Deep Denoising Autoencoder for Seismic Random Noise Attenuation

Attenuation of seismic random noise is considered as an important processing step to enhance the signal-to-noise ratio (S/N) of the seismic data. A new approach is proposed to attenuate random noise based on a deep denoising autoencoder (DDAE). In this approach, the time-series seismic data are utilized as an input for the DDAE. The DDAE encodes the input seismic data to multiple levels of abstraction, then decodes those levels to reconstruct the seismic signal without noise. The DDAE is pre-trained in a supervised way using synthetic data, following this the pre-trained model is used to denoise the field dataset in an unsupervised scheme using a new customized loss function.

doi:

https://www.researchgate.net/publication/340793215_Deep_Denoising_Autoencoder_for_Seismic_Random_Noise_Attenuation