# Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism
###### Abstract  
Shear wave velocity (VS) is a vital prerequisite for rock geophysics. However, due to historical, cost, and technical reasons, the shear wave velocity of some wells is missing. To reduce the deviation of the description of underground oil and gas distribution, it is urgent to develop a high-precision neural network prediction method. In this paper, an attention module is designed to automatically calculate the weight of each part of the input value. Then, the weighted data are fed into the long short-term memory network to predict shear wave velocities. Numerical simulations demonstrate the efficacy of the proposed method, which achieves a significantly lower MAE of 38.89 compared to the LSTM network's 45.35 in Well B. In addition, the relationship between network input length and prediction accuracy is further analyzed.  
shear wave prediction; neural network; attention mechanism; long short-term memory network; Attention-LSTM +
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diction has become a general trend [23; 24; 25; 26; 27; 28]. The machine learning method has become one of the research hotspots in the oil and gas exploration industry. Compared with traditional machine learning methods (such as SVR [29; 30; 31]), deep learning methods can improve the accuracy of prediction or classification by learning multidimensional features of data in the form of layer stacking [32; 33]. In recent years, artificial neural networks (ANNs) have been widely used in shear wave prediction, seismic wave classification, and seismic phase recognition [34; 35; 36; 37]. Compared with the ANN, which only considers the petrophysical characteristics at the same depth, LSTM synthesizes the contextual information of logging data to predict shear wave velocity, achieving more accurate prediction results [38; 39]. In addition, many scholars have integrated the powerful feature extraction capability of convolutional neural networks (CNNs) into the LSTM network and achieved good results on shear wave prediction [40; 41]. Considering the sequence characteristics of log data, the LSTM network can increase the ability of the feedforward neural network to characterize log data under different conditions by introducing recurrent structures.  
The possibility of obtaining sequence representation is improved in the LSTM network. However, with the increase in the feature dimension, information redundancy may cause the bias of predictions. To effectively learn and extract key features to improve the performance and generalization ability of the network, this paper proposes a long short-term memory network combined with an attention mechanism. By multiplying the attention matrix with the input characteristic curve, the rearranged data compensate for the information diffusion. Numerical simulation results show that the Attention-LSTM network can improve the fitting accuracy of the large velocity variation difference when predicting shear wave velocity. At the same time, this method also shows a higher prediction accuracy than the above methods. To apply the method better in the actual process, the relationship between the input length and the prediction accuracy is further discussed in this paper.