基于U-Net深度学习网络的地震数据断层检测

断层解释是地震资料解释的关键环节之一.随着人工智能技术的发展,断层的自动、快速识别成为机器学习方法在地球物理领域应用的一个研究热点.目前,断层智能识别还存在着模型训练难度大以及实际资料预测效果不理想等问题.为此,提出一种基于U-Net深度学习网络的地震数据断层检测方法,即在网络结构中结合U-Net和残差模块Res-50...

Seismic data fault detection based on U-Net deep learning network

Fault interpretation is one of the key links in seismic data interpretation. With the development of artificial intelligence technology, automatic and rapid fault recognition has become a research hot-spot in the application of machine learning methods in geophysics. At present, intelligent fault recognition is faced with problems, such as difficult model training and the unsatisfactory prediction results of actual data. Therefore, a fault detection method of seismic data based on a U-Net deep learning network is proposed, which combines U-Net and residual module Res-50 in the network structure to construct a new network:ResU-Net. ResU-Net uses the 1×1×1 convolution kernel to process the channel number of feature images. It not only reduces the time complexity but expands the depth of the network based on the original U-Net, effectively improving the operation efficiency and learning ability of the network and identifying faults in a quick and accurate manner. Training and testing of synthetic data sets prove that ResU-Net has less time complexity and solves the problems of fault detection in the case of an irregular data volume by appropriate network input, data expansion, and weighted overlapped boundaries. The application results of actual data show that the ResU-Net training model has strong anti-noise capability, remarkable generalization ability, as well as high prediction accuracy and good continuity of faults.