Ground deformation monitoring of the eruption offshore Mayotte

In May 2018, the Mayotte island, located in the Indian Ocean, was affected by an unprecedented seismic crisis, followed by anomalous on-land surface displacements in July 2018. Cumulatively from July 1, 2018 to December 31, 2021, the horizontal displacements were approximately 21 to 25 cm eastward, and subsidence was approximately 10 to 19 cm. The study of data recorded by the on-land GNSS network, and their modeling coupled with data from ocean bottom pressure gauges, allowed us to propose a magmatic origin of the seismic crisis with the deflation of a deep source east of Mayotte, that was confirmed in May 2019 by the discovery of a submarine eruption, 50 km offshore of Mayotte ([Feuillet et al., 2021]). Despite a non-optimal network geometry and receivers located far from the source, the GNSS data allowed following the deep dynamics of magma transfer, via the volume flow monitoring, throughout the eruption

Impact of the North Atlantic Oscillation on Southern Europe Water Distribution: Insights from Geodetic Data

International audienceFrom space gravity and station position data over southern Europe from 2002 to 2010, this study investigates the interannual mass redistributions using principal component analysis. The dominant mode, which appears both in gravity and positioning, results from the North Atlantic Oscillation (NAO). This analysis allows us to isolate and characterize the NAO impact on the mass distribution, which appears centered over the Black Sea and its two main catchment basins, the Danube and Dnieper

Assessing the precision in loading estimates by geodetic techniques in Southern Europe

International audienceThis paper investigates the precision of the estimation of geophysical fluid load deformation computed from GRACE space gravity, GPS vertical displacement and geophysical fluids models [Global Circulation Models (GCMs) for ocean, atmosphere and hydrology], using the three-cornered hat method. This method allows the estimation of the variance of the errors of each technique, when the same quantity is monitored by three instruments with independent errors. Applied on a network of stations, several points of view can be considered: the technique level (in order to determine the error of each technique: GRACE, GPS and GCMs), the solution level (allowing to compare the precision of the same technique when different strategies/models are used), and the station level (in order to emphasize local anomalies and geographical patterns). In particular, our results show a precision of the loading vertical displacement at the level of 1 mm when using GRACE or the fluid models, and of 2 mm using GPS. We do not find significant differences between the precision of different solutions of the same techniques, even when there are strong differences in the data processing

The 2018–2019 seismo-volcanic crisis east of Mayotte, Comoros islands: seismicity and ground deformation markers of an exceptional submarine eruption

International audienceOn 10 May 2018, an unprecedented long and intense seismic crisis started offshore, east of Mayotte, the easternmost of the Comoros volcanic islands. The population felt hundreds of events. Over the course of 1 yr, 32 earthquakes with magnitude greater than 5 occurred, including the largest event ever recorded in the Comoros (Mw = 5.9 on 15 May 2018). Earthquakes are clustered in space and time. Unusual intense long lasting monochromatic very long period events were also registered. From early July 2018, Global Navigation Satellite System (GNSS) stations and Interferometric Synthetic Aperture Radar (InSAR) registered a large drift, testimony of a large offshore deflation. We describe the onset and the evolution of a large magmatic event thanks to the analysis of the seismicity from the initiation of the crisis through its first year, compared to the ground deformation observation (GNSS and InSAR) and modelling. We discriminate and characterize the initial fracturing phase, the phase of magma intrusion and dyke propagation from depth to the subsurface, and the eruptive phase that starts on 3 July 2018, around 50 d after the first seismic events. The eruption is not terminated 2 yr after its initiation, with the persistence of an unusual seismicity, whose pattern has been similar since summer 2018, including episodic very low frequency events presenting a harmonic oscillation with a period of ∼16 s. From July 2018, the whole Mayotte Island drifted eastward and downward at a slightly increasing rate until reaching a peak in late 2018. At the apex, the mean deformation rate was 224 mm yr−1 eastward and 186 mm yr−1 downward. During 2019, the deformation smoothly decreased and in January 2020, it was less than 20 per cent of its peak value. A deflation model of a magma reservoir buried in a homogenous half space fits well the data. The modelled reservoir is located 45 ± 5 km east of Mayotte, at a depth of 28 ± 3 km and the inferred magma extraction at the apex was ∼94 m3 s−1. The introduction of a small secondary source located beneath Mayotte Island at the same depth as the main one improves the fit by 20 per cent. While the rate of the main source drops by a factor of 5 during 2019, the rate of the secondary source remains stable. This might be a clue of the occurrence of relaxation at depth that may continue for some time after the end of the eruption. According to our model, the total volume extracted from the deep reservoir was ∼2.65 km3 in January 2020. This is the largest offshore volcanic event ever quantitatively documented. This seismo-volcanic crisis is consistent with the trans-tensional regime along Comoros archipelago

Analyses comparée de séries temporelles GNSS synthétiques - Biais et précision sur les estimations de vitesses

105 synthetic time series replicating GNSS 3D position series are analyzed independently by nine different groups within the RENAG consortium in order to characterize the variability in estimations of long-term velocities. The main objective is not a detailed study of the parameters and sources controlling velocity variations, but simply to establish first-order conclusions regarding the uncertainties on GNSS velocity estimations as a function of the different analysis methods and software. Because the true velocities are known, our results are presented in terms of velocity biases (i.e. deviations of the estimated velocities relative to the expected values). Statistics on these biases can then be used as indicators of the potential precision of actual GNSS velocities.To first order, the nine methods and software of time series analysis provide horizontal (resp. vertical) velocity estimations at precisions better than 1.0 mm/a (resp. 2.0 mm/a). None of the tested methods or software clearly stands out as significantly better or worse than the others. However, a group of four solutions (including the unweighted average of all nine solutions) provides systematically better results than the others. They are based on a standard time series analysis using a least-square inversion of a parametric model (velocity, seasonal terms, offsets) with either automatic and manual offset detection methods.For time series with noise and duration characteristics corresponding to classical GNSS data (e.g., RENAG-RESIF stations), the velocity biases (and thus potential GNSS velocity precision) are characterized by the following statistics:• Medians ca. 0.1 mm/a (horizontal components) and 0.1–0.3 mm/a (vertical component).• 95th percentiles ca. 0.2–0.7 mm/a (horizontal components) and 0.5–2.0 mm/a (vertical component).• RMS (root-mean-square) ca. 0.1–0.3 mm/a (horizontal components) and 0.3–0.9 mm/a (vertical component).In addition to the variability of velocity estimations as a function of the analysis methods, first order information can be derived regarding the solution combination and velocity uncertainties:• The unweighted average of all nine analyses yields results systematically in the upper tier of all individual solutions.• Formal velocity uncertainties (standard errors) calculated on the basis of colored- noise models are statically representative of the velocity biases.• In contrast, formal velocity uncertainties (standard errors) calculated using other methods (white noise or statistical variance) are not representative of the velocity biases (resp. significantly too low or too high)