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Adaptive time-reassigned synchrosqueezing transform for seismic random noise suppression

Liu Wei, Li Shuangxi, Chen Wei

Acta Geophysica

2024

Vol. 72, no. 2

829--847

Noise suppression is of great importance to seismic data analysis, processing and interpretation. Random noise always overlaps seismic reflections throughout the time and frequency, thus, its removal from seismic records is a challenging issue. We propose an adaptive time-reassigned synchrosqueezing transform (ATSST) by introducing a time-varying window function to improve the time-frequency concentration, and integrate an improved Optshrink algorithm for the suppression of seismic random noise. First of all, a noisy seismic signal is transformed into a sparse time-frequency matrix via the ATSST. Then, the obtained time-frequency matrix is decomposed into a low-rank component and a sparse component via an improved Optshrink algorithm, where the D transformation and its first derivative are further simplified to reduce the computational burden of the original OptShrink algorithm. Finally, the denoised signal is reconstructed by implementing an inverse ATSST on the low-rank component. We have tested the proposed method using synthetic and real datasets, and make a comparison with some classical denoising algorithms such as f - x deconvolution and Cadzow filtering. The obtained results demonstrate the superiority of the proposed method in denoising seismic data.

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

Lin Hongbo, Liu Chang, Wang Shigang, Ye Wenhai

Acta Geophysica

2023

Vol. 71, no. 6

2781--2793

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.