... 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...

...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. ...

... 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...

.... 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...

... 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...

...) ( 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...

... 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...

..., 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...

... . 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...

... 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...

... 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...

... 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...

...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...