Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism

被引：2
|
作者：
Fu, Xingan [1 ,2 ]
Wei, Youhua [2 ]
Su, Yun [2 ]
Hu, Haixia [2 ]
机构：
[1] Chengdu Univ Technol, Coll Math & Sci, Chengdu 610059, Peoples R China
[2] Chengdu Univ Technol, Geomath Key Lab Sichuan Prov, Chengdu 610059, Peoples R China
来源：
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 06期
关键词：
shear wave prediction; neural network; attention mechanism; long short-term memory network; Attention-LSTM; EMPIRICAL RELATIONS; LOGS; ROCKS;
D O I：
10.3390/app14062489
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
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.
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收藏
页数：16
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