Accurate Multiple Wave Suppression Using Data Correlation and Long Short-Term Memory Networks

Seismic data processing often faces challenges in accurately suppressing multiple waves while preserving primary waves. In this study, we introduce a novel approach that integrates data correlation (DC) techniques with long short-term memory (LSTM) neural networks, referred to as DC-LSTM, to address this issue. By utilizing the positional indices of primary and multiple waves in common midpoint (CMP) gathers, DC-LSTM effectively trains the LSTM model for precise prediction and suppression of multiple waves. Experimental evaluations using synthetic and real seismic data demonstrate that DC-LSTM significantly outperforms the Radon transform, offering a robust solution for enhancing seismic data quality by achieving superior primary wave preservation and reducing pseudo-frequency artifacts.