Novel Residual Learning using Denoising CNN Framework for Seismic Noise Suppression

Seismic Noise concealment is a significant stage in subsurface designs, Tragically nature of denoising techniques vigorously depend. This paper presents a novel framework for seismic noise suppression using Residual Denoising Convolutional Neural Network (D-CNN) framework is focused with different challenges seismic data, including complex waveforms and variable noise patterns. The core innovation lies in the integration of residual learning principles within a proposed work, enabling the network to learn both low-level noise characteristics and high-level seismic features. The preliminary comparisons with traditional noise reduction techniques and standard CNN models, as evidenced by quantitative metrics such as Signal-through-Noise Ratio (SNR) and Root Mean Square Error (RMSE). our denoised seismic data showcases enhanced interpretability and structural coherence, paving the way for more accurate subsurface imaging, reservoir characterization, and resource exploration. In comparison, the accuracy of the DCNN technique is around 89.36%, but the accuracy of the CNN approach is approximately 80.63.