2022
DOI: 10.1016/j.petrol.2021.109549
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Semi-supervised learning seismic inversion based on Spatio-temporal sequence residual modeling neural network

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Cited by 16 publications
(2 citation statements)
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“…In this regard, the Recurrent Neural Networks (RNNs) [23] and its variants Long Short-Term Memory (LSTM) [24], [25] and Gated Recurrent Unit (GRU) [26] were successively utilized to model the longterm dependence in seismic sequences. GRU and bidirectional GRU were respectively combined with convolutional layers to extract information of seismic traces, enhancing the effectiveness of inversion networks [27], [28]. Nevertheless, the inherent gradient disappearance and inability to compute in parallel limit their capabilities for efficient applications.…”
Section: Introductionmentioning
confidence: 99%
“…In this regard, the Recurrent Neural Networks (RNNs) [23] and its variants Long Short-Term Memory (LSTM) [24], [25] and Gated Recurrent Unit (GRU) [26] were successively utilized to model the longterm dependence in seismic sequences. GRU and bidirectional GRU were respectively combined with convolutional layers to extract information of seismic traces, enhancing the effectiveness of inversion networks [27], [28]. Nevertheless, the inherent gradient disappearance and inability to compute in parallel limit their capabilities for efficient applications.…”
Section: Introductionmentioning
confidence: 99%
“…Kim and Byun, 2020;Feng et al, 2021;Yang et al, 2022;Zhao et al, 2022;Zhou et al, 2023). 또 한, 비지도학습, 준지도학습, 전이학습과 같은 접근법으로 자료 부족 문제를 해결하기 위한 연구들도 수행되고 있고 (e.g., Alfarraj and AlRegib, 2019;Di et al, 2020;Liu et al, 2021a;Song et al, 2022) 료 간의 상호작용을 직접적으로 모델링하는 데 제한이 있 어 성능 저하의 가능성이 있다. 최근 물리탐사 분야 딥러닝 기반 복합역산 관련 연구의 대부분이 규격화된 자료를 통합하여 사용하는 초기 통합 방식을 따른다(e.g., Wei et al., 2022a;Jiao et al, 2023)…”
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