Mantle upwellings and convective instabilities revealed by seismic tomography and helium isotope geochemistry beneath eastern Africa

International audienceThe relationship between intraplate volcanism and continental tectonics has been investigated for North and East Africa using a high resolution three-dimensional anisotropic tomographic model derived from seismic data of a French experiment ''Horn of Africa'' and existing broadband data. The joint inversion for seismic velocity and anisotropy of the upper 400 km of the mantle, and geochemical data reveals a complex interaction between mantle upwellings, and lithosphere. Two kinds of mantle upwellings can be distinguished: The first one, the Afar ''plume'' originates from deeper than 400 km and is characterized by enrichment in primordial 3 He and 3 He/ 4 He ratios higher than those along mid-ocean ridges (MOR). The second one, associated with other Cenozoic volcanic provinces (Darfur, Tibesti, Hoggar, Cameroon), with 3 He/ 4 He ratios similar to, or lower than MOR, is a consequence of shallower upwelling. The presumed asthenospheric convective instabilities are oriented in an east-west direction, resulting from interaction between south-north asthenospheric mantle flow, main plume head and topography on the base of lithosphere

First Focal Mechanisms of Marsquakes

Since February 2019, NASA's InSight lander is recording seismic signals on the planet Mars, which, for the first time, allows to observe ongoing tectonic processes with geophysical methods. A number of Marsquakes have been located in the Cerberus Fossae graben system in Elysium Planitia and further west, in the Orcus Patera depression. We present a first study of the focal mechanisms of three well-recorded events (S0173a, S0183a, S0235b) to determine the processes dominating in the source region. We infer for all three events a predominantly extensional setting. Our method is adapted to the case of a single, multicomponent receiver and based on fitting waveforms of P and S waves against synthetic seismograms computed for the initial crustal velocity model derived by the InSight team. We explore the uncertainty due to the single-station limitation and find that even data recorded by one station constrains the mechanisms (reasonably) well. For the events in the Cerberus Fossae region (S0173a, S0235b) normal faulting with a relatively steep dipping fault plane is inferred, suggesting an extensional regime mainly oriented E-W to NE-SW. The fault regime in the Orcus Patera region is not determined uniquely because only the P wave can be used for the source inversion. However, we find that the P and weak S waves of the S0183a event show similar polarities to the event S0173, which indicates similar fault regimes

Atmospheric Science with InSight

International audienceIn November 2018, for the first time a dedicated geophysical station, the InSight lander, will be deployed on the surface of Mars. Along with the two main geophysical packages, the Seismic Experiment for Interior Structure (SEIS) and the Heat-Flow and Physical Properties Package (HP3), the InSight lander holds a highly sensitive pressure sensor (PS) and the Temperature and Winds for InSight (TWINS) instrument, both of which (along with the InSight FluxGate (IFG) Magnetometer) form the Auxiliary Sensor Payload Suite (APSS). Associated with the RADiometer (RAD) instrument which will measure the surface brightness temperature, and the Instrument Deployment Camera (IDC) which will be used to quantify atmospheric opacity, this will make InSight capable to act as a meteorological station at the surface of Mars. While probing the internal structure of Mars is the primary scientific goal of the mission, atmospheric science remains a key science objective for InSight. InSight has the potential to provide a more continuous and higher-frequency record of pressure, air temperature and winds at the surface of Mars than previous in situ missions. In the paper, key results from multiscale meteorological modeling, from Global Climate Models to Large-Eddy Simulations, are described as a reference for future studies based on the InSight measurements during operations. We summarize the capabilities of InSight for atmospheric observations, from profiling during Entry, Descent and Landing to surface measurements (pressure, temperature, winds, angular momentum), and the plans for how InSight’s sensors will be used during operations, as well as possible synergies with orbital observations. In a dedicated section, we describe the seismic impact of atmospheric phenomena (from the point of view of both “noise” to be decorrelated from the seismic signal and “signal” to provide information on atmospheric processes). We discuss in this framework Planetary Boundary Layer turbulence, with a focus on convective vortices and dust devils, gravity waves (with idealized modeling), and large-scale circulations. Our paper also presents possible new, exploratory, studies with the InSight instrumentation: surface layer scaling and exploration of the Monin-Obukhov model, aeolian surface changes and saltation / lifing studies, and monitoring of secular pressure changes. The InSight mission will be instrumental in broadening the knowledge of the Martian atmosphere, with a unique set of measurements from the surface of Mars

Detection, analysis, and removal of glitches from InSight's seismic data from Mars

The instrument package SEIS (Seismic Experiment for Internal Structure) with the three very broadband and three short‐period seismic sensors is installed on the surface on Mars as part of NASA's InSight Discovery mission. When compared to terrestrial installations, SEIS is deployed in a very harsh wind and temperature environment that leads to inevitable degradation of the quality of the recorded data. One ubiquitous artifact in the raw data is an abundance of transient one‐sided pulses often accompanied by high‐frequency spikes. These pulses, which we term “glitches”, can be modeled as the response of the instrument to a step in acceleration, while the spikes can be modeled as the response to a simultaneous step in displacement. We attribute the glitches primarily to SEIS‐internal stress relaxations caused by the large temperature variations to which the instrument is exposed during a Martian day. Only a small fraction of glitches correspond to a motion of the SEIS package as a whole caused by minuscule tilts of either the instrument or the ground. In this study, we focus on the analysis of the glitch+spike phenomenon and present how these signals can be automatically detected and removed from SEIS's raw data. As glitches affect many standard seismological analysis methods such as receiver functions, spectral decomposition and source inversions, we anticipate that studies of the Martian seismicity as well as studies of Mars' internal structure should benefit from deglitched seismic data.Centre National d'Etudes Spatiales (CNES)Swiss SpaceOffice (SSO)Agence Nationale de la RechercheDLR German Space AgencyInSight PSP progra

Surface wave higher-mode phase velocity measurements using a roller-coaster-type algorithm

International audienceIn order to solve a highly non-linear problem by introducing the smallest a priori information, we present a new inverse technique called the `roller coaster' technique and apply it to measure surface wave mode-branch phase velocities. The fundamental mode and the first six overtone parameter vectors, defined over their own significant frequency ranges, are smoothed average phase velocity perturbations along the great circle epicentre-station path. These measurements explain well both Rayleigh and Love waveforms, within a maximum period range included between 40 and 500 s. The main idea of this technique is to first determine all possible configurations of the parameter vector, imposing large-scale correlations over the model space, and secondly to explore each of them locally in order to match the short-wavelength variations. The final solution which achieves the minimum misfit of all local optimizations, in the least-squares sense, is then hardly influenced by the reference model. Each mode-branch a posteriori reliability estimate turns out to be a very powerful instrument in assessing the phase velocity measurements. Our Rayleigh results for the Vanuatu-California path seem to agree correctly with previous ones

New CNN based tool to discriminate anthropogenic from natural low magnitude seismic events

International audienceWith the deployment of high quality and dense permanent seismic networks over the last 15 years comes a dramatic increase of data to process. In order to lower the threshold value of magnitudes in a catalogue as much as possible, the issue of discrimination between natural and anthropogenic events is becoming increasingly important. To achieve this discrimination, we propose the use of a convolutional neural network (CNN) trained from spectrograms. We built a database of labeled events detected in metropolitan France between 2020 and 2021 and trained a CNN with three-component 60 s spectrograms ranging frequencies from 1 to 50 Hz. By applying our trained model on independent French data, we reach an accuracy of 98.2 per cent. In order to show the versatility of the approach, this trained model is also applied on different geographical areas, a post-seismic campaign from NW France and data from Utah, and reaches an accuracy of 100.0 per cent and 96.7 per cent respectively. These tests tend to hypothesise that some features due to explosions compared to earthquakes are widely shared in different geographical places. In a first approach, we propose that it can be due to a contrast in the energy balance between natural and anthopogenic events. Earthquake seismic energies seem to be more continuous as a function of frequency (vertical bands features in a spectrogram) and conversely for explosions (horizontal strips)

Discrimination entre événements naturels et anthropiques basée sur le Deep Learning

L'un des principaux défis dans l'élaboration d'un catalogue d'événements de faible amplitude est la discrimination entre les événements naturels (événements tectoniques) et les événements anthropiques (causés par les activités humaines). Pour parvenir à une discrimination automatique, une méthode basée sur du Deep Learning a été développée : grâce à une base de données suffisamment importante, des algorithmes d'intelligence artificielle peuvent s'entraîner à reconnaitre des objets naturels et prendre des décisions. Les spectrogrammes de formes d'ondes sismiques ont été choisis pour constituer cette base de données

Seismic Station Monitoring Using Deviation from the Gaussianity

International audienceAbstract Degradation of the seismic signal quality sometimes occurs at permanent and temporary stations. Although the most likely cause is a high level of humidity, leading to corrosion of the connectors, environmental changes can also alter recording conditions in different frequency ranges and not necessarily for all three components in the same way. Assuming that the continuous seismic signal can be described by a normal distribution, we present a new approach to quantify the seismogram quality and to point out any time sample that deviates from this Gaussian assumption. We introduce the notion of background Gaussian signal (BGS) to characterize a set of samples that follows a normal distribution. The discrete function obtained by sorting the samples in ascending order of amplitudes is compared with a modified Probit function to retrieve the elements composing the BGS, and its statistical properties (mostly its standard deviation σ$_G$). As soon as there is any amplitude perturbation, σ$_G$ deviates from the standard deviation of all samples composing the time window (σ). Hence, the parameter log(σ/σ$_G$) directly quantifies the alteration level. For a single day, a given frequency range and a given component, the median of all log(σ/σ$_G$) that can be computed using short-time windows, reflects the overall gaussianity of the continuous seismic signal. We demonstrate that it can be used to efficiently monitor the quality of seismic traces using this approach at four broadband permanent stations. We show that the daily log(σσG) is sensitive to both subtle changes on one or two components as well as the signal signature of a sensor’s degradation. Finally, we suggest that log(σσG) and other parameters that are computed from the BGS bring useful information for station monitoring in addition to existing methods

Quantification des perturbations non gaussiennes dans le signal simique continu

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