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Interactive Evolutionary Computation in Geology

Interactive Evolutionary Computation in Geology. Chris Wijns, Louis Moresi, Fabio Boschetti, Alison Ord, Brett Davies, Peter Sorjonen-Ward. Outline. IEC intro Geological examples Conclusion. IEC Scheme. Geological Examples. Extension of the earth’s crust Folding of rock layers

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Interactive Evolutionary Computation in Geology

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  1. Interactive Evolutionary Computation in Geology Chris Wijns, Louis Moresi, Fabio Boschetti, Alison Ord, Brett Davies, Peter Sorjonen-Ward

  2. Outline • IEC intro • Geological examples • Conclusion

  3. IEC Scheme

  4. Geological Examples • Extension of the earth’s crust • Folding of rock layers • Subduction of oceanic crust

  5. Extension of the Earth’s Crust • We wish to model faults which arise as a consequence of extension Vein and breccia systems develop above advancing fold and thrust belt (B.Davies, Normandy Mining)

  6. Goal • After much field work, a geologist draws a picture of what may have happened • Can this be realised by a numerical model following the laws of physics?

  7. Problem Starting model of the earth’s crust Combination of material parameters? Behaviour observed (and sketched) by the geologist

  8. Non-linear Physics • Many variables may affect the result: • Crustal strength • Strength dependence with depth • Strength dependence with stress • Maximum crustal weakening with increasing stress • etc.

  9. Complication • No numerical target for the evaluation of a geological cross-section • Two numerically similar outputs may have qualitative differences which are unacceptable to the geologist

  10. Complication • We rely on the expert evaluation of model results • We would like a computationally rigorous and effective way to optimise our search for parameters

  11. Solution • IEC lets us capitalise on the knowledge of an expert user • A genetic algorithm lets us optimise our search in parameter space • The simulation outputs of each generation are ranked by the geologist

  12. IEC Scheme

  13. Simplified Visual Target

  14. Folding of Rock Layers • We wish to model folding with a double wavelength

  15. Problem Starting configuration Physical properties? Double-wavelength folding (i.e. a second wavelength different from the initial perturbation)

  16. Illustration Initial layers with slight perturbation Two wavelengths of folding are present

  17. Folding Variables • Layer viscosities • Layer yield strengths • Layer thicknesses

  18. Results of 5th Generation • Ranking is simply according to presence or absence of double wavelength

  19. Final Parameters • Analysis involves sifting through all generations to collect statistics of the parameters • Conclusion: at least one strong layer is needed (high viscosity and yield stress) and one substantially weaker layer

  20. Subduction of Oceanic Crust • We wish to model oceanic crust subducting under a continent

  21. Subduction Variables • Strength of continental/oceanic crust • Convergence rate • Depth of sedimentary wedge

  22. Problem Parameter or parameter combination? Subducting slab: • remains attached to the continent • detaches at depth animations

  23. Accumulating Models • 5 generations, population 10 • Rank both end-members highly in order to accumulate many models

  24. Visualisation of Parameters All generations Slab behaviour Red=attached Black=detached

  25. Visualisation of Parameters All generations Slab behaviour Red=attached Black=detached

  26. Visualisation of Parameters All generations Slab behaviour Red=attached Black=detached

  27. Final Parameters • Attached slab: • Parameter 4 = convergence rate = varied • Parameter 5 = continent viscosity = low • Detached slab: • Parameter 4 = convergence rate = high • Parameter 5 = continent viscosity = high

  28. Conclusions • Using IEC-based inversion, we have recovered initial parameter combinations which lead to geological behaviour which is observed in the field

  29. Conclusions The IEC technique (1) is time-effective, (2) produces high quality results, (3) is well-suited to geological problems

  30. End

  31. GA Coding • Pop size = 8 • Type of crossover = uniform • Crossover rate = 0.9 • Mutation rate = 0.1 • Coding type = real code • Maximum generations = 6

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