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AGU Fall Meeting in San Francisco, 9-13 December 2019, The SEIS seismometer package was successfully deployed on Mars by February 2019. Routine operations are split into two services: the Mars Structure Service (MSS) and the Marsquake Service (MQS), which are responsible for defining interior structure models and seismicity catalogs, respectively. Initial model delivery from MSS is based on a limited dataset of two Marsquakes with a clear P and S arrivals. Different inversion algorithms were developed by the MSS team in order to retrieve the first 1D averaged model of Mars. Two complementary approaches are considered. One set of models (called M1) is parameterized in seismic velocity and density as a function of depth. A second set of models (called M2) is obtained by parameterizing with geodynamical constraints like temperature and composition. We use Bayesian inversion techniques to obtain robust probability density functions of seismic velocity profiles. Different types of data are considered for these inversions including body waves, surface waves and receiver functions. To characterize what we could learn about Mars¿ interior structure with only one station and with the first seismic event, we performed inversions of synthetic data following a blind test process, where the interior model and the Marsquake parameters (location, depth, origin time, and moment tensor) were unknown to all team members carrying out data analysis and inversion. In this presentation we will discuss the results of this blind test in terms of structure and compare different methods developed by the MSS. We will then show results from investigations of the first, real seismic data due to quakes on Mars recorded by SEIS in terms of the structure and quake locations. We will especially focus our investigation on joint inversions made not only with the arrival time, but also with secondary seismic data extracted from the detected events, including apparent attenuation rate and with receiver functions. Of course, much more detailed analysis will be made if Mars seismicity provide us in the near future larger quakes with body wave phases and first orbit surface wave dispersion, and/or one event large enough to record multiple orbit surface waves, and will augment future interiors models of Mars.

AGU Fall Meeting 2019 in San Francisco, 9-13 December 2019, Exploring the internal structure and the dynamics of our solar system is mandatory to understand the behavior of our universe and its origin. One of the tools chosen by NASA is seismology particularly in order to constrain the parameters of the deep interior structure of the red planet via the Insight (Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport) mission. InSight was successfully landed on November 26th, 2018 in Elysium Planitia with geophysical instruments a short-period seismometer and a broadband seismometer (SEIS, Seismic Experiment for Interior Structure). Both seismometers are now installed directly on Mars surface and enable to analyze the continuous seismic signal.But, before making the structure inversion, we need to extract the features from SEIS data. However, those features may nevertheless be hidden into noise, or may escape from analysis due to the limitations imposed by the current methodologies.Therefore, the aim of this study is to overcome this problem by well extracting, recognizing and classifying the instrument signals using Machine Learning and Deep Learning new strategies inspired from the Deep scattering network.This is very promising for the SIES data as, we¿re going to be able not only to detect the familiar signals, but the exciting part is the unseen or the unknown ones. This technique is used to clean the data from the glitches. In fact, this tool has recently proved to be powerful in signal processing, data automatic feature extraction and may even be helpful to detect new types of signals. Those new signals can reveal unknown processes and lead to new discoveries about Mars physical processes.The method used in this study is divided into three fundamental steps. The first one, to make an automatic feature extraction using the Deep scattering transform which is a convolution neural network that computes a cascade of wavelets calculations and filtering operations to get a stable waveform representation stable to local deformations and overlapping at multiple times and frequencies.. The second step is to use those features for signal classification using Machine Learning classifier Gaussian Mixture Network. Finally, we update the wavelet mother bank depending on the results of the classification error minimization using Adam stochastic gradient descent.

AGU Fall Meeting 2019 in San Francisco , 9-13 December 2019, EIS/InSIght team, InSight is the first planetary mission with a seismometer package, SEIS, since the Apollo Lunar Surface Experiments Package. SEIS is complimented by APSS, which has as a goal to document the atmospheric source of seismic noise and signals. Since June 2019, SEIS has been delivering 6 axis 20 sps continuous seismic data, a rate one order of magnitude larger originally planned. More than 50 events have been detected by the end of July 2019 but only three have amplitudes significantly above the SEIS instrument requirement. Two have clear and coherent arrivals of P and S waves, enabling location, diffusion/attenuation characterization and receiver function analysis. The event¿s magnitudes are likely ¿ 3 and no clear surface waves nor deep interior phases have been identified. This suggests deep events with scattering along their final propagation paths and with large propagation differences as compared to Earth and Moon quakes. Most of the event¿s detections are made possible due to the very low noise achieved by the instrument installation strategy and the very low VBB self-noise. Most of the SEIS signals have amplitudes of spectral densities in the 0.03-5Hz frequency bandwidth ranging from 10-10 m/s2/Hz1/2 to 5 10-9 m/s2/Hz1/2. The smallest noise levels occurs during the early night, with angstrom displacements or nano-radian tilts. This monitors the elastic and seismic interaction of a planetary surface with its atmosphere, illustrated not only by a wide range of SEIS signals correlated with pressure vortexes, dust devils or wind activity but also by modulation of resonances above 1 Hz, amplified by ultra-low velocity surface layers. After about one half of a Martian year, clear seasonal changes appear also in the noise, which will be discussed. One year after landing, the seismic noise is therefore better and better understood, and noise correction techniques begun to be implemented, either thanks to the APSS wind and pressure sensors, or by SEIS only data processing techniques. These data processing techniques open not only the possibility of better signal to noise ratio of the events, but are also used for various noise auto-correlation techniques as well as searches of long period signals. Noise and seismic signals on Mars are therefore completely different from what seismology encountered previously on Earth and Moon.

EGU General Assembly in Viena, Austria,7–12 April 2019, Mars atmospheric pressure variations induce ground deformations that are the main source of environmental noise on the InSight SEIS instrument. Phenomena generating pressure variations, such as meteorological wavefronts, gravity waves, convective vortices and small-scale turbulence are covering the whole frequency range of SEIS broadband seismometers. These signals impact the capability of SEIS sensors to detect seismic waves propagating in Mars interior. However, the relation between pressure variations measured by the InSight pressure sensor and ground movements measured by SEIS being essentially linear, it is possible to invert both signals to find the transfer function between pressure and ground motion. Then, pressure generated ground movements can be substracted from SEIS records in order to improve their sensitivity to seismic waves. The transfer function, usually called compliance, depends on the ground properties. Thus, the decorrelation of SEIS records from pressure effects will also allow us to constrain the subsurface properties at the InSight landing site. Our study compares two different pressure decorrelation methods applied to Mars synthetic data, and records by both short period and very broadband SEIS sensors. The pressure-decorrelation methods are presented and their differences in terms of underlying assumptions and implementations are explained. Their performances and drawbacks, analyzed on realistic synthetic datasets, are discussed. We plan to apply these methods to real Mars data and use them to retrieve the ground compliance at the InSight landing site over a broad frequency range. Accordingly, we expect to extract elastic properties of the subsurface through the compliance measurements.Wind measurements by the TWINS wind sensor will be integrated in order to separate the effects of various atmospheric phenomena. This sensor is almost identical to the Rover Environmental Monitoring Station sent to Mars as part of the Mars Science Laboratory (MSL), which operated even after the damage suffered during the MSL landing for more than two Martian years. Thus, wind measured by TWINS will provide valuable inputs to the decorrelation on the InSight SEIS instrument. Finally, the variations of decorrelation performances and compliance measurements with the diurnal Mars cycle will be analyzed.

EGU General Assembly in Viena, Austria,7–12 April 2019, The InSight mission landed on Mars on November 26th, 2018 with several geophysical instruments including a short-period seismometer and a broadband seismometer (SEIS, Seismic Experiment for Interior Structure). Both seismometers are now installed directly on Mars surface and enable to analyze the continuous seismic signal. The purpose of this study is to analyze, quantify and characterize the frequency-dependent polarization of Mars continuous seismic signal. On Earth, the secondary microseisms are the dominant signals in the absence of earthquakes. Secondary microseisms are dominantly Rayleigh waves, with elliptical polarization in the vertical plane. We do not expect to record microseisms on Mars because of the absence of oceans. We adapted the method developed for Earth data by Schimmel et al. (2011) to analyze the frequency-dependent polarization of continuous Mars data. The method enables to detect elliptically polarized signal such as Rayleigh waves and also linearly polarized signals that could be either Love waves or body waves. The method was tested on the two blindtest datasets provided by the Marsquake Service (MQS) and by the Mars Structure Service (MSS). It is then applied to analyze the first data recorded on Mars by the broadband and the short period seismometers.

50th Lunar and Planetary Science Conference 2019 in The Woodlands, Texas, March 18–22, 2019., Introduction: Mars atmospheric pressure variations induce ground deformations that are the main source of environmental noise on the InSight SEIS instrument. Phenomena generating pressure variations, such as meteorological wavefronts, gravity waves, convective vortices and small-scale turbulence are covering the whole frequency range of SEIS broadband seismometers. These signals impact the capability of SEIS sensors to detect seismic waves propagating in Mars interior. However, the relation between pressure variations measured by InSight pressure sensor and ground movements measured by SEIS being essentially linear, it is possible to invert both signals to find the transfer function between pressure and ground motion. Then, pressure generated ground movements can be substracted from SEIS records in order to improve their sensitivity to seismic waves. The transfer function, usually called compliance, depends on the ground properties. Thus, the decorrelation of SEIS records from pressure effects also allows us to constrain the sub-surface properties at InSight location.

The 50th Lunar and Planetary Science Conference in The Woodlands, Texas, March 18–22, 2019., Introduction: The InSight mission landed on Mars on November, 26, 2018. This is the first planetary mission designed to deploy a complete geophysical observatory on Mars, following in the footsteps of the Apollo Lunar Surface Experiments Package (ALSEP) deployed on the Moon in the 1970s [1]. It will thus provide the first ground truth constraints on interior structure of the planet. The Seismic Experiment for Interior Structure (SEIS) [2] is one of three primary scientific investigations, the others being the Heat Flow and Physical Properties Package (HP3) [3] and the Rotation and Interior Structure Experiment (RISE) [4]. SEIS is supported by the APSS package, (Auxiliary Payload Sensor Suite, [5]), whose goal is to document environmental sources of seismic noise and signals, as well as an imaging system [6]. After a brief description of the SEIS experiment, we will describe the deployment process, including the evolution of the SEIS noise from the deck (with only SPs) to the ground (with both VBBs and SPs), first without and finally with wind shield. We will then discuss early scientific observations, providing first constrains on the Mars micro-seismic noise, atmospherically-generated seismic signals, and surface and subsurface elastic structure., The SEIS team acknowledge the supports of NASA, CNES, UKSA, SSO and DLR for the experiment funding and of the SEIS operation team [38] for delivering SEIS data.

EGU General Assembly in Viena, Austria,7–12 April 2019, 26th, 2018, including a broadband and a short-period seismometer (Seismic Experiment for Interior Structure, SEIS). The seismic instrument package is specifically designed to record marsquakes and meteoritic impacts in Martian conditions. Routine operations are split into two services: the Mars Structure Service (MSS) and the Marsquake Service (MQS), which are responsible for defining structure models and seismicity catalogs, respectively. The first “deliverable” of the MSS will be a model based on the events detected during the first 3 months of seismic monitoring of the mission, for which only a few quakes might be expected based on current estimates of Mars seismic activity. To test our approach of determining the interior model of Mars and to prepare the InSight science team for data return, we made use of a “blind test” time series for which the Marsquake parameters (location, depth, origin time, and moment tensor) and interior model were unknown to the group at large. In preparation for the mission, the goal was to develop mature algorithms to handle the data as efficiency as possible. Synthetic seismic waveforms were computed in a 1D mantle model with a 3D crust on top using AxiSEM and Salvus. The time series were created by adding seismic noise that relies on pre-landing estimates of noise generated by the sensors, electronic system, environment, and nearby lander. To characterize what we could learn about Mars interior structure with only one station and with the first seismic event, we performed inversions of a synthetic data following a blind test process, where the interior model was unknown to all team members carrying out data analysis and inversion. We detail and compare the results of this “blind test” using different methods including inversion of surface wave dispersion data, body waves travel times, and the waveforms themselves.We have used mainly Bayesian techniques to obtain robust probability density functions of interior structure parameters. The effects on the retrieved model distributions of fixing mars quake location and origin time are investigated, as is the effect of using fixed Vs flexible parameterizations. To allow for tighter constraints, we also test the use of priors based on thermodynamicallyconstrained models together with seismic observations, as well as seismic confirmation/rejection of models purely based on thermodynamical modelling. These techniques considered here form a large part of the planned modeling of the MSS that will be ultimately employed with the first recording of a seismic event by InSight.

International audience

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International audience

International audience

International audience