基于残差网络与迁移学习的断层自动识别

机器学习算法在地球物理领域的应用越来越广泛、深入。在地震资料解释中，目前主要利用实际或人工合成的断层样本，训练浅层卷积神经网络识别断层。实际断层样本需要人工标记，消耗大量时间成本；人工合成的断层样本虽然容易获得，但训练出的网络在应用于实际地震数据时效果不佳。为此，将深度残差网络与迁移学习结合并应用于断层识别。首先构建性能更优秀的深度残差网络训练人工合成的断层样本，然后使用少量实际断层样本进行迁移学习，增强网络的泛化能力，优化网络的识别结果。迁移学习后的网络能够有效提高实际断层的识别准确率，实际地震数据验证了该方法的可行性和有效性。

Automatic fault recognition with residual network and transfer learning

The application of machine learning algorithms in the field of geophysics has been expanded and deepened.In fault recognition on seismic data,the main approach is training a shallow convolutional neural network to achieve fault recognition using actual or synthetical fault samples.Actual fault samples require manual marking,which is very time-consuming.Synthetic fault samples are easy to obtain,but the effect of the trained network model is inadequate when applied to actual seismic data.For this reason,this paper combines a deep residual network with transfer learning to fault recognition.First train synthetical fault samples by constructing a deep residual network with better performance,then use a small number of actual fault samples for transfer learning.This way the generalization ability of the network can be enhanced, and the recognition results can be optimized.After transfer learning,the network can more effectively improve the recognition accuracy of actual faults than ever before.Actual seismic data have proved the feasibility and effectiveness of the method.