Data weighted full-waveform inversion with adaptive moment estimation for near-surface seismic refraction data

Cai, Ao; Zelt, C. A.

doi:10.1190/segam2019-3216353.1

Cited by 6 publications

(1 citation statement)

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“…Noise can contaminate seismic data and ultimately cause artifacts in seismic migration and inversion that may eventually lead to poorquality seismic images and incorrect interpretation (Calvert, 2004;Cai and Zelt, 2019). Therefore, noise attenuation is often applied in the early stages of seismic processing.…”

Section: Introductionmentioning

confidence: 99%

Attenuating coherent environmental noise in seismic data via the U-net method

Stewart²

2023

Front. Earth Sci.

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Noise attenuation is a key step in seismic data processing to enhance desired signal features, minimize artifacts, and avoid misinterpretation. However, traditional attenuation methods are often time-consuming and require expert parameter selection. Deep learning can successfully suppress various types of noise via a trained neural network, potentially saving time and effort while avoiding mistakes. In this study, we tested a U-net method to assess its usefulness in attenuating repetitive coherent events (e.g., pumpjack noise) and to investigate the influence of gain methods on denoising quality. We used the U-net method because it preserves fine-scale information during training. Its performance is controlled by network parameters and improved by minimizing misfits between true data and network estimates. A gain method is necessary to avoid the network’s parameter optimization being biased toward large values in data. We first generated synthetic seismic data with added noise for training. Next, we recovered amplitudes using an automatic gain control (AGC) or a 2D AGC (using adjacent traces’ amplitudes). Then, a back-propagation algorithm minimized the Euclidean norm cost function to optimize the network parameters for better performance. The updating step size and direction were determined using an adaptive momentum optimization method. Finally, we removed the gain effect and evaluated the denoising quality using a normalized root-mean-square error (RMSE). Based on RMSE, the data pre-processed by the 2D AGC performed better with RMSE decreasing from 0.225 to 0.09. We also assessed the limitations of the network when source wavelets or noise differed from the training set. The denoising quality of the trained network was sensitive to the change in the wavelet and noise type. The noisy data in the limitation test set were not substantially improved. The trained network was also tested on the seismic field data collected at Hussar, Alberta, by the CREWES Project. The data had not only excellent reflection events but also substantial pumpjack noise on some shot gathers. We were able to significantly reduce the noise (favorably in comparison to traditional techniques) to considerably allow greater reflection continuity. U-net noise reduction techniques show considerable promise in seismic data processing.

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Section: Introductionmentioning

confidence: 99%