Seismic velocity inversion based on CNN-LSTM fusion deep neural network

基于CNN-LSTM融合深度神经网络的地震速度反演

Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.

本文提出一种基于CNN-LSTM融合深度神经网络的地震速度建模方法,能够从共中心点(CMP)道集中同时估计均方根(RMS)速度和层速度。该方法采用卷积神经网络(CNN)编码器(Encoder)和长短期记忆神经网络(LSTMs)从地震信号中分别提取空间和时间维度的特征,而后通过CNN解码器(Decoder)从不同的特征向量中分别恢复地下介质的RMS速度和层速度。针对深层神经网络在训练过程中梯度不稳定、易陷入局部最小值的问题,本文提出利用整流单元负斜率为零的Kaiming正态初始化,通过优化引入冻结因子的均方误差(MSE)损失函数来调节网络的学习过程。测试数据集的实验结果表明CNN-LSTM融合深度神经网络能够较为准确的预测RMS速度和层速度,并优于单一神经网络模型的反演精度。在复杂构造及Marmousi模型上的预测结果符合真实速度变化趋势,在实际数据上的预测结果能够有效校平同相轴,改善同相轴的横向连续性,提高叠加剖面的质量,表明该方法的有效性并具有较好的泛化能力。