No abstract

The field of forecasting oceanic variables has traditionally relied on numerical models, which effectively consider the ocean's dynamic evolution and are of physical importance. However, to make the models more realistic, complicated processes need to be considered, which is difficult because their calculations are complex. In fact, information on the internal dynamic mechanisms and external driving forces of the ocean are already embedded in the time series of observations. Therefore, we can determine the patterns of ocean variations through data mining of these series to achieve forecasting. Furthermore, to predict variations in ocean processes more realistically, interactions between variables and spatial correlations should be effectively considered. Thus, inspired by available remote sensing data and advancements in deep learning technologies, we develop a hybrid model based on a statistical method and a deep learning model to predict multiple sea surface variables. A case study in the South China Sea shows that this model is highly promising for short‐term daily forecasts of the sea surface height anomaly (SSHA) and sea surface temperature (SST). When the forecast time is 10 days, the root mean square errors of this model forecasts for SSHA and SST are approximately 0.0276 m and 0.46°C, respectively, which are much smaller than those of persistence, climatology and linear regression predictions. The anomaly correlation coefficients for SSHA and SST are approximately 0.864 and 0.633, respectively. The model performs satisfactorily under both normal and typhoon weather conditions.

 A data-driven prediction model based on empirical orthogonal function, complete ensemble empirical mode decomposition and artificial neural networks is proposed. Effectively considers the correlations not only of different spatial points but also of different ocean variables. Spatial domain prediction of sea surface multivariate for 30 days.

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

The exploitation and utilization of seabed sediments provide vital significance in many field. Recently, the classification of seabed sediments using Sub-Bottom Profiler (SBP) data has become a research focus. Concretely speaking, SBP data can be applied not only for recognizing the individual stratigraphic layers but also for classifying the seabed sediments by inversion models. To improve the sorting effect of gravel and mud simultaneously, we propose a sediment classification method based on the back propagation neural network (BPNN) with the Biot-Stoll model and the Attenuation-Based model. In this method, two datasets of the mean grain size derived from these two models respectively are trained through the BPNN classifier to classify seabed sediments. The proposed method is verified through the SBP data and in-situ sampling data collected from the sea north of Shandong Peninsula, China. The experimental results show that the overall accuracy of sediment classification is 89.4%, and the classification accuracy of gravel and mud reach 91.4% and 93.3%, respectively, confirming that gravel and mud can be more accurately distinguished based on the proposed method than the single Biot-Stoll model and the single Attenuation-Based model.