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LSTM neural networks

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Journal Article
Published: 05 January 2022
Seismological Research Letters (2022) 93 (2A): 814–826.
... memory (LSTM) neural networks, which can produce a highly nonlinear neural network and derive an alert probability at every time step. The proposed LSTM neural network is then tested with two major earthquake events and one moderate earthquake event that occurred recently in Taiwan, yielding the results...
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High‐level architecture of long short‐term memory (<span class="search-highlight">LSTM</span>) <span class="search-highlight">neural</span> <span class="search-highlight">network</span> use...
Published: 20 May 2024
Figure 3. High‐level architecture of long short‐term memory (LSTM) neural network used in the study The color version of this figure is available only in the electronic edition.
Journal Article
Published: 20 May 2024
Seismological Research Letters (2024) 95 (5): 2886–2893.
...Figure 3. High‐level architecture of long short‐term memory (LSTM) neural network used in the study The color version of this figure is available only in the electronic edition. ...
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Journal Article
Journal: Geophysics
Published: 04 January 2023
Geophysics (2023) 88 (1): E13–E28.
... differences between the samples of massive noise and clean data and use the learned features to realize signal-noise identification of the measured data. Second, we use the measured clean data obtained by CNN identification to train the long short-term memory (LSTM) neural network and perform the prediction...
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Structure of the recurrent <span class="search-highlight">neural</span> <span class="search-highlight">networks</span>: (a) basic RNN, (b) <span class="search-highlight">LSTM</span>, and (c...
Published: 19 October 2021
Figure 9. Structure of the recurrent neural networks: (a) basic RNN, (b) LSTM, and (c) biLSTM.
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<span class="search-highlight">Neural</span> <span class="search-highlight">network</span> architectures for comparison: (a) FCDNN architecture, (b) Bi...
Published: 13 October 2023
Figure 3. Neural network architectures for comparison: (a) FCDNN architecture, (b) BiLSTM neural network, and (c) an LSTM cell.
Journal Article
Journal: Lithosphere
Publisher: GSW
Published: 12 January 2024
Lithosphere (2024) 2024 (1): lithosphere_2023_197.
.... LSTM networks include three types of gates: the forget gate, the input gate, and the output gate [ 25 - 28 ]. The forget gate and input gate are applied to the neural cell of the LSTM network. The forget gate controls whether information is retained or forgotten in the cellular state of the previous...
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Journal Article
Journal: Interpretation
Published: 13 April 2022
Interpretation (2022) 10 (2): T367–T378.
... on conventional logging data, but accuracy is limited by the formation types and properties, such as shale sandstone interbeds. Therefore, we propose a hybrid convolutional neural network long short-term memory (CNN-LSTM) deep learning model for the prediction of compressional and shear traveltimes. The new model...
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Comparison of deep learning models performance: training accuracy (A) and v...
Published: 01 November 2022
Figure 9. Comparison of deep learning models performance: training accuracy (A) and validation accuracy (B), which shows convolutional neural networks (CNNs) and CNN–long short-term memory (CNN-LSTM) (yellow and dark blue curves) models yield higher accuracy in a smaller number of epochs
Journal Article
Journal: Geophysics
Published: 13 June 2020
Geophysics (2020) 85 (4): WA213–WA225.
...) ( Adler et al., 2019 ; Pham and Zabihi Naeini, 2019 ) is introduced into the framework of the neural network to make the data representation more accurate ( Adeeba and Hussain, 2019 ). In many variants of RNN, the long short-term memory (LSTM) model ( Tai et al., 2015 ) uses a unique model structure...
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Deep learning models used in this study. Intermediate blocks denote layer t...
Published: 24 April 2023
) with two convolutional layers of varying number of channels. (c) Recurrent neural network (RNN) with varying number of long short‐term memory (LSTM) cells. The color version of this figure is available only in the electronic edition.
Journal Article
Published: 24 May 2024
Journal of Environmental and Engineering Geophysics (2023) 28 (3): 147–152.
... data processing. LSTM is a special kind of recurrent neural network (RNN) with three advantages ( Kim et al ., 2020 ). Firstly, it is much better at handling long-term time series. This is due to their ability to remember information for extended periods of time. Secondly, LSTMs are much less...
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Journal Article
Journal: Geophysics
Published: 23 December 2022
Geophysics (2023) 88 (1): D31–D48.
..., including 11 training and two test wells. We use one well to calculate the uncertainties of four time-series networks, i.e., our proposed network and three benchmark models (recurrent neural network, gated recurrent unit, and LSTM), to demonstrate the stability and robustness of the proposed method...
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Schematic of hybrid convolutional <span class="search-highlight">neural</span> <span class="search-highlight">networks</span> (CNN)–long short-term mem...
Published: 01 November 2022
Figure 4. Schematic of hybrid convolutional neural networks (CNN)–long short-term memory (LSTM) architecture used for reservoir characterization, modified from Guo et al., 2018 . A bivariate input of pressure and pressure derivative ( p D and p′ D ) are shown in this figure, but the CNN-LSTM
Journal Article
Journal: Interpretation
Published: 15 June 2022
Interpretation (2022) 10 (3): T451–T460.
... . Marchi E. Vesperini F. Eyben F. Squartini S. Schuller B. , 2015 , A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks : IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP...
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(a) Popular <span class="search-highlight">neural</span>-<span class="search-highlight">network</span> architectures used in exploration seismology and...
Published: 02 November 2023
Figure 10. (a) Popular neural-network architectures used in exploration seismology and (b) neuron types. Fully connected and CNNs are the dominant types and researchers have used U-Net, LSTM, and autoencoder architectures extensively.
Journal Article
Journal: Interpretation
Published: 17 April 2024
Interpretation (2024) 12 (3): SE15–SE24.
... identification method based on LSTM cyclic neural network : Lithologic Reservoir , 33 , 120 – 128 , doi: http://dx.doi.org/10.12108/yxyqc.20210312 . Xu M. Zhao L. Gao S. Zhu X. Geng J. , 2022 , Joint use of multi-seismic information for lithofacies prediction via supervised...
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Journal Article
Published: 23 November 2022
Seismological Research Letters (2023) 94 (1): 1–16.
... neural network, and long short‐term memory fully convolutional network (LSTM‐FCN), we constructed two‐class and three‐class models, analyzed the change in the classification with epicentral distances, and evaluated the generalizability of different classifiers. The results showed that the accuracies...
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Journal Article
Published: 05 October 2023
Seismological Research Letters (2024) 95 (1): 329–341.
... TGCN TCN LSTM TA 3621.72 858.63 1277.03 CI 6749.40 1575.91 2286.68 The unit of training time is s. CI, California Integrated Seismic Network; LSTM, long short‐term memory; TA, transportable array; TCN, temporal convolutional network; and TGCN, transformer graph neural network...
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Journal Article
Journal: AAPG Bulletin
Published: 01 November 2022
AAPG Bulletin (2022) 106 (11): 2315–2336.
...Figure 9. Comparison of deep learning models performance: training accuracy (A) and validation accuracy (B), which shows convolutional neural networks (CNNs) and CNN–long short-term memory (CNN-LSTM) (yellow and dark blue curves) models yield higher accuracy in a smaller number of epochs...
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