A large buoy-based radioactivity monitoring system for gamma-ray emitters in surface seawater

Study on reconstruction and analytical method of seawater radioactive gamma spectrum

Gamma detector detection technology based on NaI(Tl) scintillation crystal has become a popular research topic and has been applied in the field of marine radioactive environment automatic monitoring because of its advantages of low power consumption, low cost and strong environmental adaptability. However, insufficient energy resolution of the NaI(Tl) detector and great Compton scattering in the low-energy region caused by the abundance of natural radionuclides in seawater hinder the automatic analysis of radionuclides in seawater. This study adopts the combination of theoretical derivation, simulation experiment, water tank test and seawater field test, establishing an effective and feasible spectrum reconstruction method. The measured spectrum in seawater is regarded as the output signal formed by the convolution of the incident spectrum and the detector response function. The acceleration factor p is introduced to construct the Boosted-WNNLS deconvolution algorithm, which is used to iteratively reconstruct the spectrum. The analysis results of the simulation test, water tank test and field test meet the radionuclide analysis speed and accuracy requirements for the in-situ automatic monitoring of seawater radioactivity. The spectrum reconstruction method in this study converts the physical problem of insufficient detection accuracy of spectrometer in the practical application into a mathematical problem of deconvolution solution, restores the original radiation information in seawater, and improves the resolution of the seawater gamma spectrum.

Comparative study on gamma-ray detectors for in-situ ocean radiation monitoring system

Large-sized crystals and state-of-the-art photosensors are desirable to cope with low environmental radioactivity (e.g., 1-2 Bq∙m^-3137Cs in surface seawater) for homeland security purposes. We compared the performances of two different gamma-ray detector assemblies, GAGG crystal + silicon photomultiplier (SiPM) and NaI(Tl) crystal + photomultiplier tube, for our mobile in-situ ocean radiation monitoring system. We performed energy calibration, followed by water tank experiments with varying the depth of a¹³⁷Cs point source. Experimental energy spectra were compared with MCNP-simulated spectra with identical setup and the consistency was validated. We finally assessed the detection efficiency and minimum detectable activity (MDA) of the detectors. Both GAGG and NaI detectors exhibited favorable energy resolutions (7.98 ± 0.13% and 7.01 ± 0.58% at 662 keV, respectively) and MDAs (33.1 ± 0.0645 and 13.5 ± 0.0327 Bq∙m^-3 for 24-h ¹³⁷Cs measurement, respectively). Matching the geometry of the GAGG crystal with that of the NaI crystal, the GAGG detector outperformed the NaI detector. The results demonstrated that the GAGG detector is potentially advantageous over the NaI detector in detection efficiency and compactness.

A Test Method for Obstacle-Avoidance Performance of Unmanned Surface Vehicles Based on Mobile-Buoy–Shore Multisource-Sensing-Data Fusion

In order to avoid the influence of the test system itself on the autonomous navigation and performance test accuracy of unmanned surface vehicles (USVs), a test method for the obstacle-avoidance performance of USVs based on mobile-buoy–shore multisource-sensing-data fusion is proposed. In this method, a mobile-buoy-integrated test system is designed (that is, the test instrument is installed on the mobile buoy). The buoy is both the carrier of the test instrument and the obstacle. The software and hardware functions of the test system are realized in modules, and the obstacle-avoidance monitoring function of the USV is realized by the trajectory-tracking method of buoy perception preprocessing and shore adaptive weighted fusion. Then, on the basis of the mobile-buoy–shore sensing-data-fusion method, performance tests and a quantitative evaluation of the obstacle perception, static-obstacle avoidance, and dynamic-obstacle avoidance of the USV were carried out. The results show that: (1) the tested USV can accurately identify the distance between buoys; (2) the three static-obstacle-avoidance performance scores of the single obstacle, continuous obstacle, and inflection-point obstacle are 74.81, 77.14, and 47.61, respectively, and the quantitative evaluation score of the static-obstacle-avoidance comprehensive performance is 66.4; (3) the obstacle-avoidance-performance scores of overtaking, encounter, and cross encounter are about 53.92, 36.51, and 6.48, respectively, and the quantitative evaluation score of the comprehensive performance of the dynamic-obstacle avoidance is 72.36. The above quantitative evaluation results show that the system can: participate in track intervention and obstacle-avoidance monitoring as an obstacle; give the static- and dynamic-obstacle-avoidance quantitative evaluation results in a predetermined way, which verifies the feasibility and effectiveness of the obstacle-avoidance-performance test system of the USV on the basis of mobile-buoy–shore multisource-sensing fusion; and be used for the testing and evaluation of the obstacle-avoidance performance of USVs.