Fumiaki Tomita

How and when the subduction megathrust recovers its locking state after a great earthquake is important to understanding earthquake-cycle physics and future hazards. Here we report the delineation of the relocking process after the 2011 magnitude 9 Tohoku earthquake with 14 years of postseismic seafloor geodetic observations. These data, combined with onshore geodetic observations, analyses of repeating earthquakes, and numerical modeling, reveal how the recovery of locking is interrupted by afterslip on the megathrust and accompanied by stress relaxation in the viscoelastic upper mantle. The results indicate that the megathrust has already arrived at a locking state today similar to that before the earthquake despite the ongoing stress relaxation. The acquired knowledge aids understanding of megathrust earthquake cycles at other subduction zones.

An Mw 8.8 megathrust earthquake occurred off the coast of the Kamchatka Peninsula on July 29, 2025 (UTC). This event was located in the same southern segment of the Kamchatka Trench as the 1952 Mw 9.0 Kamchatka earthquake, marking the occurrence of two major earthquakes in this segment with a remarkably short interval of 73 years. Understanding whether the 1952 and 2025 events originated from the same asperities or whether their ruptures were spatially complementary is critical for understanding the generation processes of great earthquakes in subduction zones. The 1952 earthquake is thought to have involved slips exceeding 10 m and reaching depths of 60 km, whereas the main rupture of the 2025 event is thought to have occurred at a shallower depth of approximately 30 km. However, accurately determining the rupture depth of an older event such as the 1952 earthquake is challenging. To provide additional information regarding the rupture depth of the 1952 event, this study investigated whether the depth (< 60 km) at the plate interface along the Kamchatka Trench is capable of accumulating sufficient slip deficit to generate a great earthquake. Using horizontal interseismic displacement rates from global navigational satellite system (GNSS) stations, we estimated the slip deficit rate distribution in the Kamchatka subduction zone using a reversible-jump Markov-chain Monte Carlo (RJ-MCMC) method with a prior distribution that promotes modeling sparsity. The results demonstrated that strong coupling existed in the southern segment at depths of 15–60 km, suggesting that it is possible for great earthquakes to occur in the deep part of the plate interface. To clarify the relationship between the 1952 and 2025 events further, a more detailed analysis of the coseismic slip and afterslip distributions of the 2025 earthquake is necessary. Graphical Abstract

Global Navigation Satellite System‐Acoustic (GNSS‐A) seafloor geodetic observation is an effective technique not only for measuring offshore crustal deformation but also for detecting underwater sound speed structures. Collecting acoustic range data along survey tracks with broad spatial coverage is typically essential to detect horizontal gradients of the sound speed structure. Although acoustic ranging data sets with broad spatial coverage have been acquired using research vessels, the acquisition of such data sets has often been difficult using an unmanned sea surface platform, such as a Wave Glider. To ensure positioning accuracy for insufficient acoustic ranging data sets, constraining the horizontal gradients of the sound speed structure using independent information is essential. In this study, we examined the applicability of ocean models to improve GNSS‐A positioning accuracy for insufficient acoustic ranging data sets. We calculated the sound speed parameters expressing the horizontal gradients of the sound speed structure using the ocean models JCOPE2M and MOVE/MRI.COM, and compared them with those estimated by GNSS‐A using actual acoustic ranging data sets with broad spatial coverage. The results illustrated that these ocean models have the potential to improve the positioning accuracy when large‐scale horizontal inhomogeneity exists in the sound speed structure (e.g., an oceanic current). However, the GNSS‐A analysis results using actual data indicate a significant influence of small‐scale horizontal inhomogeneities, suggesting that higher‐resolution ocean models are required to further improve positioning accuracy.

Global Navigation Satellite System-Acoustic (GNSS-A) seafloor geodetic observation is an effective technique not only for measuring offshore crustal deformation but also for detecting underwater sound speed structures. Collecting acoustic range data along survey tracks with fine spatial coverage is typically essential to detect horizontal gradients of the sound speed structure. Although fine geometric acoustic ranging datasets have been acquired using research vessels, the acquisition of such datasets has often been difficult using an unmanned sea surface platform, such as a Wave Glider. To ensure positioning accuracy for insufficient acoustic ranging datasets, constraining the horizontal gradients of the sound speed structure using independent information is essential. In this study, we examined the applicability of ocean models to improve GNSS-A positioning accuracy for insufficient acoustic ranging datasets. We calculated the sound speed parameters expressing the horizontal gradients of the sound speed structure using the ocean models JCOPE2M and MOVE/MRI.COM, and compared them with those estimated by GNSS-A using actual fine geometric acoustic ranging datasets. The results illustrated that these ocean models have the potential to improve the positioning accuracy when large-scale horizontal inhomogeneity exists in the sound speed structure (e.g., an oceanic current). However, the GNSS-A analysis results using actual data indicate a significant influence of small-scale horizontal inhomogeneities, suggesting that higher-resolution ocean models are required to further improve positioning accuracy.

The precise detection of seafloor crustal deformation is important for understanding various tectonic phenomena, especially the deformation process associated with a seismic cycle. Global Navigation Satellite System–Acoustic (GNSS-A) positioning enables the measurement of horizontal and vertical seafloor deformations. However, vertical motion detected by GNSS-A positioning often has various systematic biases; therefore, the positioning error of vertical motion is typically worse than that of horizontal motion. In this study, we examined one of the systematic bias sources originating from the uncertainty of the reference sound speed profile (SSP) employed in GNSS-A positioning. Using direct oceanographic measurement data, we addressed the uncertainties of using a reference SSP in both cases, where the reference SSP was obtained from direct measurement and the oceanographic data assimilation model (JCOPE2M). We then examined the vertical GNSS-A positioning errors in both cases using the Monte Carlo method: ~2 and ~6 cm in the former and latter cases, respectively. Moreover, we optimized the vertical GNSS-A positioning errors by considering the uncertainty of a reference SSP and other error sources. The optimized vertical GNSS-A positioning errors successfully explain offsets of the campaign solutions deviated from a long-term displacement trend compared with the conventional error evaluations. Although this study focused on the uncertainty of a reference SSP, it is important to carefully evaluate various other systematic error sources to precisely detect vertical seafloor motion.

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

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.”

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

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.