Pollution caused by ship emissions has drawn attention from various countries. Because of the high density of ships in ports, channels, and anchorages and their proximity to the densely populated areas, ship emissions will considerably impact these areas. Herein, a Chinese seagoing ship is selected and a platform is established for monitoring the ship emissions to obtain detailed characteristics of the ship’s nearshore emissions. The ship navigation and pollution emission data are obtained under six complete operating conditions, i.e., berthing, manoeuvring in port, acceleration in a channel, cruising, deceleration before anchoring, and anchoring. This study analyzes the concentrations of the main emission gases (O₂, NO_X, SO₂, CO₂, and CO) and the average emission factors (EFs) of the pollution gases (NO_X, SO₂, CO₂, and CO) based on the engine power under different operating conditions. Results show that the change in O₂ concentration reflects the load associated with the main engine of the ship. The NO_X, SO₂, and CO₂ emission concentrations are the highest during cruising, whereas the peak CO emission concentration is observed during anchoring. The average EFs of NO_X and SO₂ based on the power of the main engine are the highest during cruising, and those of CO₂ and CO are the highest after anchoring. The ship EFs are different during acceleration and deceleration. By comparing the EFs along the coast of China and the global EFs commonly used to perform the emission inventory calculations in China, the NO_X EFs under different operating conditions is observed to be generally lower than the global EFs under the corresponding operating conditions. Furthermore, the SO₂ EF is considerably affected by the sulfur content in the fuel oil and the operating conditions of the ship. The average CO₂ EFs are higher than the global EFs commonly used during cruising, and the CO EFs are higher than the global EFs under all the conditions. Our results help to supplement the EFs for this type of ship under different operating conditions, resolve the lack of emission data under anchoring conditions, and provide data support to conduct nearshore environmental monitoring and assessment.

The sound speed of seawater is not constant. It varies with season, day time, depth and range. This variation was not considered in marine seismic data processing and imaging before deep-water seismic acquisition became a routine activity. As a result, non-ignorable errors may be contained in the final migrated-image of deep-water seismic data. To eliminate such errors, we propose here a scheme for inverting the seawater velocity under the condition that the seabed has a complex topography. In this scheme, the seabed topography is represented by step grids constrained by measured water depth data, and the sound profile is assumed to be the one satisfying the Munk formula. Thus, only the parameters appearing in the Munk formula need to be inverted. This is quite different from the conventional inversion schemes that take the velocity as the inversion target. We use the conjugate gradient method to minimize the objective function given as the sum of squares of traveltime differences. Tests on synthetic common-shot data set show that our scheme presented here works as expected. Applications to real marine data set further validates the reasonability and feasibility of our scheme under complex seabed topography. To investigate the reliability of our inversion scheme, we migrate the real 2D data set using the Kirchhoff migration with the inverted Munk profile as the velocity model. The corresponding results show that the seawater velocity obtained by our inversion scheme improves the imaging quality of underwater structures.

Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity.

In marine seismic data processing, velocity errors in deep-sea water can lead to distortions in seabed positioning, which in turn cause deformations in deeper geological strata. Existing methods suffer from sparse data, limited applicability, and an inability to invert for the deep-sea sound channel structures. Therefore, we propose a method employing wave equation traveltime inversion (WTI) for the velocity of deep-sea water body. When traditional ray-based traveltime methods are used in complex seabed topographies, seismic wave propagation paths become extraordinarily intricate, rendering ray tracing and traveltime picking imprecise. Conversely, the WTI does not require ray path tracking nor seabed reflection traveltime picking, enabling more accurate simulation of wavefront diffusion in complex structures. Through experiments conducted on theoretical models and actual data, it has been verified that the WTI method can provide more accurate inversion results in complex seabed topography, reliably depicting the velocity structure of deep-sea water body even when the initial velocity model is far from the true seawater velocity.

Vessel trajectory prediction is pivotal for maritime traffic safety and autonomous collision avoidance. However, existing studies predominantly rely on massive public AIS (Automatic Identification System) datasets, often overlooking the challenges of data sparsity and long-tailed distributions inherent in practical engineering scenarios, where high-dynamic maneuvering samples are scarce. Furthermore, as a low-frequency kinematic observation system, AIS suffers from inherent kinematic lag relative to the vessel’s true dynamic state, particularly failing to timely reflect turning intentions during the maneuver initiation phase. To address these challenges, this paper proposes a Physics-Aware Multimodal Fusion Framework. By incorporating high-frequency acceleration and angular velocity from an Inertial Measurement Unit (IMU), the framework applies physical compensation to AIS kinematic observations, thereby enhancing the model’s perception of maneuvering intent. Validation based on real-vessel experimental data demonstrates that the proposed method effectively mitigates the prediction lag observed in pure-AIS models at the 60 s horizon, significantly improving accuracy in turning scenarios. Moreover, in 180 s long-term predictions, the multimodal fusion mechanism effectively suppresses integration drift, ensuring superior trajectory shape consistency and engineering stability. The study indicates that incorporating IMU inertial information is critical for enhancing the robustness of vessel trajectory prediction under practical engineering conditions characterized by sparse samples and complex maneuvers.