Marine Data Research Articles

Regional Gravity Model of Greece Based on Satellite, Marine and Terrestrial Data

Greece is a country structured by land, several islands and sea. A regional gravity model of such a country demands the involvement of several types of gravity data in order to cover all its territory. In this paper, we present the development of a regional combined gravity model of Greece and its surrounding area, by integrating terrestrial, marine (shipborne and altimetry-derived gravity data) and satellite data (GOCE and GRACE data). These kinds of data, especially the terrestrial and marine ones, have passed quality and validation control since they were collected from different organizations, which means that they probably have been acquired and processed with different parameters and formulas. Following that, their integration was accomplished with the application of the least-squares collocation (LSC). Therefore, a newly combined regional gravity model of Greece and its surrounding areas has been developed. This gravity model can be valuable for regional geological and geophysical studies of Greece, since it provides homogeneously the distribution of Complete Bouguer anomaly (CBA) all over Greece, with updated gravity data and in good agreement with the initial gravity datasets that have been used.

Using Marine Data to Estimate the Accuracy of Free Air Gravity Anomaly Models Derived From Satellite Altimetry

AbstractGlobal gravity models from satellite altimetry missions have widely contributed to the improvement of the knowledge over the oceans. However, few models are available with a validated accuracy model, as the most recent models V21 to V32 from Scripps Institution of Oceanography (SIO). The aim of this paper is to propose a method to estimate the accuracy of any free air gravity anomaly model derived from satellite altimetry from validated marine data sets. The main idea is to correlate the error of the model to the mean roughness of the free air gravity anomaly on precise marine measurement areas. The relationship obtained is then generalized to each point of the model. This error grid is eventually combined with the grid provided by the satellite models, reflecting the errors related to the technology of the different altimetry satellites used. The method has been successfully applied to successive versions of SIO gravity anomaly models (from V18 to V31) to produce continuous accuracy grids over the North Atlantic Ocean. Validation of the results from independent marine data shows that the proposed approach allows to strongly improve the estimation of the used models' accuracy which converges, whatever the tested versions, toward a minimal value of 0.9 mGal over flat areas.

Deep-learning-based local wavefront attributes and their application to 3D prestack data enhancement

The work presents a novel workflow to accelerate the estimation of local wavefront attributes (LWAs) from massive 3D prestack seismic data using deep learning (DL) focusing on data enhancement. A standard estimation method based on a semblance-based brute-force optimization provides good results but is time consuming. A modification of the U-net convolutional neural network, commonly used in image processing applications, is proposed to link the seismic data with the wavefront attributes. Color pixel image input for the neural network is generated through a straightforward seismic data regularization based on supergrouping followed by red, green, and blue encoding. The proposed workflow can be adapted to any 3D prestack seismic volume. Conventional semblance-based attributes estimation is required for the training step but only for approximately 1% of the total data. The prediction step is very efficient and reduces the overall run time significantly. The verification of the proposed approach is performed on challenging real land and marine data sets. As a result, DL-based estimation of LWAs accelerates computation up to 200 times compared to the standard method. The attributes from the proposed DL-based approach indicate an acceptable match compared with the brute-force semblance-based optimization results. Conventional and proposed estimation methods result in comparable prestack data enhancement results for more reliable seismic processing in challenging areas.

Combining terrestrial, marine, and satellite gravity data to compute gravity potential values at IHRF stations

Combining terrestrial, marine, and satellite gravity data to compute gravity potential values at IHRF stations

Design of Vessel Data Lakehouse with Big Data and AI Analysis Technology for Vessel Monitoring System

The amount of data in the maritime domain is rapidly increasing due to the increase in devices that can collect marine information, such as sensors, buoys, ships, and satellites. Maritime data is growing at an unprecedented rate, with terabytes of marine data being collected every month and petabytes of data already being made public. Heterogeneous marine data collected through various devices can be used in various fields such as environmental protection, defect prediction, transportation route optimization, and energy efficiency. However, it is difficult to manage vessel related data due to high heterogeneity of such marine big data. Additionally, due to the high heterogeneity of these data sources and some of the challenges associated with big data, such applications are still underdeveloped and fragmented. In this paper, we propose the Vessel Data Lakehouse architecture consisting of the Vessel Data Lake layer that can handle marine big data, the Vessel Data Warehouse layer that supports marine big data processing and AI, and the Vessel Application Services layer that supports marine application services. Our proposed a Vessel Data Lakehouse that can efficiently manage heterogeneous vessel related data. It can be integrated and managed at low cost by structuring various types of heterogeneous data using an open source-based big data framework. In addition, various types of vessel big data stored in the Data Lakehouse can be directly utilized in various types of vessel analysis services. In this paper, we present an actual use case of a vessel analysis service in a Vessel Data Lakehouse by using AIS data in Busan area.

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA).

In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.

Generating live commentary for marine traffic scenarios based on multi-model learning

The Internet of Things (IoT), which allows for seamless communication and interaction with an array of smart devices, has rapidly become an indispensable part of our daily lives. Maritime transportation is one of the primary modes of transportation that heavily relies on Internet of Things (IoT) technologies to obtain crucial information such as atmospheric, oceanographic, navigation, and state data. By utilizing these IoT technologies, marine traffic scenarios can be constructed to obtain a comprehensive understanding of the traffic flow. Having a thorough understanding of marine traffic scenarios is crucial for improving the efficiency and safety of maritime transportation. Due to the time lag and data incompleteness inherent in marine traffic broadcasting and manual marine monitoring systems, it can be challenging to rely on them entirely. Meanwhile, the required resource to analyze marine traffic scenarios can be enormous. To address these challenges,we propose an approach based on multi-model learning for the automatic interpretation of marine traffic scenarios and developed an innovative dataset of multi-model marine traffic scenarios. Specifically, we have aligned maritime meteorological data with existing marine video data to create a comprehensive dataset. This dataset includes both raw data and manually created professional annotations to address the lack of benchmark datasets for multi-modal marine traffic scenarios. In addition to the dataset, we have developed a text generation model based on a multi-modal Transformer architecture. This model extracts the characteristics of multi-scale objects from visual marine frames by a parallel convolution mechanism, converts meteorological indexes into post-numeric codes, and combines the coding results of each mode through a multi-head self-attention mechanism to form fine-grained interpretation text. To train our text generation model for marine traffic scenarios, the threshold cross-entropy loss function is used to compensate the class imbalance. Our experimental results demonstrate the effectiveness of our approach, showing that our model can efficiently generate accurate and informative descriptions of maritime activity.

Implementation of ocean element curves and profiles on GIS

This paper introduces the realization algorithm of ocean element curves and profiles on GIS. The algorithm projects the curves and profiles of ocean elements onto the geographic map. It realizes the combination of two-dimensional spatial geographic location, time dimension of curve change and spatial dimension of vertical profile, and displays three-dimensional information on a two-dimensional plane map. It can well express the actual survey data, statistical analysis data, and realize the comprehensive visualization of marine elements. The algorithm is useful in the visualization of ocean data. The algorithm has good application prospects in the visualization display of marine data, quality control of survey data, joint analysis of different elements or disciplines, and scientific research.

Comparing eight remotely sensed sea surface temperature products and Bayesian maximum entropy-based data fusion products

Sea surface temperature is an important oceanographic physical attribute. In this work, 5 kinds of infrared SST data sets (including FY3C-VIRR, MetopB-AVHRR3, MODIS-Aqua, MODIS-Terra, SNPP-VIIRS) and 3 kinds of microwave SST data sets (including AMSR-2, GMI, WindSat) were considered for fusion purposes. The area with geographical coordinates ranging from 100° W to 180° W and from 10° N to 10° S was chosen as the study area. Using the Bayesian maximum entropy (BME) method, four SST fusion products were generated from January 1 to January 31, 2020, with temporal resolution of 1 day, spatial resolution of 0.25 decimal degrees, and coverage rate of 100%. The Holdout cross-validation method was employed to evaluate the performance of BME for SST interpolation purposes by using two data sets, i.e., WinSat SST and MODIS-Terra SST data; and the validation results showed that the mean absolute error (MAE) values of the WindSat SST data and the MODIS-Terra SST data were 0.117 mg/m3 and 0.191 mg/m3, respectively, and the root mean square error (RMSE) values were 0.158 mg/m3 and 0.321 mg/m3, respectively. In addition, four SST interpolation products were compared with the Optimum Interpolation Sea Surface Temperature (OISST) and the Group for High-Resolution Sea Surface Temperature (GHRSST) products by combining Argo data with Buoy data treated as in-situ measured data sets. Three (MW-WindSat SST product, ALL-Terra SST product and ALL-WindSat SST product) of the four interpolation products performed better than the OISST and GHRSST data sets. Among them, the ALL-WindSat SST product considering WindSat SST data and other 7 SST data as hard and soft data exhibited the best performance in terms of all three indicators: the coefficient of determination value (0.744), the MAE value (0.82 mg/m3) and the RMSE value (1.082 mg/m3). In sum, the present work made full use of the inclusiveness and integration abilities of the BME method for various types of data, and demonstrated its high accuracy in a number of real world data, thus demonstrating its considerable potential in the context of marine data interpolation and fusion.

Increasing Efficiency of Nautical Chart Production and Accessibility to Marine Environment Data through an Open-Science Compilation Workflow

Electronic Navigational Chart (ENC) data are essential for safe maritime navigation and have multiple other uses in a wide range of enterprises. Charts are relied upon to be as accurate and as up-to-date as possible by the vessels moving vast amounts of products to global ports each year. However, cartographic generalization processes for updating and creating ENCs are complex and time-consuming. Increasing the efficiency of the chart production workflow has been long sought by the nautical charting community. Toward this effort, approaches must consider intended scale, data quality, various chart features, and perform consistently in different scenarios. Additionally, supporting open-science initiatives through standardized open-source workflows will increase marine data accessibility for other disciplines. Therefore, this paper reviews, improves, and integrates available open-source software, and develops new custom generalization tools, for the semi-automated processing of land and hydrographic features per nautical charting specifications. The robustness of this approach is demonstrated in two areas of very different geographic configurations and the effectiveness for use in nautical charting was confirmed by winning the first prize in an international competition. The presented rapid data processing combined with the ENC portrayal of results as a web-service provides new opportunities for applications such as the development of base-maps for marine spatial data infrastructures.

A Comprehensive Study of Clustering-Based Techniques for Detecting Abnormal Vessel Behavior

Abnormal behavior detection is currently receiving much attention because of the availability of marine equipment and data allowing maritime agents to track vessels. One of the most popular tools for developing an efficient anomaly detection system is the Automatic Identification System (AIS). The aim of this paper is to explore the performance of existing well-known clustering methods for detecting the two most dangerous abnormal behaviors based on the AIS. The methods include K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Affinity Propagation (AP), and the Gaussian Mixtures Model (GMM). In order to evaluate the performance of the clustering methods, we also used the AIS data of vessels, which were collected through the Finnish transport agency from the whole Baltic Sea for three months. Although most existing studies focus on ocean route recognition, deviations from regulated ocean routes, or irregular speed, we focused on dark ships or those sets of vessels that turn off the AIS to perform illegal activities and spiral vessel movements. The experimental results demonstrate that the K-means clustering method can effectively detect dark ships and spiral vessel movements, which are the most threatening events for maritime safety.

Imaging ocean water columns by acoustic contrast reflection signals in existing marine seismic data

Marine seismic surveys conducted for oil and gas exploration use a source-receiver system consisting of airgun sources and hydrophone receiver arrays. The collected data were commonly used to image subsurface beneath the seafloor. The surveys had also collected signals of acoustic reflections from ocean water columns due to acoustic impedance contrast by temperature and salinity. These signals can be processed to provide high-resolution images of the water-column structures such as eddies and internal waves, forming a discipline termed as seismic oceanography in the literature. Acoustic contrast reflection signals in a three-dimensional seismic survey, collected by multiple parallel receiver arrays, can even be used to explore movements of water columns {see our recent study in [Zou,etal., Ocean Sci. 17(4), 1053–1066 (2021)]}. Nevertheless, because of the low acoustic impedance contrast in oceans, these water-column reflection signals are extremely weak, e.g., about 2–3 orders weaker than the seafloor reflections. Meanwhile, these signals consist of acoustic multipath features that require an appropriate filtering of the signals (see our recent study in the study by Zou and Zhang [JASA 150(5), 3852–3860 (2021)]. Understanding of the signal features and appropriate applications of acoustic signal processing methods are inspired to improve the imaging quality.

Enhanced detectability using differential fields for marine controlled-source EM data in shallow waters

ABSTRACT In shallow waters, the interpretation of the frequency-domain marine controlled-source electromagnetic (CSEM) data is challenging due to the airwave. The airwave dominates and will lead to misinterpretation of the data set. A differential-field approach is presented to attenuate the airwave for the shallow-water marine CSEM data. The difference of the fields between two consecutive receivers or source points is calculated and weighted by the geometric spreading related factor. By using differential fields, the detectability given by the field ratio between the models with and without the target is enhanced, which indicates that the impact of airwaves is suppressed.

Scaling of Activity Space in Marine Organisms across Latitudinal Gradients.

AbstractUnifying models have shown that the amount of space used by animals (e.g., activity space, home range) scales allometrically with body mass for terrestrial taxa; however, such relationships are far less clear for marine species. We compiled movement data from 1,596 individuals across 79 taxa collected using a continental passive acoustic telemetry network of acoustic receivers to assess allometric scaling of activity space. We found that ectothermic marine taxa do exhibit allometric scaling for activity space, with an overall scaling exponent of 0.64. However, body mass alone explained only 35% of the variation, with the remaining variation best explained by trophic position for teleosts and latitude for sharks, rays, and marine reptiles. Taxon-specific allometric relationships highlighted weaker scaling exponents among teleost fish species (0.07) than sharks (0.96), rays (0.55), and marine reptiles (0.57). The allometric scaling relationship and scaling exponents for the marine taxonomic groups examined were lower than those reported from studies that had collated both marine and terrestrial species data derived using various tracking methods. We propose that these disparities arise because previous work integrated summarized data across many studies that used differing methods for collecting and quantifying activity space, introducing considerable uncertainty into slope estimates. Our findings highlight the benefit of using large-scale, coordinated animal biotelemetry networks to address cross-taxa evolutionary and ecological questions.

Supporting marine data discovery and accessibility to enable cross-domain research

Supporting marine data discovery and accessibility to enable cross-domain research

Architectural Framework for Underwater IoT: Forecasting System for Analyzing Oceanographic Data and Observing the Environment

With the passage of time, the exploitation of Internet of Things (IoT) sensors and devices has become more complicated. The Internet of Underwater Things (IoUT) is a subset of the IoT in which underwater sensors are used to continually collect data about ocean ecosystems. Predictive analytics can offer useful insights to the stakeholders associated with environmentalists, marine explorers, and oceanographers for decision-making and intelligence about the ocean, when applied to context-sensitive information, gathered from marine data. This study presents an architectural framework along with algorithms as a realistic solution to design and develop an IoUT system to excel in the data state of the practice. It also includes recommendations and forecasting for potential partners in the smart ocean, which assist in monitoring and environmental protection. A case study is implemented which addresses the solution’s usability and agility to efficiently exploit sensor data, executes the algorithms, and queries the output to assess performance. The number of trails is performed for data insights for the 60-day collection of sensor data. In the context of the smart ocean, the architectural design innovative ideas and viable approaches can be taken into consideration to develop and validate present and next-generation IoUTs and are simplified in this solution.

Integral Sliding Mode-Based Fault-Tolerant Control for Dynamic Positioning of Unmanned Marine Vehicles Based on a T-S Fuzzy Model

This paper investigates a fault-tolerant control problem for the dynamic positioning of unmanned marine vehicles based on a Takagi–Sugeno (T-S) fuzzy model using an integral sliding mode scheme. First, the T-S fuzzy model of an unmanned marine vehicle is established by taking the yaw angle variable range into account. An integral sliding mode control scheme combined with the H∞ performance index is then developed to attenuate the initial influence of thruster faults and ocean disturbances. The unknown nonlinear function is approximated using a fuzzy logic system based on a representation of marine data, which provides a good tradeoff between resolution of the unknown nonlinear term approximation and computational complexity for marine engineering by adjusting the number of fuzzy logic system rules. In addition, the fault estimation information is utilized to design the sliding mode surface on the basis of an adaptive mechanism and a matrix full rank decomposition technique, which reduces conservatism. The validity of the proposed approach is finally demonstrated by an analysis of simulation results using a typical floating production vessel model.

Literature-based occurrences data of marine species in Venezuela.

Venezuela has suffered a severe academic and research management crisis and funding opportunities for marine research and data management have been practically absent. This has worsened over the past five years and, as a result, libraries and other institutional spaces have been repeatedly vandalised, with hundreds of records, specimens and historical data stolen, destroyed or burned. To avoid the loss of irreplaceable data on Venezuelan biodiversity, an initiative was promoted, aimed at digitising information to create a rich dataset of biodiversity records, with emphasis on marine protected areas for the country, as well as to fill gaps in the distribution and status of marine biodiversity in Venezuela. Nighteen (19) institutions in the country focusing on marine science have consistently produced a wealth of information about Venezuela's marine biodiversity in the form of specimen collections, unpublished sampled data and research theses through the work of hundreds of researchers and students. An inventory of available data sources at these national institutions was conducted under the National Biodiversity Data Mobilization Grant and the Biodiversity Information for Development Program, together with the Global Biodiversity Information Facility (GBIF) support. All recovered and processed datasets were published in the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF) repositories. This occurrences data collection represents a major contribution to the marine biodiversity inventory in Venezuela. It is based on numerous published papers, reports, books and checklists provided by experts, covering a broad taxonomic collection from which we obtained species occurrences (present and absent), organised into 59 datasets containing 40,881 records. This represents a 28.49% contribution to the records of the Venezuelan marine biodiversity reported to the OBIS (143,513 records in the OBIS until November 2022). The extracted data showed 3,041 marine species, with representatives of each of the six kingdoms: Animalia, Chromista, Bacteria, Plantae, Fungi and Protozoa. The datasets provide information on occurrence since 1822, extending the temporal coverage of the species occurrence inventory for Venezuela, which was established in 1879 before this project. The number of records for Venezuela increased by 41.3% compared with the data available before the project. Most of the occurrences (63.47%) were registered in Marine Protected Areas. Data collection included records of non-native species, descriptions of new species and species listed under different IUCN categories.

Unfolding the science behind policy initiatives targeting plastic pollution

The intensive global plastic production, use and associated plastic pollution have caused concern for the potential risks to human health and the environment. This has led to the adoption of numerous regulatory initiatives aiming to combat plastic pollution. Despite the considerable regulatory activity in the field of plastic, it appears that there is still debate about the actual risks of plastic to humans and the environment. This raises the question of to what extent the current plastic regulation is evidence-based, a declared ambition in the European Union. Therefore, the aim of this study was to investigate to what extent key policy initiatives targeting plastic pollution are based on scientific evidence. Selection of initiatives was based on expert elicitation accounting for the opinions of persons involved in the development of the policy initiatives, and a thorough assessment of the historical development of plastic pollution regulation, with focus on their importance both with respect to regulation of plastics as well as their historical importance as drivers for societal actions on plastic pollution. We find that scientific evidence appears to be generally present in the scientific foundation for the policy initiatives analysed in this study. All the initiatives are supported by scientific articles and reports about among others plastic sources, ecological impacts of plastic production and consumption patterns. Marine litter monitoring data was found to contribute to the evidence base for 4 out of the 6 policy initiatives and thereby appears to be one of the central scientific drivers behind the societal actions on plastic pollution. Other scientific tools applied when shaping the policy initiatives include risk assessment, impact assessment and life cycle assessment. Despite the prevalent consideration and application of scientific evidence, there seems to be a broad recognition in the preparatory work of the initiatives that there is still a lot of uncertainty related to determining the harm of plastic pollution. In these cases, taking precautionary actions seems however to be justified, recalling not least the precautionary principle. As the issue of plastic pollution is complex and still subject to uncertainty, it seems important both that policy initiatives allow for flexibility and continuing adjustment to the on-going knowledge generation and that the scientific community provides the needed research to continue the science-informed policy development.

A New Wave Energy Converter for Marine Data Buoy

Providing stable and reliable power supply for offshore equipment with special power requirements, such as marine data platforms and marine buoys far away from the coast is currently topical research issue. At present, most of these devices use photovoltaic power generation or install high-capacity batteries and carry out regular replacement of electricity supply schemes. Due to the high dependence of photovoltaic power generation on solar radiation and the high cost arising from frequent battery replacements, wave energy is considered as a solution. Wave energy converters (WECs) are devices that convert the energy stored in waves into electrical energy, and its main advantage is the sustainable supply of electricity. In this article, a new built-in wave energy converter with high reliability and low maintenance costs is proposed and investigated for marine data buoys. Its core is a direct-drive energy conversion unit based on a magnetic lead screw design. The device can passively follow the pitching state of the buoy under the impact of waves to maximize its power generation. The proposed WEC is introduced and investigated through model analysis and prototype experiments. The experimental results confirm the power generation performance of a 50 kg power generation prototype and show the advantages of the proposed device.