The Feasibility Study Of Sea Turtles As Carriers Of Ocean Observation

The ocean has been closely linked with the survival of mankind for long time.Exploring amount of the resources of the oceans,researching the protection of the marine ecological environment,forecasting and warning of natural disasters,and maritime military activities are inseparable from the marine environment.Therefore,research on the marine environment is increasingly drawing attention from various countries.The premise of exploring the marine environment is the real-time monitoring of the marine environment to obtain massive monitoring data,and the monitoring carrier of marine data is the cornerstone of data acquisition.The marine monitoring carrier is an important component of establishing a real-time,all-round monitoring system for the marine environment,and it has been a research hotspot for various oceanic countries in the world over the years.Firstly,this paper analyzes and summarizes the types of traditional marine monitoring carriers,describes their core key technologies and their application,and points out the shortcomings such as large cost consumption,limited scope of observation by the marine environment,and insufficient flexibility.As a new type of marine monitoring carrier,marine animals have a natural advantage in ocean exploration and can acquire massive ocean data that humans cannot detect.Based on the use of language of Python,the paper analyzes marine temperature monitoring data by selecting sea turtles as the carrier,and demonstrates the feasibility of sea turtles as marine monitoring carriers for marine monitoring and the significance of animal observation of oceans.The following work has mainly been done:1)Introduce temperature monitoring data for 113 turtles,use GPS for position correction and eliminate 50%of telemetry data.Comparing the upcasts and downcasts of original data of sea turtles,the advantage of using upcasts data as telemetry data is proved,and the accuracy of the telemetry data in ocean temperature-depth simulation is demonstrated.The time for collecting turtle data is analyzed for three months and the average collection frequency is 1.12 times/day,and dive most of the time to the bottom of the ocean.2)The data of fishboats were introduced,the temperature and depth of the data of the fishiboats were compared with the profile data of the turtles.The average temperature difference between them was only 0.45?,and the temperature difference did not fluctuate.The accuracy of the sea turtle telemetry data recorded in the ocean temperature depth was verified.Comparison of the temperature difference between sea turtles and fishboats on the time-series sequence.When studying the temperature of the entire section,space-time has an effect on the temperature difference,but in the seabed,space is the main cause of the temperature difference,indicating that the fishing boat validates turtle data profile data at different distance scales and The influence of different time scales on the temperature difference.3)The temperature-depth profile data of sea turtles,fishboats and ROMS models were compared.It was pointed out that the deviation of ROMS model simulation was larger in the range of 10?35 meters underwater and the correlation coefficient between sea turtles and fishing boats was 0.751,which was related to ROMS.The coefficient is 0.589.Before and after the analysis of the storm ROMS model and turtle data,the turtle and model data in the range of 10 meters underwater fell after the storm,but in the range of 10 to 30 meters underwater,most of the temperature increases after the storm,but the model occasionally has the opposite record,and it is concluded that the ROMS model is not as good as the calmness of the sea.In the analysis of the Isothermal layer depth(ILD),the ILD of the turtle profile data is stable at 5 to 10 meters,while the ILD of the ROMS fluctuates between 10 and 25 meters,which is deeper than the Isothermal layer depth of the turtle.These descriptions apply the turtle's record data to the ROMS model's feasibility and provide a method and reference for the model's data sources and validation.