Predicting anisotropic parameters of strata by deep multiple triangular kernel extreme learning machine optimized by flower pollination algorithm

Abstract

Stratigraphy in the crust is widely anisotropic. Anisotropic parameters play an important role from inversion and migration to stratigraphic interpretation and reservoir characterization. At present, under conventional geophysical methods, whether logging or seismic, do not directly measure anisotropic parameters. That is, it is difficult to obtain anisotropic parameters. However, there is a certain correlation between anisotropy parameters and other kinds of logging data, so that anisotropy parameters can be calculated from other logging curves. In view of the complexity of this relationship, a machine learning approach can be used. So, we propose a deep multiple triangular kernel extreme learning machine optimized by the flower pollination algorithm (FPA-D-MK-ELM), which is used to predict the anisotropy parameters of the strata. The accuracy and stability of the FPA-D-MK-ELM algorithm are verified by comparing the algorithm before and after optimization.