Global Navigation Satellite System—Acoustic (GNSS-A) positioning is the main technique for seafloor geodetic positioning. A transceiver lever arm offset and sound velocity bias in seawater are the main systematic errors of the GNSS-A positioning technique. Based on data from a sea trial in shallow water, this paper studies the functional model of GNSS-A positioning. The impact of the two systematic errors on seafloor positioning is analysed and corresponding processing methods are proposed. The results show that the offset in the lever arm measurement should be parameterised in the observation equation. Given the high correlation between the vertical lever arm offset and the vertical coordinate of the seafloor station, a sample search method was introduced to fix the vertical offset correction. If the calibration of the sound velocity profiler cannot be ensured, the correction parameter of the sound velocity bias should be solved. According to the refined functional model and corrections, the position of a seafloor station in shallow water can be determined with a precision of better than 1 cm.

SUMMARY: In traditional tight integration of high-rate GNSS and strong motion sensors, an appropriate process variance is crucial for obtaining accurate broad-band coseismic deformations. In this paper, instead of using a subjectively empirical value, we present an approach for determining the process variance adaptively based on the adaptive Kalman filter for real-time use. The performance of the approach was validated by the colocated stations collected during the 2010 Mw 7.2 earthquake in El-Mayor, 2016 Mw 7.8 earthquake in New Zealand and 2016 Mw 6.5 earthquake in central Italy. The results show that this method complements the advantages of GNSS and strong motion accelerometers and can provide more accurate coseismic waveforms especially during the strong shaking period, due to the ability of the method to adjust the process variance in real time according to the actual status of the station. In addition, this method is also free from the influence of the baseline shift. Testing of the new method for the integration of strong motion and multi-GNSS indicates that multi-GNSS has an obvious improvement in the precision while single GPS has a poor observation condition.

The construction of underwater geodetic networks (UGN) is crucial in marine geodesy. To provide high-precision kinematic positioning for underwater submersibles, an underwater acoustic geodetic network configuration of three seafloor base stations, one subsurface buoy, and one sea surface buoy is proposed. The simulation results show that, for a 3 km-deep sea, based on the proposed UGN, the submersible positioning range and positioning accuracy are primarily affected by the size of the seafloor base station array, while the height of the subsurface buoy has a greater impact on the submersible positioning accuracy than the positioning range. Considering current acoustic ranging technology, the kinematic positioning performance of the UGN is optimal when the seafloor base stations are 9~13 km apart and the subsurface buoy is less than 2.5 km above the seafloor, which can achieve a submersible positioning accuracy of less than 30 m within an underwater space of 25 km × 25 km × 3 km. The proposed cost-effective UGN configuration can provide high-precision submersible kinematic positioning performance for seafloor surveying and ocean precision engineering. The impact of the underwater environment on the acoustic transmission characteristics should be further investigated.

Global Navigation Satellite System–Acoustic ranging (GNSS-A) technology can achieve centimeter-level seafloor positioning. However, the horizontal gradient of ocean sound speed limits the seafloor positioning accuracy of GNSS-A. This paper evaluates the impact of ocean sound speed horizontal gradients on GNSS-A seafloor positioning utilizing Bayesian estimation. Publicly available GNSS-A datasets from 2012 to 2021 were processed using strategies with and without estimating sound speed horizontal gradients. The comparison of results demonstrates that the ocean sound speed horizontal gradient has a significant impact on horizontal positioning but a smaller impact on vertical positioning. The mean root mean square (RMS) of horizontal positioning differences for both strategies is 0.12 m, with a maximum of 0.19 m. The mean RMS of vertical positioning differences is 0.014 m, with a maximum of 0.021 m. The mean RMS of station velocity differences is 0.004 m/a and 0.008 m/a in the east and the north components, respectively, with a maximum RMS of 0.01 m/a in the horizontal component. The vertical station velocity differences for both strategies are relatively small, with a mean RMS of 0.002 m/a and a maximum RMS of 0.003 m/a. The mean RMS difference in sound speed correction for both strategies is 0.01 m/s. The sound speed horizontal gradient is larger in the shallow portion than in the deep portion. In the shallow portion, the mean RMS is 0.052 m/s/km and 0.072 m/s/km in the east and north component, respectively. In the deep portion, the mean RMS is 0.023 m/s/km and 0.024 m/s/km in the east and north components, respectively. The sound speed horizontal gradient varies significantly at different locations due to the marine environment discrepancies, which require refined GNSS-A processing to improve seafloor positioning accuracy.

Integration of high-rate Global Navigation Satellite Systems (GNSS) with strong motion (SM) sensors enables accurate broadband coseismic displacements, which are critical for earthquake early warning and rapid source inversion. However, GNSS colored noise and SM baseline shift can degrade the accuracy and stability of the integrated displacements. In this study, we propose a novel loose integration approach where a two-step Kalman filter (KF) is used. In the first step, the high-rate GNSS displacements without colored noise are estimated using an adaptive KF that parameterizes the colored noise. Then, the denoised high-rate GNSS displacements are integrated with SM in the second KF where the baseline shift in SM is parameterized as a random walk process. The effectiveness of the proposed method was validated with co-located high-rate GNSS and strong motion data collected from a shake table experiment, the 2010 Mw 7.2 El Mayor-Cucapah earthquake, the 2016 Mw 7.8 Kaikōura earthquake, and the 2019 Mw 7.1 Ridgecrest earthquake. The results show that the proposed method achieves an RMSE of 1.1 mm, a 21% improvement over the KFb solution when shake table recordings are used as the reference. Application to three real earthquake cases demonstrates that the method effectively mitigates low-frequency GNSS noise and SM baseline shift, resulting in more accurate and stable coseismic displacement estimates.