基于模型参考控制理论的随机地震反演方法研究

项目名称： 基于模型参考控制理论的随机地震反演方法研究

项目编号： No.41304087

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 天文学、地球科学

项目作者： 赵鹏飞

作者单位： 吉林大学

项目金额： 25万元

中文摘要： 针对随机地震反演具有运算量巨大和阻抗反演结果容易脱离实际地震剖面的横向展布特征等缺点，迫切需要更为有效的随机地震反演理论。模型参考控制是工程学中的重要方法，主要用于动态跟踪控制领域，并通过神经网络实现。项目的主要理念是"纵向控制，横向优化"。项目的研究内容是：依据动态跟踪控制的思想，提出一种基于模型参考控制的随机地震反演方法，通过等概率模型的优选来降低运算量，并基于实际地震剖面横向展布特征的约束来实现反演结果的优化。项目的目的在于建立一套基于模型参考控制理论的降低运算量和加强结果横向展布特征的随机地震反演方法，弥补以往解决此类问题的不足，进一步发展和完善随机地震反演的理论和应用。

中文关键词： 随机地震反演；模型参考控制；神经网络；等概率模型；横向展布

英文摘要： The drawbacks of stochastic seismic inversion are that the computational complexity is very high and the impedance results generated by inversion may break away from the lateral distribution of the actual seismic profiles. Hence, we need a more effective theory of stochastic seismic inversion. Model reference control is an important method in engineering mainly for the field of dynamic tracking control by neural network. The idea is that vertical control and lateral optimization. The details are as follows: firstly, we give a stochastic seismic inversion based on model reference control with the idea of dynamic tracking control theory; secondly, we reduce the computational complexity by the optimization of equi-probablility models, and optimize the inversion results with the constraints of the lateral distributed characteristics of the actual seismic profiles. The aims are to establish a new stochastic seismic inversion theory to reduce the computational complexity enhance the contraints of the lateral distributed characteristics based on model reference control, overcome the drawbacks of the classic stochastic seismic inversion, and develop the stochastic seismic inversion in theory and application.

英文关键词： stochastic seismic inversion；model reference control；neural network；equi-probability model；lateral distribution