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Regional staining reverse time migration algorithm based on energy-constrained factors

Liu Chang, Qu Yingming, Li Zhenchun, Zeng Shenghan, Yang Tingyu, Zhao Weijie

Acta Geophysica

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2024

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Vol. 72, no. 3

1807--1817

EN

Stained migration algorithms have the potential to enhance imaging results in specific geological structures, such as subsalt layers and steeply inclined layers, by incorporating the complex domain. However, conventional stained migration algorithms often require correct staining of the target geological body, which is challenging to achieve in practical applications, leading to inaccurate imaging results. To address this issue, we propose a novel regional staining algorithm that takes into account energy-constrained factors. First, we derive the discrete form of the complex domain wave equation and the imaging formula for the imaginary migration result. We then investigate the impact of stained region boundaries on seismic wave propagation when different migration velocities are used as input. By analyzing the energy difference between the reflected waves generated at the stained region boundary and those generated by the target geological body, we introduce positive and negative energy-constrained factors to control the interference waves caused by stained boundaries. The positive energy-constrained factor effectively filters out weak interference waves, while the negative energy-constrained factor eliminates strong interference waves. Numerical examples using a simple three-layer horizontal model and a complex salt model demonstrate that our new regional staining algorithm, based on energy-constrained factors, enhances the illumination of the target geological body and improves the imaging resolution of the target region, even when the stained areas are incorrect.

2

Wavelet-Hilbert transform based bidirectional least squares grey transform and modified binary grey wolf optimization for the identification of epileptic EEGs

Liu Chang, Chen Wanzhong, Zhang Tao

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 2

442--462

EN

Wavelet based seizure detection is an importance topic for epilepsy diagnosis via electroencephalogram (EEG), but its performance is closely related to the choice of wavelet bases. To overcome this issue, a fusion method of wavelet packet transformation (WPT), Hilbert transform based bidirectional least squares grey transform (HTBiLSGT), modified binary grey wolf optimization (MBGWO) and fuzzy K-Nearest Neighbor (FKNN) was proposed. The HTBiLSGTwas first proposed to model the envelope change of a signal, then WPT based HTBiLSGT was developed for EEG feature extraction by performing HTBiLSGT for each subband of each wavelet level. To select discriminative features, MBGWO was further put forward and employed to conduct feature selection, and the selected features were finally fed into FKNN for classification. The Bonn and CHB-MIT EEG datasets were used to verify the effectiveness of the proposed technique. Experimental results indicate the proposed WPT based HTBiLSGT, MBGWO and FKNN can respectively lead to the highest accuracies of 100% and 98.60 ± 1.35% for the ternary and quinary classification cases of Bonn dataset, it also results in the overall accuracy of 99.48 ± 0.61 for the CHB-MIT dataset, and the proposal is proven to be insensitive to the choice of wavelet bases.

3

Attention mechanism-based deep denoiser for desert seismic random noise suppression

Lin Hongbo, Liu Chang, Wang Shigang, Ye Wenhai

Acta Geophysica

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2023

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Vol. 71, no. 6

2781--2793

EN

Seismic data collected from desert areas contain a large amount of low-frequency random noise with similar waveforms to the effective signals. The complex noise characteristics make it difficult to effectively identify and recover seismic signals, which will adversely affect subsequent seismic data processing and imaging. In order to recover the complex seismic events from low-frequency random noise, we propose an attention mechanism guided deep convolutional autoencoder network (ADCAE) to assign different importance to different features at different spatial position. In ADCAE, an attention module (AM) is connected to the deep convolutional autoencoder network (DCAE) with soft-thresholded symmetric skip connection that helps to enhance the ability of feature extraction. By combining the global features of the input data and the output local features of DCAE, AM generates an attention weight matrix, which assigns different weights to the features associated with the seismic events and random noise during the training process. In this way, AM can guide the update of the target gradient, thus retains the complex structure of the seismic events in the denoised results and improves the training efficiency of the model. The ADCAE is applied to the synthetic data and field seismic data, and denoised results show that ADCAE has achieved satisfactory denoising performance in signals recovery and low-frequency random noise suppression at the low signal-to-noise ratio.