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

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 rectified 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 field data can effectively correct the phase axis, improve the lateral continuity of phase axis and quality of stack section, indicating the effectiveness and decent generalization capability of the proposed method.