基于深度卷积神经网络的地震数据断层识别方法

对地震数据进行断层解释一直是油气勘探开发过程中的一项重点工作.传统的断层解释主要是以人机交互方式进行的,效率低,并且人为因素可能增大断层解释结果的不确定性;而常规的断层识别方法则通常需要设置多个控制参数,导致断层识别的结果严重依赖参数设置的准确性.为此,提出一种基于卷积深度神经网络的地震数据断层识别方法,该方法利用Re...

Seismic fault interpretation based on deep convolutional neural networks

Seismic fault interpretation has always been a key task in the process of oil and gas exploration and development. Conventional fault interpretation is mainly based on human-computer interaction, which is of low efficiency and causes the results with many uncertainties. In addition, conventional methods for fault interpretation usua-lly set multiple parameters, whose controls accuracy of the predicted faults. This paper proposes a method using seismic data based on convolutional deep neural networks. Taking the advantages of ResNet for effectively training deep convolutional neural network and U-Net architecture for characterizing multi-scale and multi-layer characteristic information, this method combines deep residual neural network and U-Net architecture to construct a network architecture (SeisFault-Net) for fault interpretation based on seismic data. The U-Net architecture consists of an encoding sub-network and a decoding sub-network. They enable the SeisFault-Net to train models in an end-to-end manner. The residual neural network can suppress the gradient dispersion of deep network, and effectively improve the training efficiency of the SeisFault-Net. After trained, the SeisFault-Net can perform fault interpretation based on seismic data without setting any parameters. This avoids the empirical error and uncertainties caused by parameters artificially set in conventional methods. Applications to raw data have proved that the SeisFault-Net me-thod can effectively and accurately detect fault loca-tions, and the faults have good vertical continuity and clear outlines. The detailed information of faults interpretated by the SeisFault-Net method is more abundant and accurate than the coherent algorithm. And the calculating efficiency of the SeisFault-Net method is very high in seismic fault interpretation.