Consumption of irradiated foods: strawberries case study

Faced with the various technologies used today to preserve food, consumers are becoming more demanding on information relating to both the quality and the processing of food. One of the technologies that has proven its effectiveness in food safety is irradiation, however people remain cautious or even refuse this technology which is not yet very popular and popularized thinking that it is a toxic treatment. This paper identifies the basic knowledge of two populations (Tunisian and Portuguese) about ionizing treatment and their intention to purchase irradiated foods, focusing on strawberry fruits. An online survey was conducted for research purposes and 1,000 people, living in Tunisia and Portugal were involved. The findings showed that there is still a dearth of knowledge on food irradiation, which demonstrates mistrust, misunderstandings, and reluctance to purchase irradiated products. In contrast to 56.3% of Tunisians, the data indicated that 60.7% of Portuguese do not know what food irradiation is. The two populations think that irradiating food and consuming it are harmful, despite the fact that their knowledge of the process is spread out differently. The Portuguese, who were more interested about food irradiation, were also more inclined to purchase and consume irradiated strawberries than the Tunisians. In fact, 62.7% of Portuguese people indicated they would be convinced to buy irradiated strawberries, in contrast to 33.5% of Tunisians who stated they would certainly not buy it and insisted on the harmful effects of the treatment if they had more knowledge and evidence if the treatment had been shown to be successful

Unsupervised representation learning for clustering SEIS data in continuous records with deep scattering network

AGU Fall Meeting 2019 in San Francisco, 9-13 December 2019Exploring 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

Anatomy of continuous Mars SEIS and pressure data from unsupervised learning

International audienceThe seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories

Mars Structure Service: Single-station and single-event marsquake inversion for structure using synthetic Martian waveforms

EGU General Assembly in Viena, Austria,7–12 April 201926th, 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

The Mars Structure Service for InSight:Single-Station Marsquake Inversions for Structure

AGU Fall Meeting in San Francisco, 9-13 December 2019The 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

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

The SEIS instrument package with the three very broad-band 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 for 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 precursors. These pulses, which we term "glitches", can be modeled as the response of the instrument to a step in acceleration, while the precursors 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 and they are all due to minuscule instrument tilts. In this study, we focus on the analysis of the glitch+precursor phenomenon and present how these signals can be automatically detected and removed from SEIS' raw data. As glitches affect many standard seismological analysis methods such as receiver functions or spectral decomposition, we anticipate that studies of the Martian seismicity as well as studies of Mars' internal structure should benefit from deglitched seismic data

InSight seismic data from Mars: Effect and treatment of transient data disturbances.

The instrument package SEIS (Seismic Experiment for Internal Structure) with the two co-located seismometers VBB and SP is installed on the surface of Mars as part of NASA's InSight 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. The daily atmospheric temperature variations of approx. 80K are attenuated by different insulation layers to approx. 15K peak-to-peak at the sensor level. Typical wind speeds vary between 0 and 5 m/s leading to a diurnal variation in the broad-band rms noise level by two orders of magnitude. One ubiquitous artifact in the raw broad-band data is an abundance of one-sided, transient pulses often accompanied by high-frequency spikes. We show that 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 intermittent stress relaxation events internal to SEIS caused by the large diurnal temperature variations to which the instrument is exposed during a Martian sol. Only a small fraction of glitches correspond to a motion of the SEIS package as a whole caused by minuscule tilts of the instrument. Whilst such kind of data disturbances are typically discarded when occurring in terrestrial data, this is no option for the data returned from the Red Planet. We therefore do not only demonstrate their effects on the seismic data and analyze their origins, but also propose algorithms that are able to detect and remove many of these (mostly) non-seismic signals. We further published our codes (both Python and MATLAB) so that interested researchers can make their own choices on how to treat the data and to which extent

Mars from the InSight: Seismology Beyond Earth

International audienc

SEIS first year: nm/s^2 (and less) broadband seismology on Mars and first steps in Mars-Earth-Moon comparative seismology. (Invited)

AGU Fall Meeting 2019 in San Francisco , 9-13 December 2019EIS/InSIght teamInSight 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