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.
引用
收藏
页数：16
相关论文
共 50 条
  • [1] A Proton Flux Prediction Method Based on an Attention Mechanism and Long Short-Term Memory Network
    Zhang, Zhiqian
    Liu, Lei
    Quan, Lin
    Shen, Guohong
    Zhang, Rui
    Jiang, Yuqi
    Xue, Yuxiong
    Zeng, Xianghua
    AEROSPACE, 2023, 10 (12)
  • [2] Multi-scale network traffic prediction based on attention mechanism and long short-term memory network
    Qian, Tang
    Liu, Yang
    Chao, Ma
    Yifei, Wei
    Journal of China Universities of Posts and Telecommunications, 2024, 31 (06): : 26 - 34
  • [3] Multi-scale network traffic prediction based on attention mechanism and long short-term memory network
    Tang Qian
    Yang Liu
    Ma Chao
    Wei Yifei
    The Journal of China Universities of Posts and Telecommunications, 2024, 31 (06) : 26 - 34+56
  • [4] Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network
    Hu, Wenwen
    Yu, Yongchuan
    Yan, Jianzhuo
    Zhao, Zhe
    Sun, Wenxue
    Shen, Xumeng
    WATER, 2024, 16 (11)
  • [5] Attention-based long short-term memory network temperature prediction model
    Kun, Xiao
    Shan, Tian
    Yi, Tan
    Chao, Chen
    PROCEEDINGS OF 2021 7TH INTERNATIONAL CONFERENCE ON CONDITION MONITORING OF MACHINERY IN NON-STATIONARY OPERATIONS (CMMNO), 2021, : 278 - 281
  • [6] Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism
    Zhou, Hangxia
    Zhang, Yujin
    Yang, Lingfan
    Liu, Qian
    Yan, Ke
    Du, Yang
    IEEE ACCESS, 2019, 7 : 78063 - 78074
  • [7] Urban Water Demand Prediction Based on Attention Mechanism Graph Convolutional Network-Long Short-Term Memory
    Liu, Chunjing
    Liu, Zhen
    Yuan, Jia
    Wang, Dong
    Liu, Xin
    WATER, 2024, 16 (06)
  • [8] Remaining useful life prediction based on double self-attention mechanism and long short-term memory network
    Wu J.
    Su C.
    Zhang Y.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2024, 46 (06): : 1986 - 1994
  • [9] Tool Wear Prediction Based on Attention Long Short-term Memory Network with Small Samples
    Yu, Weiwei
    Huang, Hua
    Guo, Runlan
    Yang, Pengqiang
    SENSORS AND MATERIALS, 2023, 35 (07) : 2321 - 2335
  • [10] Research on Attention Classification Based on Long Short-term Memory Network
    Wang Pai
    Wu Fan
    Wang Mei
    Qin Xue-Bin
    2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020), 2020, : 1148 - 1151
12345