Seismometer Detection of Dust Devil Vortices by Ground Tilt

We report seismic signals on a desert playa caused by convective vortices and dust devils. The long-period (10-100s) signatures, with tilts of ~10$^{-7}$ radians, are correlated with the presence of vortices, detected with nearby sensors as sharp temporary pressure drops (0.2-1 mbar) and solar obscuration by dust. We show that the shape and amplitude of the signals, manifesting primarily as horizontal accelerations, can be modeled approximately with a simple quasi-static point-load model of the negative pressure field associated with the vortices acting on the ground as an elastic half space. We suggest the load imposed by a dust devil of diameter D and core pressure {\Delta}Po is ~({\pi}/2){\Delta}PoD$^2$, or for a typical terrestrial devil of 5 m diameter and 2 mbar, about the weight of a small car. The tilt depends on the inverse square of distance, and on the elastic properties of the ground, and the large signals we observe are in part due to the relatively soft playa sediment and the shallow installation of the instrument. Ground tilt may be a particularly sensitive means of detecting dust devils. The simple point-load model fails for large dust devils at short ranges, but more elaborate models incorporating the work of Sorrells (1971) may explain some of the more complex features in such cases, taking the vortex winds and ground velocity into account. We discuss some implications for the InSight mission to Mars.Comment: Contributed Article for Bulletin of the Seismological Society of America, Accepted 29th August 201

Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data

Source separation involves the ill-posed problem of retrieving a set of source signals that have been observed through a mixing operator. Solving this problem requires prior knowledge, which is commonly incorporated by imposing regularity conditions on the source signals, or implicitly learned through supervised or unsupervised methods from existing data. While data-driven methods have shown great promise in source separation, they often require large amounts of data, which rarely exists in planetary space missions. To address this challenge, we propose an unsupervised source separation scheme for domains with limited data access that involves solving an optimization problem in the wavelet scattering covariance representation space\unicode{x2014}an interpretable, low-dimensional representation of stationary processes. We present a real-data example in which we remove transient, thermally-induced microtilts\unicode{x2014}known as glitches\unicode{x2014}from data recorded by a seismometer during NASA's InSight mission on Mars. Thanks to the wavelet scattering covariances' ability to capture non-Gaussian properties of stochastic processes, we are able to separate glitches using only a few glitch-free data snippets.Comment: ICML 202

Estimations of the Seismic Pressure Noise on Mars Determined from Large Eddy Simulations and Demonstration of Pressure Decorrelation Techniques for the Insight Mission

The atmospheric pressure fluctuations on Mars induce an elastic response in the ground that creates a ground tilt, detectable as a seismic signal on the InSight seismometer SEIS. The seismic pressure noise is modeled using Large Eddy Simulations (LES) of the wind and surface pressure at the InSight landing site and a Green’s function ground deformation approach that is subsequently validated via a detailed comparison with two other methods: a spectral approach, and an approach based on Sorrells’ theory (Sorrells,Geophys. J. Int. 26:71–82, 1971; Sorrells et al., Nat. Phys. Sci. 229:14–16, 1971). The horizontal accelerations as a result of the ground tilt due to the LES turbulence-induced pressure fluctuations are found to be typically ∼ 2–40 nm/s2 in amplitude, whereas the direct horizontal acceleration is two orders of magnitude smaller and is thus negligible in comparison. The vertical accelerations are found to be ∼ 0.1–6 nm/s2 in amplitude. These are expected to be worst-case estimates for the seismic noise as we use a half-space approximation; the presence at some (shallow) depth of a harder layer would significantly reduce quasi-static displacement and tilt effects. We show that under calm conditions, a single-pressure measurement is representative of the large-scale pressure field (to a distance of several kilometers), particularly in the prevailing wind direction. However, during windy conditions, small-scale turbulence results in a reduced correlation between the pressure signals, and the single-pressure measurement becomes less representative of the pressure field. The correlation between the seismic signal and the pressure signal is found to be higher for the windiest period because the seismic pressure noise reflects the atmospheric structure close to the seismometer. In the same way that we reduce the atmospheric seismic signal by making use of a pressure sensor that is part of the InSight Auxiliary Payload Sensor Suite, we also the use the synthetic noise data obtained from the LES pressure field to demonstrate a decorrelation strategy. We show that our decorrelation approach is efficient, resulting in a reduction by a factor of ∼ 5 in the observed horizontal tilt noise (in the wind direction) and the vertical noise. This technique can, therefore, be used to remove the pressure signal from the seismic data obtained on Mars during the InSight mission

Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders

Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-posed and is further challenged by the variety of time-scales exhibited by sources in time series data. Existing methods typically rely on a preselected window size that limits their capacity to handle multi-scale sources. To address this issue, instead of operating in the time domain, we propose an unsupervised multi-scale clustering and source separation framework by leveraging wavelet scattering covariances that provide a low-dimensional representation of stochastic processes, capable of distinguishing between different non-Gaussian stochastic processes. Nested within this representation space, we develop a factorial Gaussian-mixture variational autoencoder that is trained to (1) probabilistically cluster sources at different time-scales and (2) independently sample scattering covariance representations associated with each cluster. Using samples from each cluster as prior information, we formulate source separation as an optimization problem in the wavelet scattering covariance representation space, resulting in separated sources in the time domain. When applied to seismic data recorded during the NASA InSight mission on Mars, our multi-scale nested approach proves to be a powerful tool for discriminating between sources varying greatly in time-scale, e.g., minute-long transient one-sided pulses (known as ``glitches'') and structured ambient noises resulting from atmospheric activities that typically last for tens of minutes. These results provide an opportunity to conduct further investigations into the isolated sources related to atmospheric-surface interactions, thermal relaxations, and other complex phenomena

Evaluating the Wind-Induced Mechanical Noise on the InSight Seismometers

The SEIS (Seismic Experiment for Interior Structures) instrument onboard the InSight mission to Mars is the critical instrument for determining the interior structure of Mars, the current level of tectonic activity and the meteorite flux. Meeting the performance requirements of the SEIS instrument is vital to successfully achieve these mission objectives. Here we analyse in-situ wind measurements from previous Mars space missions to understand the wind environment that we are likely to encounter on Mars, and then we use an elastic ground deformation model to evaluate the mechanical noise contributions on the SEIS instrument due to the interaction between the Martian winds and the InSight lander. Lander mechanical noise maps that will be used to select the best deployment site for SEIS once the InSight lander arrives on Mars are also presented. We find the lander mechanical noise may be a detectable signal on the InSight seismometers. However, for the baseline SEIS deployment position, the noise is expected to be below the total noise requirement > 97 % of the time and is, therefore, not expected to endanger the InSight mission objectives

RESPACK: An ab initio tool for derivation of effective low-energy model of material

RESPACK is a first-principles calculation software for evaluating the interaction parameters of materials and is able to calculate maximally localized Wannier functions, response functions based on the random phase approximation and related optical properties, and frequency-dependent electronic interaction parameters. RESPACK receives its input data from a band-calculation code using norm-conserving pseudopotentials with plane-wave basis sets. Automatic generation scripts that convert the band-structure results to the RESPACK inputs are prepared for xTAPP and Quantum ESPRESSO. An input file for specifying the RESPACK calculation conditions is designed pursuing simplicity and is given in the Fortran namelist format. RESPACK supports hybrid parallelization using OpenMP and MPI and can treat large systems including a few hundred atoms in the calculation cell

Lunar Seismology: An Update on Interior Structure Models

An international team of researchers gathered, with the support of the Interna- tional Space Science Institute (ISSI), (1) to review seismological investigations of the lunar interior from the Apollo-era and up until the present and (2) to re-assess our level of knowl- edge and uncertainty on the interior structure of the Moon. A companion paper (Nunn et al. in Space Sci. Rev., submitted) reviews and discusses the Apollo lunar seismic data with the aim of creating a new reference seismic data set for future use by the community. In this study, we first review information pertinent to the interior of the Moon that has become available since the Apollo lunar landings, particularly in the past ten years, from orbiting spacecraft, continuing measurements, modeling studies, and laboratory experiments. Fol- lowing this, we discuss and compare a set of recent published models of the lunar interior, including a detailed review of attenuation and scattering properties of the Moon. Common features and discrepancies between models and moonquake locations provide a first esti- mate of the error bars on the various seismic parameters. Eventually, to assess the influence of model parameterisation and error propagation on inverted seismic velocity models, an inversion test is presented where three different parameterisations are considered. For this purpose, we employ the travel time data set gathered in our companion paper (Nunn et al. in Space Sci. Rev., submitted). The error bars of the inverted seismic velocity models demon- strate that the Apollo lunar seismic data mainly constrain the upper- and mid-mantle struc- ture to a depth of ∼1200 km. While variable, there is some indication for an upper mantle low-velocity zone (depth range 100–250 km), which is compatible with a temperature gradi- ◦ent around 1.7 C/km. This upper mantle thermal gradient could be related to the presence of the thermally anomalous region known as the Procellarum Kreep Terrane, which contains a large amount of heat producing elements