There are few researches on offshore gusts at home and abroad, and most of them focus on gust prediction and application research. There is no systematic discussion on the acquisition technology of wind gust data. Based on the backscattering coefficient observed by HY-2B satellite radar altimeter and the brightness temperature information observed by correction microwave radiometer, a method for retrieving gust wind speed is proposed in this paper. The gust wind speed obtained from the joint inversion of the two remote sensing sensors is verified with the National Data Buoy Center (NDBC) buoy data from 2019 to 2021. The results show that the gust wind speed root mean square error (RMSE) is 0.98 m/s and the correlation coefficient is 0.82. The RMSE of the gust wind speed obtained based on the method using a similar satellite Jason-3 is 0.96 m/s and the correlation coefficient is 0.88. Based on the observation of sea surface wind speed with HY-2B satellite radar altimeter and the synchronous observation information of correction microwave radiometer by satellite platform, the observation of sea surface wind gust is realized jointly. The comparison results of data show that the method in this paper has high observation accuracy. At the same time, this method is also applicable to domestic and foreign satellites with the same observation system. This provides a simple and reliable means of ocean remote sensing technology for the current situation of insufficient observation capacity of offshore wind gust.

Retrieving shallow water depth based on multispectral satellite imagery is highly cost-effective. However, the extensive application of satellite-derived bathymetry has been restricted by its low prediction accuracy. To improve about the accuracy of the retrieved bathymetry, spatial autocorrelation features within the in situ depth measurements and the multi-spectral image are focused in this research. To this end, we develop a machine learning method combining with spatial autocorrelation features and statistical intercorrelation features of learned samples. The experimental results of Xisha Beidao show that compared with the traditional machine learning, the accuracy of the new method is improved by 18% when the number of in situ depths is small. On the contrary, when the number of in situ depths is large, an improvement of 27% in root mean square error is achieved. This demonstrates that incorporating the spatial autocorrelation features of data sources into the machine learning can significantly improve the prediction accuracy, and then provide effective data support for shallow ocean research.

The Yap Trench is an important part of the trench-arc-basin system in the western Pacific Ocean. In the northern part of the Yap Trench, the Yap Trench and the Mariana Trench are typically vertically intersected. The geomorphology of the sea area was studied in detail. The results show that the water depth, morphology and profile of the trenches change obviously near the junction of the two trenches, and have segmented. The slopes on both sides have uplift, depression and fault geomorphology, which are closely related to the special subduction position at the junction of the trenches. In addition, according to the geomorphic characteristics and plate spreading rate, the spreading center of the Parece Vera Basin should be located near 137°35'34''E before 20 Ma, and the Yap Trench is likely to be transformed from the spreading center exposed by the Parece Vera Basin.

Based on the field data of floc size, suspended sediment concentration and hydrodynamic data, the flocculation characteristics of tidal flat in the south of the Huanghe River Estuary are studied. The results show that the floc size of Huanghe River Estuary tidal flat range is 25.42–264.44 μm, and the average diameter is 95.20 μm. The effect of water turbulence on the flocculation of tidal flat at the Huanghe River Estuary is different, and the upper limit of turbulence on flocculation promotion is about G_l=3.76 s^–1. While the turbulence intensity of water is lower than G_l, turbulence promotes sediment flocculation, and the particle size of floc increases with the strengthen of turbulence intensity, whereas the turbulence mainly inhibits flocculation, the particle size of floc decreases with the attenuate of turbulence intensity. The suspended sediment concentration inhibits the flocculation, particle size of floc corresponding to high sediment content under the same turbulent conditions is smaller. There is a negative correlation between the effective density and the particle size of the floc, and the settling speed of the floc is mainly affected by the particle size. This study complements the understanding of sediment flocculation characteristics in tidal flat of weak tidal estuary.

Global navigation satellite system-reflectometry (GNSS-R) technology is an emerging technology for monitoring sea level changes. Based on the principle of the signal to noise ratio (SNR) analysis method in GNSS-R technology, this paper established a new sea level height estimation model to improve the accuracy by analyzing the process of separating the trend term and extracting the oscillation frequency. Aiming at the problem of poor signal separation in the traditional model, this paper proposed to use the variational mode decomposition (VMD) algorithm to replace the traditional least squares fitting (LSF) to separate the trend term components. On this basis, this paper combined Lomb-Scargle Periodogram (LSP) spectral analysis method and Kaiser window function (referred to as WinLSP) to reduce the inversion error caused by spectral leakage. The results of sea level inversion experiments carried out at GTGU Station in Onsala, Sweden and SC02 Station in Alaska, USA show that the estimation model established in this paper has higher inversion accuracy than traditional model. The root mean square error (RMSE), correlation coefficient and number of inversion points of the inversion results of GTGU Station based on the VMD+WinLSP estimation model are 4.70 cm, 0.98 and 5 647, respectively. The inversion accuracy and GNSS data utilization are increased by about 29.7% and 15.0%, respectively; The RMSE, correlation coefficient, and inversion points of SC02 Station are14.34 cm, 0.99 and 1 785, respectively, and the inversion accuracy and GNSS data utilization are increased by about 12.3 % and 9.4%.

The purpose of this paper is to clarify the genesis of natural gas of Y gas field, and establish accumulation model to guide the next exploration deployment in X Sag, East China Sea Basin. Based on the analyses of natural gas composition, carbon isotope of alkane gas, light hydrocarbon and biomarker compound of condensate oil, this paper systematically studies the genetic types and sources of oil and gas, establishes the reservoir accumulation model of Y large and medium-sized gas field, and puts forward the exploration direction of large and medium-sized gas field. The analyses of carbon isotope, light hydrocarbon and burial history show that the natural gas in Y gas field is highly mature coal type gas generated by the source rocks of middle Eocene Pinghu formation in the sag during the Longjing movement period (13 Ma BP). The characteristics of pristane/phytane and regular sterane of condensate oil reflect that the source rocks of middle Eocene Pinghu formation in the central sag are developed in tidal flat and lagoon sedimentary environment with weak oxidation weak reduction, and there are a certain number of lower aquatic organisms in the hydrocarbon generating parent material. Y gas field has a spatiotemporal coupling reservoir accumulation model of “Pinghu formation source rock, Huagang formation large channel sand reservoir and Mid-Miocene compressional tectonism” in the central sag. It is clear that the compressional anticline belt in the central sag is the main exploration direction of large and medium-sized gas fields in X Sag.

Estuarine flow is the fundamental driving of physical processes such as estuarine ecological environment, river evolution and material transport. Due to the runoff, tide, topography and meteorology, the estuarine flow presents a complex three-dimensional structure, including not only the residual flow induced by fresh water injection, but also the periodical tidal current, wind-driven flow, baroclinic flow and the residual flow caused by estuarine nonlinearity. In order to explore the composition and intra-tidal variation of estuarine flow, based on the field data in the Oujiang River Estuary, the estuarine flow is decomposed by principal component analysis (PCA) method, and the decomposition performance of PCA method for estuarine flow and high-frequency characteristics of baroclinic overtide are deeply discussed. The results show that PCA method can operate original data or standardized data in the study of estuarine flow structure. PCA method can decompose baroclinic components (estuarine gravitational circulation structure), but cannot separate barotropic components (runoff and tidal current). The coupled effect of runoff and tidal current is reflected in the score of principal components. The filter of principal components should employ conjoint judgment of flow structure and cumulative interpretation variance, rather than only cumulative interpretation variance. The estuarine baroclinic flow has clearly high-frequency characteristics, which approximates quarter-diurnal.

Swordfish (Xiphias gladius) is a highly migratory fish whose habitat suitability is significantly influenced by the marine environment, and the prediction of its habitat using changes in the marine environment is of great scientific importance. In this study, we used the catch information of swordfish in the Chinese Indian Ocean Longline Fisheries Observer Data from 2017 to 2019 as species occurrence data, combined with the environmental data in the western Indian Ocean waters, including sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a) concentration, mixed layer depth (MLD), and sea surface salinity (SSS), the habitat suitability distribution of swordfish in the western Indian Ocean is simulated by using a maximum entropy model (MaxEnt). Model results show that: (1) the model has very high accuracy in simulating the habitat suitability distribution of swordfish in the western Indian Ocean, with AUC values greater than 0.9 in all seasons, and can be used to simulate the potential habitat suitability distribution of swordfish; (2) changes in the distribution of suitable habitat for swordfish in the study area are generally consistent with changes in the actual operational location, and the distribution of areas with high habitat suitability for swordfish is more concentrated in both the dry and rainy seasons, but the distribution range is greater in the wet season than in the dry season; (3) SST, SSS and MLD are important environmental factors affecting the habitat suitability distribution of swordfish in the western Indian Ocean. The optimum ranges of SST, SSS and MLD in the dry and rainy seasons are 25.8−31.6°C, 34.4−35.9 and 0.1−24.9 m, and 25.6−30.5°C, 34.8−36.4 and 13.1−54.1 m, respectively. The results of the study provide essential reference information for the sustainable use and scientific management of swordfish populations in the western Indian Ocean.

Based on the Earth System Model (ESM2M) of the Coupled Model Intercomparison Project 5 (CMIP5), combined with Argo observation data and the reanalysis dataset compiled by Ishii et al., this paper presents the seasonal variation characteristics of mixed layer depth (MLD) and subduction process in the subtropical Northeast Pacific Ocean (10°−40°N, 110°−160°W) under the present climate background and extreme enhancement of radiative forcing are presented, to study its response to global warming. Under the current climate background, both of MLD and subduction rate reach their maximum values in winter. The main contribution items of subduction rate have significant seasonal variation. From January to May, the subduction rate is mainly controlled by the change of lateral induction rate, while from June to December, the main mechanism is the change of Ekman pumping velocity controlled by wind stress curl. After global warming, the main control elements of seasonal signals remain unchanged. However, under the influences of wind stress curl and other factors, the MLD in each season decreased and the range of the core maximum region shrinks. As the decrease in winter is much larger than that in summer, the seasonal fluctuation range (amplitude) of MLD is significantly smaller. In the long run, MLD shows a trend of continuous shallower, and the weakening of MLD front caused by the weakening of its spatial non-uniformity is the key to control the weakening of lateral induction rate and eventually lead to the weakening of the subduction rate. Since the seasonal variation signal of Ekman pumping velocity has little response to global warming, subduction rate is most strongly affected in winter. The results show that the contribution proportion of the two key factors to the subduction rate changes with the seasons: when the MLD front is strong in winter, the influence of the lateral induction rate will be significantly enhanced. The different variations of the two factors before and after global warming will significantly change the seasonal amplitude of the subduction rate, which may have a profound impact on the formation and transport of mode water in the region.

Marine sediments are characterized by soft texture and low strength, and their strength parameters are closely related to offshore platform pile placement and jacket installation, and are closely related to offshore operation safety. Conventional methods for obtaining strength parameters, such as drilling and cone penetration test, have high cost, few sampling points, and great disturbance to the soil. Therefore, it is of great significance to predict the strength parameters of marine sediments with easily accessible acoustic data. Based on the acoustic propagation theories such as Wood equation, Biot-Stoll model and Dvorkin equivalent medium model, the theoretical P-wave velocity under different physical parameter (density, porosity) gradients was calculated. Combined with the indoor simulated stratum acoustic experiment, the variation characteristics of the calculated and measured sound velocity were compared, and the relationship model between sound velocity and physical parameter was established. The relationship between soil physical parameter and shear strength, cohesive force and other parameters was revealed based on laboratory geotechnical test, and the relationship model between soil physical parameter and strength parameters was established. A prediction model for strength parameters of marine sediments based on acoustic characteristics was established by using physical parameters as bridge. This model not only avoids the problem of water loss disturbance of in-situ sampled soil, but also makes up for the limitation of empirical formula. It has universality and accuracy, and can effectively improve the precision of strength parameters of soil in unsampled areas, improve economic benefits, and play a theoretical guiding role in shallow exploration and development.