Genetic algorithms in seismic waveform inversion
Genetic algorithms in seismic waveform inversion
复制标题
DOI：
10.1111/j.1365-246x.1992.tb00100.x
复制
发表时间：
1992-05
影响因子：
2.8
通讯作者：
M. Sambridge;G. Drijkoningen
中科院分区：
文献类型：
--
作者：
M. Sambridge;G. Drijkoningen
总结 最近，解决非线性优化问题的一类新方法引起了人工智能领域的极大兴趣。这些方法被称为遗传算法，能够解决高度非线性和非局部优化问题，属于全局优化技术类别，其中包括蒙特卡罗和模拟退火方法。与局部技术（例如阻尼最小二乘法或共轭梯度）不同，遗传算法避免使用目标函数上的所有曲率信息。这意味着它们不需要任何导数信息，因此可以同样好地使用任何类型的失配函数。大多数迭代方法都适用于单个模型，并通过以某种方式扰动模型来找到改进。然而，遗传算法同时处理一组模型，并使用随机过程来指导搜索最佳解决方案。模拟退火和遗传算法都是基于自然优化系统建模的。模拟退火与热力学进行类比；遗传算法与生物进化有类比。这种演变导致所有遇到的模型之间进行有效的信息交换，并允许算法快速吸收和利用获得的信息来找到更好的数据拟合模型。为了说明遗传算法与蒙特卡罗相比的威力，我们考虑一个简单的多维二次优化问题，并表明其相对效率随着未知数的增加而急剧增加。作为它们在真实数据地球物理问题中的应用示例，我们考虑海洋地震折射波形的非线性反演。结果表明，遗传算法本质上优于随机搜索技术，并且比需要良好起始模型的迭代矩阵求逆表现得更好。这主要是因为遗传算法能够将局部和全局搜索机制组合成一个有效的方法。由于许多正向和逆向问题都涉及求解优化问题，因此我们预计遗传方法将在许多其他地球物理问题中得到应用；其中包括地震射线追踪、地震定位、非线性数据拟合以及可能的地震层析成像。
SUMMARY Recently a new class of methods, to solve non-linear optimization problems, has generated considerable interest in the field of Artificial Intelligence. These methods, known as genetic algorithms, are able to solve highly non-linear and non-local optimization problems and belong to the class of global optimization techniques, which includes Monte Carlo and Simulated Annealing methods. Unlike local techniques, such as damped least squares or conjugate gradients, genetic algorithms avoid all use of curvature information on the objective function. This means that they do not require any derivative information and therefore one can use any type of misfit function equally well. Most iterative methods work with a single model and find improvements by perturbing it in some fashion. Genetic algorithms, however, work with a group of models simultaneously and use stochastic processes to guide the search for an optimal solution. Both Simulated Annealing and genetic algorithms are modelled on natural optimization systems. Simulated Annealing uses an analogy with thermodynamics; genetic algorithms have an analogy with biological evolution. This evolution leads to an efficient exchange of information between all models encountered, and allows the algorithm to rapidly assimilate and exploit the information gained to find better data fitting models. To illustrate the power of genetic algorithms compared to Monte Carlo, we consider a simple multidimensional quadratic optimization problem and show that its relative efficiency increases dramatically as the number of unknowns is increased. As an example of their use in a geophysical problem with real data we consider the non-linear inversion of marine seismic refraction waveforms. The results show that genetic algorithms are inherently superior to random search techniques and can also perform better than iterative matrix inversion which requires a good starting model. This is primarily because genetic algorithms are able to combine both local and global search mechanisms into a single efficient method. Since many forward and inverse problems involve solving an optimization problem, we expect that the genetic approach will find applications in many other geophysical problems; these include seismic ray tracing, earthquake location, non-linear data fitting and, possibly seismic tomography.