Define linear inverse problem. Introduce linear regression and least square solution.
Chapter 4: The characteristics of solutions of linear inverse problem (8 hours)
Introduce generalized inverse, singular value decomposition, and regularization.
Chapter 5: Examples (I) (4 hours)
Deconvolution, seismic tomography.
Mid-term (2 hours)
Chapter 6: Linearization of nonlinear inverse problem (4 hours)
Introduce steepest descent, conjugate gradient, and Newton method.
Chapter 7: Examples (II) (4 hours)
Earthquake location, gravity inversion.
Chapter 8: Maximum likelihood (4 hours)
An overview of the prior knowledge of the probability and statistics. Introduce the viewpoint of probability in solve
inverse problem.
Chapter 9: Complete inverse problem (4 hours)
Introduce the fundamental concepts of the grid-search, Monte Carlo, simulate annealing, genetic algorithm.
Chapter 10: Artificial Neural Network (4 hours)
Introduce the basis of artificial neural network and machine learning.
1. Geophysical Data Analysis: Discrete Inverse Theory, Third or Fourth Edition. William Menke, Elsevier Inc.
2. 地球物理反演基本理论与应用方法,姚姚,中国地质大学出版社