Residual Learning for Enhanced Seismic Signal Quality with Denoising CNN and Kalman Filtering

This study introduces an innovative approach to enhance seismic signal quality through the integration of Denoising Convolutional Neural Networks (DCNN) and Kalman Filtering (KF). Seismic data often suffers from pervasive noise, impacting the accuracy of signal interpretation. In our methodology, Denoising CNN serves as a robust feature extractor to effectively suppress noise, while Kalman Filtering contributes adaptive noise tracking to refine the denoising procedure. Extensive studies were carried out to evaluate the efficacy of our strategy. on real-world seismic datasets. A comparative analysis was performed against conventional denoising algorithms are compared with Wavelet Transform (WT), Sparse Coding (SC), and Non-Local Means (N-LM). The results indicate that our proposed framework significantly outperforms these benchmark algorithms, achieving noteworthy improvements in accuracy, noise reduction, and signal preservation (SP). Our approach demonstrated an accuracy enhancement of up to 94.33% compared to existing methods, showcasing its superior performance in preserving crucial seismic signal information. This study not only advances seismic data processing but also provides a practical solution for reliable signal interpretation in exploration activities. The synergistic combination of Denoising CNN and Kalman Filtering establishes a potent strategy for superior seismic noise suppression, offering promising prospects for enhancing the overall quality of seismic data analysis.