In today's world, developing the best practice measures for geoscientists is a complicated task. Basics of Computational Geophysics aims to provide a one-stop, collective resource for practitioners, covering different techniques and models in geoscience, their practical applications, and case studies. The reference provides the modelling theory in an easy-to-read format, verified with on-site models for specific regions and scenarios. Basics of Computational Geophysics offers a platform whereby readers will learn both the theory and the practical applications, focusing on a comparison of real-world problems of geomechanics, modelling, and optimizations. Covers various advanced computational techniques for solving different problems in geophysicsIncludes case studies for examples of practical applicationsProvides an assessment of the capabilities of commercial software INDICE: 1. Earthquake 2. Landslide 3. Debris flow 4. Volcano 5. Monte Carlo Simulation 6. Stochastic Optimization Method 7. Reliability Analysis 8. Stochastic Finite Element Method 9. Kalman Filter 10. Random matrix 11. Markov Chain 12. Gaussian Process Regression 13. Logistic regression 14. Geostatistics 15. Kriging 16. Bayesian inference 17. Bayesian updating 18. Probabilistic Neural Network 19. SVM, Relevance vector machine 20. Minimax probability machine regression copula-statistical models 21. Optimization 22. Finite Element Method 23. Boundary Element Method 24. Glaciers 25. Soft Computing 26. Artificial intelligence
- ISBN: 978-0-12-820513-6
- Editorial: Elsevier
- Encuadernacion: Rústica
- Páginas: 375
- Fecha Publicación: 27/02/2021
- Nº Volúmenes: 1
- Idioma: Inglés