Abstract: The deepwater area of the southern South China Sea is a crucial strategic successor zone for petroleum resources in China, and its exploration and development hold significant importance in ensuring national energy security. As exploration targets extend to greater depths, the quality of mid- to deep-seismic imaging has become a key constraint on understanding deep geological structures and identifying hydrocarbon reservoirs. Seismic data in this region face challenges such as a low signal-to-noise ratio (SNR) at low frequencies, a narrow effective bandwidth due to ghost-notch effects, and inaccurate velocity modeling in complex structural areas. To address these issues, this study proposes an integrated processing workflow that employs amplitude-preserving broadband noise attenuation technology to suppress low-frequency noise and improve low-frequency signal-to-noise ratio (SNR), applying adaptive de-ghosting technology to eliminate notch effects, broadening the effective bandwidth, and introducing a high-density dual-spectrum velocity analysis to construct an accurate anisotropic velocity model. Application to real data demonstrates that this combination of technologies effectively improves the quality of mid- to deep-seismic imaging. The clarity of deep reflective structures and the continuity of seismic events were significantly enhanced, providing a reliable geophysical data foundation for hydrocarbon exploration in deepwater areas.