Explore the vast range of titles available.

The 22 July 2020 Mw7.8 Simeonof earthquake was a deep megathrust event that ruptured along the Shumagin segment of the Alaska‐Aleutian subduction zone. This earthquake occurred ∼250 km from a seafloor geodetic GNSS‐Acoustic site IVB1, where we observed a velocity of 3.78 ± 1.15 cm/yr with the down‐going slab prior to the earthquake followed by 0.6 ± 0.7 eastward and −15.5 ± 0.8 cm northward coseismic offset. We computed a slip model of the coseismic rupture using the static offset at IVB1 alongside regional continuous GNSS and strong motion stations. The small static horizontal offset at the site precludes significantly shallower rupture than previously inferred from terrestrial observations, confirming that the Simeonof earthquake was a deep megathrust earthquake. The observed site velocity implies partial locking prior to the earthquake, implying significant shallow strain accumulation such that the small coseismic offset is unlikely to have relieved all of the accumulated strain since the last coseismic rupture. Plain Language Summary The 22 July 2020 Mw7.8 Simeonof earthquake ruptured east of the Shumagin Islands along the interface of the Alaska‐Aluetian subduction zone, where the Pacific plate subducts beneath the North American plate. This earthquake occurred ∼250 km from a seafloor geodetic GNSS‐Acoustic site IVB1, which acts as a seafloor GPS station and provides the only direct offshore measurement of seafloor deformation following the earthquake. In late September 2020, we observed 0.6 ± 0.7 of eastward offset and −15.5 ± 0.8 cm of northward offset at the station IVB1 and used this measurement to refine a model of the earthquake rupture area that previously only used the GPS and seismic stations installed on the nearby Aleutian Islands. The seafloor offset was not large enough to require additional rupture to the south of where previous studies have imaged the earthquake, confirming that the Simeonof earthquake was a deep megathrust earthquake. Prior to the earthquake, station IVB1 was moving with the down‐going Pacific plate with a velocity of 3.78 ± 1.15 cm/yr despite resting on the North American plate, implying that the two plates are partially stuck together. We expect that there could still be another future earthquake beneath IVB1 since the offset at IVB1 following the Simeonof earthquake was smaller than expected. Key Points We observed seafloor offset at a GNSS‐Acoustic site along the Alaska subduction zone following the 22 July 2020 Mw7.8 Simeonof earthquake Additional slip updip of the main earthquake rupture area is not required to generate the observed seafloor offset The shallow plate interface was partially locked prior to the Simeonof earthquake and may still host unrelieved strain

The accuracy of GNSS/Acoustic seafloor geodetic calibration is greatly influenced by the temporal variation of sound velocity, especially in the vertical direction. Aiming at correcting of the unknown parameters related to both the positions and the sound velocity, this paper proposes a step-by-step inversion scheme based on moving survey data. The proposed method firstly estimates the horizontal normalized travel time delay with sound ray tracing strategy and then computes the horizontal position with circle line observations. We reconstructed an inversion scheme for extracting the surface sound velocity disturbance (SSVD) and corrected the vertical position from cross line data. The SSVD is decomposed into a sum of different period disturbances, and a new SSVD is reconstructed by combining the long period disturbance and short period disturbance. The proposed algorithm is verified by the South China Sea experiment for GNSS/Acoustic seafloor geodetic calibration. The results demonstrate that the new method can take the effects of sound velocity variation into consideration and improve the precision of the vertical position, which is superior to the least squares (LS), the single-difference LS for seafloor geodetic calibration.

Seafloor real-time positioning is important for the instantaneous detection of seafloor crustal motion, seismic activities, hydrological rapid variations and rapid geodetic datum updates. Current GNSS-Acoustic (GNSS-A) seafloor positioning usually utilizes batch processing of long-term observations in the postprocessing mode. Seafloor real-time positioning can be achieved using sequential processing of the epoch-wise observations. We propose the sequential GNSS-A seafloor point positioning method with modeling of the sound speed variation in the kinematic survey. We propose real-time modeling of the sound speed variation using oceanography analysis data and we then calculate the random walk (RW) process noise of the acoustic nadir total delay (NTD) caused by the sound speed variation. The experiments conducted in the South China Sea and Japan Trench validate the method performance in terms of epoch-wise positioning accuracy, high-resolution sound speed variation, and filter convergence time. The difference between the estimated sound speed and the in situ sound velocity profiles was 0.128 m/s root mean square. The vessel track of the line and circle combination performs best with a high positioning accuracy and a short convergence time. The position in real-time sequential processing with the modeled NTD RW process noise converged to a 3D range of 0.125 m from the static post-determined position. The a posteriori residual of the acoustic travel time observations was equivalently 0.270 m in range. These findings can improve the temporal resolution of the GNSS-A positioning and oceanography.

It is important to appropriately model underwater sound speed structures to detect seafloor displacements accurately using GNSS-acoustic seafloor geodetic observations. In recent years, various sea surface platforms (e.g., wave gliders) have been developed for GNSS-acoustic observations. Sub-mesoscale oceanic phenomena can be detected by simultaneously employing multiple sea surface platforms. However, the use of a single sea surface platform with slow navigation speeds may degrade the modeling accuracy of underwater sound speed structures, even when compared to conventional ship-based observations. Therefore, the development of a GNSS-acoustic positioning technique that expresses a complex underwater sound speed structure and simultaneously provides constraints on sound speed parameters, if necessary. This study arranges the observation equation by considering multiple-layered sound speed gradients and develops a GNSS-acoustic positioning scheme using a Bayesian framework. The performance of the proposed GNSS-acoustic positioning method was investigated using synthetic datasets. The proposed method successfully modeled a complex underwater sound speed structure (e.g., temporal variations in sound speed gradients) using a dataset collected by dual sea surface platforms, which is highly sensitive to the underwater sound speed structure. It also provides robust solutions, even for a dataset with low sensitivity, by appropriately introducing constraints on the sound speed parameters. Moreover, the proposed method was applicable to an actual observational dataset, and it was confirmed that the GNSS-acoustic positioning method under special conditions (assumption of a temporally constant single-layered sound speed gradient) in a previous study can be reproduced by the constraints in the proposed method. Thus, the proposed method enabled us to flexibly model the underwater sound speed structure and accurately detect seafloor displacements for various types of observation datasets. The proposed method was implemented in the open-source GNSS-acoustic positioning software “SeaGap.” Graphical abstract

Global navigation satellite system-acoustic (GNSS-A) positioning is an important geodetic observation technique for detecting seafloor crustal deformation. After the 2011 Tohoku–Oki earthquake, GNSS-A observational networks were extended along various subduction zones, and observational systems have been improved, especially for sea surface platforms, such as the introduction of an unmanned vehicle, the Wave Glider. The aforementioned development of GNSS-A observations has provided a large amount of observational data. Furthermore, GNSS-A positioning methods were recently developed considering the lateral heterogeneity of the sound speed structure. Thus, it is important to develop a software that makes it easy for widespread use of the latest GNSS-A positioning methods. However, there is currently only one open-source GNSS-A positioning software, which may hinder the entry of various researchers into GNSS-A positioning analyses. Here, we developed a new GNSS-A positioning software, henceforth called “SeaGap” (Software of enhanced analyses for GNSS-acoustic positioning), that executes various positioning methods from the conventional kinematic positioning technique to the latest Markov Chain Monte Carlo (MCMC)-based static positioning technique. We introduce their methodology and demonstrate its application to actual observational data. The software newly added optional prior distributions to the unknown parameters expressing the heterogeneity of a sound speed structure into the MCMC-based static positioning method, and we also applied the new method to actual observational data. In addition to the positioning functions, the software contains various auxiliary functions, including drawing. The developed software is written using the “Julia” language and is distributed as an open-source software. Graphical Abstract

The Global Navigation Satellite System–Acoustic (GNSS-A) combined positioning technique extends geodetic networks into the seafloor. Currently, GNSS-A can achieve static seafloor positioning accuracy at centimeter level. However, in practical operations, substantial time, manpower, financial and instrument resources are required to measure in situ Sound Speed Profiles (SSPs). This paper evaluates the feasibility of GNSS-A with alternative SSPs instead of in situ measurements. The GNSS-A positioning using three different SSPs are compared: the Munk empirical profile, the profiles from the HYbrid Coordinate Ocean Model (HYCOM) global ocean analysis product, and the in situ profiles. Compared with the in situ profile, the Munk SSP has little impact on the GNSS-A horizontal position (0.6 cm in root-mean-square, RMS) but introduces a large systematic error in the vertical position (10.3 cm in RMS), and the impact on the displacement velocity is at the mm/a level. When the HYCOM profile is substituted for in situ profiles, the impact on GNSS-A positioning is only 0.2 cm in the horizontal and 2.9 cm in the vertical, and the impact on displacement velocity is at the sub-mm/a level in the horizontal and mm/a level in the vertical. The HYCOM global ocean analysis SSPs can largely serve as a cost-effective substitute for in situ profiles in GNSS-A seafloor positioning, which is especially applicable to GNSS-A measurements using unmanned surface vehicles, for which full-depth SSP measurements are difficult. Therefore, when SSPs are selected, appropriate decisions should be made on the basis of specific GNSS-A application needs and conditions.

The Global Navigation Satellite System combined with acoustic technique has achieved great economic benefits in positioning of ocean bottom seismometers, with hundreds of underwater transponders attached to seismometers typically being deployed during oil exploration. The previous single transponder positioning method ignored the similar underwater environments between the transponders. Due to the refraction effect of sound, the technique usually showed poor positioning accuracy in shallow water when the incidence angles are large. In this paper, the effect of sound ray bending is analyzed based on the sound ray tracing method in shallow water, and a new piecewise incidence angle model is proposed to improve the positioning accuracy of multiple objects in order to estimate the sound ray bending correction. The parameters of the new model are divided into groups and estimated by sequential least squares method, together with all of the transponders. The observability analysis is discussed in simulation and testing experiments in the South China Sea. The results show that the newly proposed method is able to make full use of the acoustic observation data of hundreds of transponders to accurately estimate the SRB correction, which could also significantly improve the positioning accuracy of multiple transponders.

Monitoring deformation on the seafloor is a major challenge for modern geodesy and a key to better understanding tectonic processes and assess related hazards. The extension of the geodetic networks offshore can be achieved by combining satellite positioning (GNSS) of a surface platform with acoustic ranging to seafloor transponders. This approach is called GNSS-Acoustic (GNSS-A). The scope of this work is to provide a tool to identify and quantify key points in the error budget of such experiment. For this purpose, we present a least-squares inversion method to determine the absolute position of a seafloor transponder array. Assuming the surface platform is accurately positioned by GNSS, the main observables are the two-way travel time in water between the transponders on the seafloor and the surface platform acoustic head. To better constrain transponder positions, we also consider the baseline lengths and the relative depth-differences between different pairs of them. We illustrate the usefulness of our forward modeling approach and least-square inversion by simulating different experimental protocols (i.e., platform trajectories, with or without information on the distance and depth between transponders). We find that the overall accuracy of a GNSS-A experiment is significantly improved with additional information about the relative depths of the instruments. Baseline lengths also improve the accuracy, but only when combined with depth differences. The codes in Python3 used in this article are freely available online.

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.

Traditional Global Navigation Satellite System-Acoustic (GNSS-A) positioning assumes the Layered Model in the sound speed structure, and any of horizontal perturbation of seawater degrades its accuracy. However, the use of the Gradient Model analytically demonstrated that the horizontal gradient of the sound speed structure and displacement can simultaneously be solved using multiple transponders for each of ping. We applied this technique to our observed data and found it unsuitable for real data. We confirmed that a horizontal perturbation with wavelength shorter than the horizontal extent of the transponder array significantly violates the linear approximation in the Gradient Model. Our vertical 2D numerical simulation of internal waves (IWs) forced by tidal oscillation showed that such small-scale IWs could effectively be generated by nonlinear cascade from large-scale IWs of the major tidal constituents. In addition, a small-scale IW in deep water typically has a period of 3–4 h, which degrades positioning accuracy significantly, whereas an IW of much shorter period in shallow water has less effect after removal of the fluctuation by time averaging within a typical observation period. Apparent array position obtained in the synthetic test based on the simulated IW-derived sound speed structure showed features quite similar to that observed in real surveys. To incorporate such deeper perturbation, we proposed a Disturbance Model using dual sea surface platforms, that can solve time-varying perturbation in the vicinity of each transponder.