Seismic resolution enhancement with variational modal-based fast-matching pursuit decomposition

Wang, Chaohe; Zong, Zhaoyun; Yin, Xingyao; Li, Kun

doi:10.1190/int-2023-0002.1

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…The alternating direction multiplier method (ADMM) is introduced to solve Equation (3) and thus obtain the optimal solution [35]. Then, the Parseval-Plancherel Fourier equivalence method is used under the L 2 -norm and Hilbert's transformation [36][37][38], as shown below.…”

Section: Decomposition and Reconstruction Of Load Datamentioning

confidence: 99%

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

He,

Zhao,

Zhang

et al. 2024

The Journal of Engineering

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Short‐term load forecasting is critical for power system planning and operations, and ensemble forecasting methods for electricity loads have been shown to be effective in obtaining accurate forecasts. However, the weights in ensemble prediction models are usually preset based on the overall performance after training, which prevents the model from adapting in the face of different scenarios, limiting the improvement of prediction performance. In order to improve the accurateness and validity of the ensemble prediction method further, this paper proposes an ensemble deep reinforcement learning approach using Q‐learning dynamic weight assignment to consider local behaviours caused by changes in the external environment. Firstly, the variational mode decomposition is used to reduce the non‐stationarity of the original data by decomposing the load sequence. Then, the recurrent neural network (RNN), long short‐term memory (LSTM), and gated recurrent unit (GRU) are selected as the basic power load predictors. Finally, the optimal weights are ensembled for the three sub‐predictors by the optimal weights generated using the Q‐learning algorithm, and the final results are obtained by combining their respective predictions. The results show that the forecasting capability of the proposed method outperforms all sub‐models and several baseline ensemble forecasting methods.

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Section: Decomposition and Reconstruction Of Load Datamentioning

confidence: 99%

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

He,

Zhao,

Zhang

et al. 2024

The Journal of Engineering

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An improved Holo-Hilbert spectral analysis guided by fast matching pursuit

Wang,

Zong

2024

Fourth International Meeting for Applied Geoscience &Amp; Energy

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Frequency-broadening method of seismic data based on sparse reconstruction inversion strategy

Liu,

Song,

Yan

et al. 2024

Front. Earth Sci.

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To broaden the effective frequency bandwidth of seismic data and enhance its resolution, we investigate the high-resolution reconstruction method grounded in compressed sensing sparse theory, utilizing the characteristics of sparse decomposition of seismic signals. First, we examine the construction of an over-complete dictionary, which is then employed to sparsely represent the seismic data and derive the reflection coefficient, combined with noise, forms a mixed sparse component. By removing the noise sparse component from this mixture, we isolate a clean reflection coefficient. In the iterative reconstruction process of compressed sensing sparse decomposition, weak signal can easily be overwhelmed by the application of the maximum energy principle. To address this issue, we propose a useful signal control retention method incorporating lateral adjacent low-rank constraints. This approach increases the probability of optimizing weak signal dictionary atoms, mitigates the unbalanced reconstruction of strong and weak signals, and facilitates the comprehensive reconstruction of both signal types. Ensuring reconstruction accuracy is crucial, as the conditions for reconstruction significantly affect reliability. Therefore, we employ a signal-to-noise ratio estimation method to establish an adaptive iteration stop condition based on a residual threshold. During each iteration, the signal-to-noise ratio is recalculated, and the signal-to-noise ratio is multiplied by the residual to produce a weight residual. Finally, this new residual is used in the inner product calculations, allowing for the preferential selection of dictionary atoms. Both theoretical models and actual data validate the rationality and effectiveness of the proposed method. Analysis of real data demonstrates that our approach significantly enhances the seismic frequency band width and markedly improves the resolution of seismic data.

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Seismic resolution enhancement with variational modal-based fast-matching pursuit decomposition

Cited by 3 publications

References 28 publications

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection

An improved Holo-Hilbert spectral analysis guided by fast matching pursuit

Frequency-broadening method of seismic data based on sparse reconstruction inversion strategy

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