Neural Computing in Geophysics

本研究旨在开发和实现基于 神经网络在地球物理反演中的应用 问题，特别是地震反演。 这样做的动机 方法是改进弹性参数沿着a 地震测线 沿沿着a 地震测线目前在计算上是昂贵的。 但对于 沿着沿着选定的点的数量，它可以用来导出 在训练神经网络时的弹性特性，即， 导出连接权重。 训练后的地震道集 从中间表面点可以输入到网络 来估计这些点的深度依赖弹性特性 从而得到完整的地图。 中最困难的部分 问题是训练网络。 全局优化方法，如 由于模拟退火和遗传算法将用于 网络培训，因为这些方法已证明是成功的， 地球物理反演

This research is to develop and implement inversion methods based on neural networks for application to geophysical inversion problems, particularly, seismic inversion. The motivation for this approach is to improve the mapping of elastic parameters along a seismic line. This type of inversion for each point along a seismic line is currently computationally expensive. However, for a selected number of points along a line, it can be used to derive the elastic properties while training a neural network, that is, deriving the connection weights. After training, seismic gathers from the intermediate surface points can be input to the network to estimate depth-dependent elastic properties for those points resulting in a complete map. The most difficult part of the problem is to train the network. Global optimization methods such as simulated annealing and genetic algorithms will be used for the network training since the methods have proven successful in geophysical inversion.