Reconstruction of Fault Surface Models Based on Reproducing Kernel Neural Network
基于再生核神经网络的断层面模型重构

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.