基于改进AlexNet模型的断层识别方法

Fault recognition method based on improved AlexNet

从地震数据中识别断层在地震资料解释中至关重要,但随着勘探规模的扩大,传统的人工解释断层已满足不了实际生产需要。如何研究出一种能满足断层识别高精度需求并能提升运算速度的方法是急需解决的问题。为此,基于改进的AlexNet模型,把自动识别断层的方法看作图像识别二分类问题。首先将批量归一化代替局部响应归一化,加快模型收敛;其次引入平衡交叉熵损失,解决在地震数据中断层与非断层高度不平衡问题,使模型朝着正确的方向收敛;最后用卷积层代替全连接层,极大缩减了训练参数,加快了训练速度。训练的模型对理论数据和实际数据预测结果表明,改进的AlexNet模型充分学习了断层特征,具有可以从地震数据中识别断层的能力。

Fault recognition from seismic data is crucial to the seismic data interpretation,but with the expansion of exploration scale,the traditional artificial fault interpretation cannot meet the actual production needs.How to develop a high-precision fault recognition method and improve the operation speed of the method is an urgent problem for those skilled in the art.Therefore,an automatic fault recognition method based on the improved AlexNet model is proposed to treat fault recognition as binary classification of image recognition.First,instead of local response normalization(LRN),batch normalization is used to accelerate the model convergence.Then,the balanced cross entropy loss is introduced to solve the problem of unbalanced height between the fault and the non-fault in seismic data,which makes the model converge in the right direction.Finally,the convolution layer is adopted to replace the full connection layer,which greatly reduces the training parameters and speeds up the training.The prediction results of the theoretical data and actual data of the training model show that the improved AlexNet model fully learns the fault features and has the ability to identify faults from seismic data.