Journal ArticleDOI
Nonlinear multiparameter optimization using genetic algorithms; inversion of plane-wave seismograms
Paul L. Stoffa,Mrinal K. Sen +1 more
TLDR
In this paper, the applicability of genetic algorithms to the inversion of plane-wave seismograms was investigated, where a random walk in model space and a transition probability rule were used to help guide their search.Abstract:
Seismic waveform inversion is one of many geophysical problems which can be identified as a nonlinear multiparameter optimization problem. Methods based on local linearization fail if the starting model is too far from the true model. We have investigated the applicability of “Genetic Algorithms” (GA) to the inversion of plane‐wave seismograms. Like simulated annealing, genetic algorithms use a random walk in model space and a transition probability rule to help guide their search. However, unlike a single simulated annealing run, the genetic algorithms search from a randomly chosen population of models (strings) and work with a binary coding of the model parameter set. Unlike a pure random search, such as in a “Monte Carlo” method, the search used in genetic algorithms is not directionless. Genetic algorithms essentially consist of three operations, selection, crossover, and mutation, which involve random number generation, string copies, and some partial string exchanges. The choice of the initial popul...read more
Citations
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Journal ArticleDOI
Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space
TL;DR: In this article, a derivative-free search method for finding models of acceptable data fit in a multidimensional parameter space is presented, which falls into the same class of method as simulated annealing and genetic algorithms, which are commonly used for global optimization problems.
Journal ArticleDOI
Geophysical inversion with a neighbourhood algorithm—II. Appraising the ensemble
TL;DR: In this paper, a Monte Carlo direct search method is used to estimate the information in the available ensemble to guide a resampling of the parameters of the model space, which can be used to obtain measures of resolution and trade-off in the model parameters.
Book ChapterDOI
Probabilistic Earthquake Location in 3D and Layered Models
TL;DR: A probabilistic earthquake location methodology is described and an efficient Metropolis-Gibbs, non-linear, global sampling algorithm is introduced to obtain complete, probabilistically locations for large numbers of events and for location in 3D models.
Book
Global Optimization Methods in Geophysical Inversion
Mrinal K. Sen,Paul L. Stoffa +1 more
TL;DR: In this paper, the authors present a mathematical model of a GA multimodal fitness function, genetic drift, GA with sharing, and repeat (parallel) GA uncertainty estimates evolutionary programming -a variant of GA.
Journal ArticleDOI
Monte carlo methods in geophysical inverse problems
TL;DR: The development and application of Monte Carlo methods for inverse problems in the Earth sciences and in particular geophysics are traced from the earliest work of the Russian school and the pioneering studies in the west by Press [1968] to modern importance sampling and ensemble inference methods.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Nonlinear one-dimensional seismic waveform inversion using simulated annealing
Mrinal K. Sen,Paul L. Stoffa +1 more
TL;DR: In this paper, the authors used simulated annealing to find the critical temperature at which a solid in a heat bath is heated by increasing the temperature, followed by slow cooling until it reaches the global minimum energy state.
Journal ArticleDOI
Rapid sampling of model space using genetic algorithms: examples from seismic waveform inversion
Mrinal K. Sen,Paul L. Stoffa +1 more
TL;DR: This paper investigates GA to rapidly sample the most significant portion or portions of the PPD, when very little prior information is available, and addresses the problem of ‘genetic drift’ which causes the finite GAs to converge to one peak or the other when the algorithm is applied to a highly multimodal fitness function with several peaks of nearly the same height.