Publications

Stress transmission along mid-crustal faults highlighted by the 2021 Mw 6.5 San Juan (Argentina) earthquake

Published in Scientific Reports, 2022

Understanding the mechanisms of crustal deformation along convergent margins is critical to identifying seismogenic structures and assessing earthquake hazards for nearby urban centers. In the southern central Andes (28–33 S), differences in the style of middle to upper-crustal deformation and associated seismicity are highlighted by the January 19th, 2021 (Mw 6.5) San Juan earthquake. We integrate waveforms recorded at regional and teleseismic distances with co-seismic displacements calculated from local Global Navigation Satellite System time series, to re-estimate the source parameters of the 2021 San Juan earthquake, confirming a mid-crustal nucleation depth (21 ± 2 km) and right-lateral transpressional mechanism. Considered alongside decades of seismic observations and geological data, this event provides evidence for retroarc deformation partitioning among inherited basement faults and upper-crustal structures in response to oblique convergence of the Nazca and South American plates. As they may transfer shortening to active upper-crustal faults associated with historically devastating shallower earthquakes, a better understanding of seismogenic basement faults such as the mid-crustal structure activated during the 2021 San Juan earthquake earthquake could help future re-assessment of the seismic risk in western Argentina.

Recommended citation: Ammirati, J. B., Mackaman-Lofland, C., Zeckra, M., & Gobron, K. (2022). "Stress transmission along mid-crustal faults highlighted by the 2021 Mw 6.5 San Juan (Argentina) earthquake." Scientific Reports. 12(1). https://doi.org/10.1038/s41598-022-22752-6

Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series

Published in Journal of Geodesy, 2022

Understanding and modelling the properties of the stochastic variations in geodetic time series is crucial to obtain realistic uncertainties for deterministic parameters, e.g., long-term velocities, and helpful in characterizing non-modelled processes. With the increasing span of geodetic time series, it is expected that additional observations would help better understand the low-frequency properties of these stochastic variations. In the meantime, recent studies evidenced that the choice of the functional model for the time series biases the assessment of these low-frequency stochastic properties. In particular, frequent offsets in position time series can hinder the evaluation of the noise level at low frequencies and prevent the detection of possible random-walk-type variability. This study investigates the ability of the Maximum Likelihood Estimation (MLE) method to correctly retrieve low-frequency stochastic properties of geodetic time series in the presence of frequent offsets. We show that part of the influence of offsets reported by previous studies results from the MLE method estimation biases. These biases occur even when all offset epochs are correctly identified and accounted for in the trajectory model. They can cause a dramatic underestimation of deterministic parameter uncertainties. We show that one can avoid biases using the Restricted Maximum Likelihood Estimation (RMLE) method. Yet, even when using the RMLE method or equivalent, adding offsets to the trajectory model inevitably blurs the estimated low-frequency properties of geodetic time series by increasing low-frequency stochastic parameter uncertainties more than other stochastic parameters.

Recommended citation: Gobron, K., Rebischung, P., de Viron, O., Demoulin, A., & Van Camp, M. (2022). "Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series." Journal of Geodesy. 96(7). https://doi.org/10.1007/s00190-022-01634-9

Extreme hydrometeorological events, a challenge for gravimetric and seismology networks

Published in Earth's Future, 2022

Extreme events will become more common due to global change, requiring enhanced monitoring and pushing conventional observation networks to their limits. This encourages us to combine all the possible sources of information to obtain a complete picture of extreme events and their evolution. This commentary builds on an example of the July 2021 catastrophic floods that hit northwest Europe, for which the use of seismometer and gravimeter captures complementary data and brings a new understanding of the event and its dynamics. A sudden increase in seismic noise coincides with the testimony reporting on a “tsunami” downstream of the geophysical station. Concurrently, the gravimeter showed increasing saturation of the weathered zone, showing less and less water accumulation and increasing runoff. When rain re-intensified after a 3-hr break, the subsoil’s saturation state induced an accelerated runoff increase, as revealed by the river flow, in a much stronger way than during the rainy episodes just before. We show that the gravimeter detected the saturation of the catchment subsoil and soil in real-time. When the rain re-intensified, this saturation resulted in a sudden, devastating and deadly flood. Our study opens up the possibility of integrating real-time gravity in early warning systems for such events.

Recommended citation: Van Camp, M., de Viron, O., Dassargues, A., Delobbe, L., Chanard, K., & Gobron, K. (2022). "Extreme hydrometeorological events, a challenge for gravimetric and seismology networks." Earth's Future. 10(4). https://doi.org/10.1029/2022EF002737

Influence of aperiodic non-tidal atmospheric and oceanic loading deformations on the stochastic properties of global GNSS vertical land motion time series

Published in Journal of Geophysical Research: Solid Earth, 2021

Monitoring vertical land motions (VLMs) at the level of 0.1 mm/yr remains one of the most challenging scientific applications of global navigation satellite systems (GNSS). Such small rates of change can result from climatic and tectonic phenomena, and their detection is important to many solid Earth-related studies, including the prediction of coastal sea-level change and the understanding of intraplate deformation. Reaching a level of precision allowing to detect such small signals requires a thorough understanding of the stochastic variability in GNSS VLM time series. This paper investigates how the aperiodic part of non-tidal atmospheric and oceanic loading (NTAOL) deformations influences the stochastic properties of VLM time series. Using the time series of over 10,000 stations, we describe the impact of correcting for NTAOL deformation on 5 complementary metrics, namely: the repeatability of position residuals, the power-spectrum of position residuals, the estimated time-correlation properties, the corresponding velocity uncertainties, and the spatial correlation of the residuals. We show that NTAOL deformations cause a latitude-dependent bias in white noise plus power-law model parameter estimates. This bias is significantly mitigated when correcting for NTAOL deformation, which reduces velocity uncertainties at high latitudes by 70%. Therefore, removing NTAOL deformation before the statistical analysis of VLM time series might help to detect subtle VLM signals in these areas. Our spatial correlation analysis also reveals a seasonality in the spatial correlation of the residuals, which is reduced after removing NTAOL deformation, confirming that NTAOL is a clear source of common-mode errors in GNSS VLM time series.

Recommended citation: Gobron, K., Rebischung, P., Van Camp, M., Demoulin, A., & de Viron, O. (2021)."Influence of Aperiodic Non-Tidal Atmospheric and Oceanic Loading Deformations on the Stochastic Properties of Global GNSS Vertical Land Motion Time Series." Journal of Geophysical Research: Solid Earth. 126(9). https://doi.org/10.1029/2021JB022370

Assessment of tide gauge biases and precision by the combination of multiple collocated time series

Published in Journal of Atmospheric and Oceanic Technology, 2019

This study proposes a method for the cross calibration of tide gauges. Based on the combination of at least three collocated sea level time series, it takes advantage of the least squares variance component estimation (LS-VCE) method to assess both sea level biases and uncertainties in real conditions. The method was applied to a multi-instrument experiment carried out on Aix Island, France, in 2016. Six tide gauges were deployed to carry out simultaneous sea level recordings for 11 h. The best results were obtained with an electrical contact probe, which reaches a 3-mm uncertainty. The method allows us to assess both the biases and the precision—that is, the full accuracy—for each instrument. The results obtained with the proposed combination method have been compared to that of a buddy-checking method. It showed that the combination of all the time series also provides more precise bias estimates.

Recommended citation: Gobron, K., de Viron, O., Woppelmann, G., Poirier, E., Ballu, V., & Van Camp, M. (2019)."Assessment of Tide Gauge Biases and Precision by the Combination of Multiple Collocated Time Series." Journal of Atmospheric and Oceanic Technology. 36(10). https://doi.org/10.1175/JTECH-D-18-0235.1