Citizen seismology helps decipher the 2021 Haiti earthquake

5 pages, 4 figures, supplementary materials https://doi.org/10.1126/science.abn1045.-- Data and materials availability: All data and code used in this study are openly available. RADAR data can be obtained through ESA (Sentinel) or JAXA (Alos-2). Aftershock data can be obtained from https://ayiti.unice.fr/ayiti-seismes/ (7). The codes used to process or model the data are published and public (8). The catalog of high-precision earthquake relocated with the NLL-SSST-coherence procedure (SM4) is available as supplementary dataOn 14 August 2021, the moment magnitude (Mw) 7.2 Nippes earthquake in Haiti occurred within the same fault zone as its devastating 2010 Mw 7.0 predecessor, but struck the country when field access was limited by insecurity and conventional seismometers from the national network were inoperative. A network of citizen seismometers installed in 2019 provided near-field data critical to rapidly understand the mechanism of the mainshock and monitor its aftershock sequence. Their real-time data defined two aftershock clusters that coincide with two areas of coseismic slip derived from inversions of conventional seismological and geodetic data. Machine learning applied to data from the citizen seismometer closest to the mainshock allows us to forecast aftershocks as accurately as with the network-derived catalog. This shows the utility of citizen science contributing to our understanding of a major earthquakeThis work was supported by the Centre National de la Recherche Scientifique (CNRS) and the Institut de Recherche pour le Développement (IRD) through their “Natural Hazard” program (E.C., S.S., T.M., B.D., F.C., J.P.A., J.C., A.D., D.B., S.P.); the FEDER European Community program within the Interreg Caraïbes “PREST” project (E.C., S.S., D.B.); Institut Universitaire de France (E.C., R.J.); Université Côte d’Azur and the French Embassy in Haiti (S.P.); the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 758210, Geo4D project to R.J. and grant no. 805256 to Z.D.); the French National Research Agency (project ANR-21-CE03-0010 “OSMOSE” to E.C. and ANR-15-IDEX-01 “UCAJEDI Investments in the Future” to Q.B.); the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 949221 to Q.B.); and HPC resources of IDRIS (under allocations 2020-AD011012142, 2021-AP011012536, and 2021-A0101012314 to Q.B.With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe

Dehydration of subducting slow-spread oceanic lithosphere in the Lesser Antilles

Subducting slabs carry water into the mantle and are a major gateway in the global geochemical water cycle. Fluid transport and release can be constrained with seismological data. Here we use joint active-source/local-earthquake seismic tomography to derive unprecedented constraints on multi-stage fluid release from subducting slow-spread oceanic lithosphere. We image the low P-wave velocity crustal layer on the slab top and show that it disappears beneath 60–100 km depth, marking the depth of dehydration metamorphism and eclogitization. Clustering of seismicity at 120–160 km depth suggests that the slab’s mantle dehydrates beneath the volcanic arc, and may be the main source of fluids triggering arc magma generation. Lateral variations in seismic properties on the slab surface suggest that serpentinized peridotite exhumed in tectonized slow-spread crust near fracture zones may increase water transport to sub-arc depths. This results in heterogeneous water release and directly impacts earthquakes generation and mantle wedge dynamics

A Bayesian source model for the 2004 great Sumatra-Andaman earthquake

International audienceThe 2004 Mw 9.1–9.3 Sumatra-Andaman earthquake is one of the largest earthquakes of the modern instrumental era. Despite considerable efforts to analyze this event, the different available observations have proven difficult to reconcile in a single finite-fault slip model. In particular, the critical near-field geodetic records contain variable and significant postseismic signal (between 2 weeks' and 2 months' worth), while the satellite altimetry records of the associated tsunami are affected by various sources of uncertainties (e.g., source rupture velocity and mesoscale oceanic currents). In this study, we investigate the quasi-static slip distribution of the Sumatra-Andaman earthquake by carefully accounting for the different sources of uncertainties in the joint inversion of available geodetic and tsunami data. To this end, we use nondiagonal covariance matrices reflecting both observational and modeling uncertainties in a fully Bayesian inversion framework. Modeling errors can be particularly large for great earthquakes. Here we consider a layered spherical Earth for the static displacement field, nonhydrostatic equations for the tsunami, and a 3-D megathrust interface geometry to alleviate some of the potential epistemic uncertainties. The Bayesian framework then enables us to derive families of possible models compatible with the unevenly distributed and sometimes ambiguous measurements. We infer two regions of high fault slip at 3°N–4°N and 7°N–8°N with amplitudes that likely reach values as large as 40 m and possibly larger. These values are a factor of 2 larger than typically found in previous studies—potentially an outcome of commonly assumed forms of regularization. Finally, we find that fault rupture very likely involved shallow slip. Within the resolution provided by the existing data, we cannot rule out the possibility that fault rupture reached the trench

Quantification of Tsunami Bathymetry Effect on Finite Fault Slip Inversion,

International audienceThe strong development of tsunami instrumentation in the past decade now provides observations of tsunami wave propagation in most ocean basins. This evolution has led to the wide use of tsunami data to image the complexity of earthquake sources. In particular, the 2011 Mw9.0 Tohoku-Oki earthquake is the first mega-event for which such a tsunami instrumentation network was available with an almost complete azimuthal coverage. Source inversion studies have taken advantage of these observations which add a lot of constrain on the solutions, especially in the shallow part of the fault models where other standard data sets tend to lack resolution: while on-land data are quite insensitive to slip on the often-distant shallow part of a subduction fault interface, tsunami observations are directly sensitive to the shallowest slip. And it is in this shallow portion that steep bathymetry combined with horizontal motion, the so-called bathymetry effect, can contribute to the tsunami excitation, in addition to the direct vertical sea-bottom deformation. In this study, we carefully investigate the different steps involved in the calculation of this bathymetry effect, from the initial sea-floor deformation to the prediction of the tsunami records, and evaluate its contribution across the main subduction zones of the world. We find that the bathymetry effect locally exceeds 10 % of the tsunami excitation in all subduction zones and 25 % in those known to produce the largest tsunami, either from mega- or tsunami- earthquakes. We then show how the bathymetry effect can modify the tsunami wave predictions, with time shifts of the wavefront and amplitudes sometimes varying by a factor of two. If the bathymetry effect can have a strong impact on the simulated tsunami, it will also affect the solution of the finite-fault slip inversion. We illustrate this later aspect in the case of the Tohoku-Oki earthquake. We find that not accounting for the bathymetry effect will not necessarily cause strong variations in the spatial extent of the inferred coseismic rupture but can severely distort the solution. We also find that the bathymetry effect improves the consistency of the slip model inverted from tsunami data with seafloor geodesy observations, implying that taking the bathymetry effect into account reduces the epistemic uncertainties on tsunami modeling. Implementing this easily quantifiable effect in the tsunami early warning system could thus lead to improved estimates of the tsunami impact across ocean basins

Characteristics of secondary slip fronts associated with slow earthquakes in Cascadia

We implement an algorithm to automatically detect migrations of low frequency earthquakes at time scales between 30 min and 32 h during the 2003, 2004 and 2005 slow slip events in Cascadia. We interpret these migrations of seismicity as a passive manifestation of secondary slip fronts (SSFs) that propagate faster than the main front. We identify the dominant features of 383 SSFs, including time, location, duration, area, propagation velocity and estimate: their moment, stress drop, slip, and slip rate. We apply the same algorithm to continuous tremor detection in Cascadia between 2009 and 2015 and characterize 693 SSFs at time scales between 4 h and 32 h. We identify — to our knowledge for the first time — numerous 11–22.5 h long SSFs that propagate at velocities intermediate between slow slip events and previously reported SSFs. The systematic detection of SSFs fills a gap between seismically and geodetically detectable slow earthquake processes. Analysis of SSF basic features indicates a wide range of stress drops and slip rates (with medians of 5.8 kPa and 1.1 mm/h) as well as an intriguing relationship between SSF direction and duration that was observed in other contexts and could potentially help discriminate between the different physical models proposed to explain slow slip phenomena

Optimizing the information content available in geodetic data to jointly estimate co-seismic and early afterslip models

International audienceWhen analyzing the rupture of a large earthquake, geodetic data are often critical. Yet, these data are generally characterized by either a good temporal (continuous GNSS) or a good spatial (InSAR and subpixel image correlation) resolution, but rarely both. As a consequence, many studies analyze the co-seismic rupture with data also including days of early post-seismic deformation, usually corresponding to afterslip. This approximation implies that the co-seismic slip models can be biased, and that the early afterslip process is disregarded. Here, we propose a new and simple approach to improve the use of the information contained in the data: we invert simultaneously for the co-seismic slip and the early afterslip with the condition that the sum of the two models remains compatible with data covering encompassing the two slip episodes (e.g. InSAR). Meanwhile, each individual model is also constrained by geodetic data covering its own specific time frame (e.g. continuous GNSS). We validate the benefits of our approach with a toy model and an application to the 2009 Mw6.3 l'Aquila earthquake. We find that if early afterslip deformation is acknowledged as co-seismic signal, co-seismic models may be biased for a third of their amplitude while longer term post-seismic models may overlook up to 300% of the total afterslip amplitude. This example illustrates how the proposed approach could improve our comprehension of the seismic cycle, of the fault frictional properties, and how the co-seismic rupture, afterslip and aftershocks relate to one another

Mega-earthquakes rupture flat megathrusts

International audienc

Rapid source characterization of the Maule earthquake using Prompt Elasto‐Gravity Signals

Abstract The recently identified Prompt Elasto‐Gravity Signals (PEGS), generated by large earthquakes, propagate at the speed of light and are sensitive to the earthquake magnitude and focal mechanism. These characteristics make PEGS potentially very advantageous for tsunami early warning, which relies on fast and accurate estimation of the magnitude of large offshore earthquakes. PEGS‐based early warning does not suffer from the problem of magnitude estimation saturation, that P‐wave based early warning algorithms have, and could be faster than Global Navigation Satellite Systems (GNSS)‐based systems while not requiring a priori assumptions on slip distribution. We use a deep learning model called PEGSNet to evaluate the possibility to estimate in real time the evolution of the magnitude of big earthquakes in the tsunamigenic zone of Chile. The model is a Convolutional Neural Network (CNN) trained on a database of synthetic PEGS – simulated for an exhaustive set of possible earthquakes distributed along the Chilean subduction zone – augmented with empirical noise. The approach is multi‐station and leverages the information recorded by the seismic network to estimate as fast as possible the magnitude and location of an ongoing earthquake. Our results indicate that PEGSNet could have estimated that the magnitude of the 2010 M w 8.8 Maule earthquake was above 8.7, 90 seconds after origin time. Our offline simulations using real data and noise recordings further support the instantaneous tracking of the source time function of the earthquake and show that deploying seismic stations in optimal locations could improve the performance of the algorithm