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Interactive Evolutionary Computation in Geology

This presentation explores the application of Interactive Evolutionary Computation (IEC) in geological modeling. The authors, including Chris Wijns and Louis Moresi, illustrate how IEC can optimize the understanding of complex geological processes such as the extension of the Earth's crust, folding of rock layers, and subduction of oceanic crust. By leveraging expert knowledge and a genetic algorithm, the study highlights how feedback from geologists improves the accuracy of numerical models. The findings demonstrate IEC's effectiveness in quickly producing high-quality geological results and providing insights into parameter interactions.

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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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