基于再生核神经网络的断层面模型重构

Reconstruction of Fault Surface Models Based on Reproducing Kernel Neural Network

为了提高勘探精度及采油效率,需要更深入地了解地质构造,因此断层面模型的重构有着重要的意义,为此提出了基于再生核神经网络的断层面重构方法。再生核源于不同学科分支,目前已成为函数逼近的重要工具。将再生核与神经网络有机地结合起来,提出一种新型的神经网络———再生核神经网络,且将网络的训练归结为求解线性方程组问题,为了建立既具有足够精度又能表现系统行为的简单模型,考虑线性方程组的稀疏解是必要的,稀疏解就是具有大量零元素的近似解。虽然稀疏解整体误差较小,但可能在一些点上的误差较大,为此提出对稀疏解的误差修正方法。将再生核神经网络应用于大庆地区的断层面模型重构,实验结果表明,本文重构的断层面与传统方法重构的断层面相比,更符合大庆地区的地质情况。

In order to improve the accuracy of prospecting and efficiency of oil extraction, it is necessary to understand the geological construction deeply. Therefore, the reconstruction of fault surface models is highly important. A method for fault surface models reconstruction is proposed in this paper. Reproducing kernel developed in different disciplines has become an important tool in functional approximation. By combining the reproducing kernel and neural network, a new kind of neural networks, i. e. the reproducing kernel neural network (RKNN) has been initiated. Besides, learning of the network is converted into seeking the solution of the linear equations system. It is essential to consider the sparse solution so as to construct a simple model with sufficient accuracy and represent the system behavior. The sparse solution is an approximating solution with a large part of components as zero. Although the over all error is small, errors of some points may be very big. The error correction of the sparse solution is also discussed. The reconstruction of fault surface models based on the reproducing kernel neural network is implemented in Daqing, and the experimental results show that the reconstructed fault surfaces based on the method presented in this paper is more suitable for the geological situation in Daqing compared with traditional method.