Single-channel seismic (SCS) methods play a crucial role in offshore wind farm assessments, offering rapid and continuous imaging of the subsurface. Conventional SCS methods often fall short in resolution and signal completeness, leading to potential misinterpretations of geological structures. In this study, we propose the application of seismic interferometry as a powerful tool to address these challenges by utilizing multiple reflections that are usually considered as noise. First, we demonstrate the feasibility of using seismic interferometry to approximate the primary wavefield. Then, we evaluate a series of seismic interferometry applied in SCS data, including cross-correlation, deconvolution, and cross-coherence, and determine the most appropriate one for our purpose. Finally, by comparing and analyzing the differences in amplitude, continuity, time–frequency properties, etc., between conventional primary wavefield information and reconstructed primary wavefield information by seismic interferometry, it is proved that incorporating multiples as supplementary information through seismic interferometry significantly enhances data reliability and resolution. The introduction of seismic interferometry provides a more detailed and accurate geological assessment crucial for optimal site selection in offshore wind farm development.

Indirect observation systems, such as hyperspectral remote sensing and passive geophysical measurements, retrieve useful information from redundant observations of the same scene. However, the resulting data are often contaminated by structured coherent artifacts caused by sensor nonuniformity, calibration residuals, or incomplete illumination. These artifacts are difficult to suppress because they are spatially organized components with directional continuity and non-negligible correlation. Their signal-like coherence allows them to mimic image textures or physical events, making conventional denoising methods prone to residual artifacts or signal leakage. To address this problem, we propose a consistency-guided collaborative filtering framework for suppressing structured coherent artifacts while preserving useful signals. The proposed framework extends paired-observation similarity analysis into a consistency-guided strategy for redundant observations. Paired observations of the same target are constructed to distinguish useful signals from physically inconsistent artifacts. This consistency contrast is incorporated into collaborative filtering to guide block matching and aggregation, while a coherent noise power spectral density model characterizes the directional and spatial correlation of the artifacts for targeted noise shrinkage. The proposed framework is evaluated primarily on hyperspectral remote-sensing images contaminated by simulated stripe artifacts, with additional validation on synthetic and field geophysical paired-observation data containing nonphysical coherent events. The results demonstrate that the proposed method can suppress structured coherent artifacts while preserving useful signals and maintaining high signal fidelity. This work provides a unified way to exploit observational redundancy for enhancing imaging reliability.

To address the conflict between trajectory fragmentation and the trade-off between association efficiency and data integrity in underwater micro-scale multi-target sonar motion detection and tracking in video sequences, a multi-target motion detection and tracking algorithm based on a dual trajectory storage mechanism and adaptive trajectory association is proposed. The method first obtains target centroids through Gaussian mixture model foreground extraction, morphological post-processing, and connected region analysis. By employing a dual-storage structure consisting of real-time trajectories and complete trajectories, it dynamically adjusts association thresholds based on frame sampling rates to achieve adaptive distance calculation for trajectory tracking. Experimental results demonstrate that the proposed method achieves a completeness rate of 100% in recording valid trajectory point lengths. The adaptive threshold mechanism improves association accuracy to 96.07% while reducing trajectory fragmentation rate to 0.9%. The average association time is 0.28 ms per frame, enabling efficient real-time association while ensuring the integrity of motion trajectory tracking. This research contributes to enhancing real-time detection and tracking capabilities for micro-scale underwater targets and provides support for applications such as underwater security surveillance, marine resource exploration, and intelligent autonomous underwater vehicle navigation.

Bubbles in submarine cold-seep plumes cause strong scattering, attenuation, and poor continuity in water-column seismic responses, hindering continuous two-dimensional characterization of acoustic properties. Using multichannel seismic data from the Shenhu area, northern South China Sea, this study proposes a conductivity–temperature–depth (CTD)-constrained, layer-wise two-angle allied elastic impedance (AEI) inversion method to reconstruct water-column velocity. Based on the scattering-equivalent AEI theory, a two-angle equation is derived for layer-wise estimation of water-column velocity, thereby providing a theoretical basis for the subsequent inversion. A relative-amplitude-preserving workflow mitigates bubble-induced attenuation and random scattering, while pre-stack time migration produces common reflection point (CRP) angle gathers. Coherent scattered energy after imaging is parameterized by the local scattering half-angle. Small-angle and large-angle gathers are selected according to effective angular coverage for AEI inversion. CTD-derived seawater velocity and density provide AEI constraints, and the normalized two-angle AEI equation yields a continuous velocity section. Results show velocities of 1460–1560 m/s, with pronounced vertical stratification, relatively weak lateral variation, and agreement between inverted and CTD-derived velocity trends. Within the 0–1800 ms water-column time window, the root-mean-square difference between the inverted and CTD-derived velocities is 18.19 m/s. Combining the lateral continuity of multichannel seismic data with high-accuracy CTD-derived physical-property constraints enables continuous two-dimensional velocity reconstruction in bubble-scattering water columns and offers a new approach for observing cold-seep velocity structure and geophysically characterizing bubble-plume activity.