Takeshi Iinuma

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.

Kinematic coupling models inverted from geodetic data are widely used to evaluate how slip deficit is distributed along subduction megathrusts during the interseismic period, and are central to earthquake and tsunami hazard assessment. Yet, existing coupling models differ widely in methodology and inputs, lack common community standards, and are scattered across publications and repositories. Here, we introduce the "Coupling Cloud" (https://couplingcloud.ucsd.edu), an open, extensible, community-driven platform that curates, standardizes, documents, and disseminates more than 96 kinematic coupling models from 55 publications across 21 subduction margins. The platform provides interactive 2D and 3D plate-interface viewers to inspect coupling models together with associated information such as slab geometry, uncertainty estimates and metadata. All datasets can be downloaded directly in standardized formats: surface-projected coupling values as NetCDF, plate-interface dislocation geometries as VTU, and model metadata as YAML files. We demonstrate the advantages of centralized and standardized coupling data through a Cascadia subduction zone example, where synthesizing eight full-margin models reveals along-strike patterns that are not apparent when models are examined individually. Consolidating coupling models within a coherent, version-controlled framework enables systematic cross-margin comparison and FAIR-compliant data sharing, opening the door to more comprehensive assessment of megathrust mechanics.

Kinematic coupling models inverted from geodetic data are widely used to evaluate how slip deficit is distributed along subduction megathrusts during the interseismic period, and are central to earthquake and tsunami hazard assessment. Yet, existing coupling models differ widely in methodology and inputs, lack common community standards, and are scattered across publications and repositories. Here, we introduce the “Coupling Cloud” (https://couplingcloud.ucsd.edu), an open, extensible, community-driven platform that curates, standardizes, documents, and disseminates more than 95 kinematic coupling models from 56 publications across 21 subduction margins. The platform provides interactive 2D and 3D plate-interface viewers to inspect coupling models together with associated information such as slab geometry, uncertainty estimates and metadata. All datasets can be downloaded directly in standardized formats: surface-projected coupling values as NetCDF, plate-interface dislocation geometries as VTU, and model metadata as YAML files. We demonstrate the advantages of centralized and standardized coupling data through a Cascadia subduction zone example, where synthesizing eight full-margin models reveals along-strike patterns that are not apparent when models are examined individually. Consolidating coupling models within a coherent, version-controlled framework enables systematic cross-margin comparison and FAIR-compliant data sharing, opening the door to more comprehensive assessment of megathrust mechanics.

Numerous high-resolution seismological and magnetotelluric observations depict a sharp and distinct Lithosphere-Asthenosphere Boundary (LAB) at the base of oceanic lithosphere, in some cases beneath the subducting slab. Many lines of evidence indicate ponding of partial melts at the LAB. A melt-rich oceanic LAB is expected to have a low viscosity to affect plate motion, subduction, and earthquake deformation. Therefore, it is important to seek direct geodetic evidence for the rheological weakness of the LAB and its effects on deformation. Here we summarize our recent progress in finding the evidence. (1) Immediately after several recent large subduction earthquakes (e.g., the 2011 Mw=9 Tohoku-oki and the 2010 Mw=8.8 Maule) in the Japan-Kuril and Chile subduction zones, GNSS observations show enhanced landward motion (ELM) of coastal areas 100s of km outside the rupture area. Using 3-D viscoelastic finite element models, we explained the postseismic ELM in terms of mechanical decoupling of the subducting slab from the underlying asthenosphere due to a low-viscosity LAB (Sun et al., 2024). The ELM observation is thus considered the first geodetic evidence for a weak LAB beneath subducting oceanic lithosphere. Assuming a thickness of no more than 10 km for the LAB, key characteristics of the observed ELM can be explained to first order by an LAB viscosity of no more than 5e16 Pa s, lower than typical mantle viscosities by 2-3 orders of magnitude. (2) In a more detailed investigation of the postseismic deformation following the 2011 Tohoku-oki earthquake, constrained by extraordinarily dense onshore and offshore (seafloor GNSS/Acoustic) geodetic measurements, we find that both near-field deformation and the more distant ELM can be optimally explained by having a thin (~5 km) and low-viscosity (~5e16 Pa s) LAB down to a depth of ~120-150 km. Our geodesy-based research adds a new dimension to the geophysical studies of the LAB and contributes to understanding the origin, spatial distribution, and consequence of the ponded partial melts.Sun, T., Wang, K., & He, J. (2024). Geodetic signature of a weak lithosphere-asthenosphere boundary in postseismic deformation of large subduction earthquakes. Earth and Planetary Science Letters, 630, 118619, https://doi.org/10.1016/j.epsl.2024.118619

JAMSTEC have been monitoring changes in underground fluid pressure, or "pore pressure," from boreholes near the site of the 1944 Tonankai earthquake in southwestern Japan. These changes are linked to Slow Slip Events (SSEs), which occur on the boundary between the Eurasian plate and the subducting Philippine Sea plate beneath the Nankai Trough. By connecting their borehole observatory (LTBMS) to a seafloor monitoring network (DONET), they now collect real-time pore pressure data, allowing them to update their SSE catalog.This updated catalog revealed something unusual: the SSE in February 2012 lasted significantly longer than similar events. Researchers studied pore pressure and seafloor pressure data to understand why. We found that the February SSE moved more slowly and lasted longer because of two key factors: internal and external forces.Internally, the SSE occurred in a region where little stress had built up on the fault, causing it to slip more slowly, consistent with frictional behavior on faults. Externally, we found that changes in seafloor pressure, driven by shifts in the Kuroshio Current (a major ocean current), coincided with the end of the February SSE. This suggests that the Kuroshio Current's meander may influence the duration of SSEs.Our study highlights that SSEs are not only shaped by fault interactions but also by environmental factors like ocean currents and atmospheric pressure. Understanding these influences is key to better predicting such events. These findings are based on a paper accepted by Tectonophysics (https://doi.org/10.1016/j.tecto.2024.230439), and we plans to share additional insights and recent practical analysis in our presentation.

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.

The Nankai subduction zone presents significant seismic and tsunami risks, given its historical earthquakes exceeding magnitude 8 and the expectations of similar future events. Slow earthquakes, common at the shallow and deep plate interface, result from different frictional properties linked to interplate slip deficit accumulation. This study estimates slip deficit rates at the Nankai subduction zone using land and ocean‐bottom geodetic data. Previous estimates encountered limitations, often smoothing slip deficits, omitting observational error differences between ocean‐floor and land data, and relying on homogeneous structure models. To address these issues, we employ a novel trans‐dimensional reversible jump Markov Chain Monte Carlo algorithm. This approach dynamically adjusts slip parameters, accommodating data resolution and producing a flexible slip distribution without predetermined spatial constraints. Additionally, it automatically weights data for observational errors and integrates elastic Green functions from a 3D structure of the Nankai region. Our results provide a finer, heterogeneous slip distribution, improving estimates in inland regions. However, limitations remain offshore in areas with sparse data. We revised the spatial distribution of Nankai slow earthquakes and confirmed a good agreement with intermediate slip deficit rates, identifying coupled and uncoupled regions. High slip deficit rates align with rupture areas of historic large earthquakes. Slow earthquakes occur at frictionally weak plate interfaces, and shallow slow earthquakes may result from subducting relief heterogeneities with important pore fluid pressure effects. We introduce an updated distribution of slip deficit rates for the Nankai subduction zone, considering observed slip deficit rates and the fast and slow earthquake occurrence.

Monitoring and predicting fault slip behaviors in subduction zones are essential for understanding earthquake cycles and assessing future earthquake potential. We developed a data assimilation (DA) method for fault slip monitoring and short-term prediction of slow slip events (SSEs), which was applied to the 2010 Bungo Channel SSE in southwest Japan. The observed geodetic data were quantitatively explained using a physics-based model with DA. We investigated short-term predictability by assimilating observation data with limited periods. Without prior constraint on fault slip style, observations solely during slip acceleration predicted the occurrence of a fast slip; however, the inclusion of slip deceleration data successfully predicted a slow transient slip. With prior constraint to exclude unstable slip, the assimilation of data after the SSE occurrence predicted a slow transient slip. This study provided a tool using DA for fault slip monitoring and prediction based on real observation data.