The method combines the advantages of TCN, SA, and BiLSTM in processing temporal data. First, the raw logging data will be pre-processed, and then the initial feature extraction will be performed by TCN. Then, SA is used to extract the internal self-correlation of the temporal features of the previous step.
May 1, 2023 · TCN-SA-BiLSTM is a deep learning model that hybridizes Temporal Convolutional Network (TCN), Self-Attention mechanism (SA), and Bidirectional ...
A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight ...
Furthermore, Yang et al. (2023) employed a deep learning approach using TCN and SA-Bi-LSTM for reservoir logging identi cation, effectively improving the ...
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A method based on the bidirectional temporal convolutional network (BiTCN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM)
Oil Logging Reservoir Recognition Based on TCN and SA-BiLSTM Deep Learning Method. Engineering Applications of Artificial Intelligence. 2023-05 | Journal ...
This paper proposes a logging data reconstruction method based on migration learning, which can reduce the dependence on labeled data.
The aim of this research is to describe and characterize the reservoir sections by well log data. Well logs are used to examine the porosity, ...
This paper proposes a logging reconstruction method based on improved sand cat swarm optimization (ISCSO) and a temporal convolutional network (TCN)
This paper proposes a logging data reconstruction method based on migration learning, which can reduce the dependence on labeled data and also help to im-.