Sparse Constraint Inverse Data Domain Framework for Multiple Attenuation in Single-Channel Seismic Data

In marine seismic exploration, multiple reflections present a considerable challenge, complicating the process of data imaging and interpretation. Traditional methods for mitigating these multiples often rely on simplified assumptions and require extensive tuning of parameters. These approaches struggle to address the inherent variability and noise present in seismic data, leading to suboptimal results. This study introduces a novel framework, the Sparse Constraint Inverse data domain for Multiple Attenuation (SCIMA), specifically designed for single-channel seismic (SCS) data. SCIMA capitalizes on the unique properties of SCS data by incorporating sparse constraints within an inverse data domain, effectively enhancing noise resistance and preserving signal fidelity in a pseudo two-dimensional framework. This method offers an improvement over conventional attenuation techniques, which often fail to adapt to the complexities of real-world seismic data. The performance of SCIMA was validated using field data collected from an offshore wind farm, where it demonstrated a significant reduction in multiple reflections and a marked improvement in the signal-to-noise ratio. These results not only highlight SCIMA’s effectiveness in overcoming the limitations of traditional methods but also underscore the potential of the inverse data domain approach in practical field applications, representing a meaningful advance in seismic data processing technology.