… • Currently underway • developments toward looking for “structures” in data • other attribute types, e.g. coherency • other datatypes included in SI Come and join us
- 27. …by I•GIS Thank You !
Editor's Notes
- An enormous amount of information are available to the geologist when modeling large AEM surveys. One the one hand - manual interpretation is both time consuming and prone to errors, when incorporate all information. On the other hand - geophysical resistivity is not necessarily directly linked to geological formation or lithology – a Geological expert is needed. We will develop practical tools that assist the Geologist in the modelling procedure, enabling geophysical results are used in a manner consistent with the Geological expert knowledge provided to the system.
- The procedure is itterative and on this slide we can follow the workflow. First the geologist add the manual interpretation Second the algoritm is traind in a local area until the geolgist i satisfied Third, the algorithm is applied on the whole dataset And then the geologist evaluate and makes a quality controll of the results. If the results look perculiar in some way or area, the geologist can go back and make another interpretation or change the area where the alorithm works well. If everything looks fine the geologist can go on with another area or geological layer
- The case study area is situated on an Island a few km outside of the Swedish mainland The geology can be simplified as shown in this illustration, a none existing to thin soil cover on flatlying carbonates and marlstone The main aquifers for drinking water are situated in fractured and karst aquifers.