Earthquake Magnitude Prediction using Spatia-temporal Features Learning Based on Hybrid CNN- BiLSTM Model
Author:
Affiliation:
1. University of Mazandaran,Department of Civil Engineering,Babolsar,Iran
2. Tarbiat Modares University,Department of Electrical and Computer Engineering,Tehran,Iran
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9729328/9729329/09729358.pdf?arnumber=9729358
Reference24 articles.
1. LSTM-based Models for Earthquake Prediction
2. Earthquake warning system: Detecting earthquake precursor signals using deep neural networks;ibrahim;Technical Report CS 230,2018
3. Hybrid Event Detection and Phase‐Picking Algorithm Using Convolutional and Recurrent Neural Networks
4. Recurrent convolutional neural networks help to predict location of earthquakes;kail;IEEE Geoscience and Remote Sensing Letters,2021
5. Prediction of intensity and location of seismic events using deep learning
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1. Earthquake Magnitude and Depth Prediction Based on Hybrid GRU-BiLSTM Model;Algorithms for Intelligent Systems;2024
2. A CNN-BiLSTM model with attention mechanism for earthquake prediction;The Journal of Supercomputing;2023-05-26
3. Most complicated lock pattern-based seismological signal framework for automated earthquake detection;International Journal of Applied Earth Observation and Geoinformation;2023-04
4. Deep Multi-scale Dilated Convolution Neural Network with Attention Mechanism: A Novel Method for Earthquake Magnitude Classification;2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS);2022-12-28
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