<p>The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander successfully delivered a geophysical instrument package to the Martian surface on November 26, 2018, including a broadband seismometer called SEIS (Seismic Experiment for Interior Structure). After two years of recording, seismic body waves phases of a small number of high-quality marsquakes have been clearly identified. In this work, we will present how we estimate the body waves arrival times, and how we handle them to constrain the locations of the marsquakes and the interior structure. The inverse problem relies on a Bayesian approach, to investigate a large range of possible locations and interior models. Due to the small number of data, the advantage of using such a method is to provide a quantitative measure of the uncertainties and the non-uniqueness. In order to take into account the strong variations of the crustal thickness due to the crustal dichotomy, and thus consider the seismic lateral variations, which could cause significant misinterpretations, arrival times corrections are added using crustal thickness maps obtained from gravity and topography data.</p><p>&#160;</p>

<p>Since InSight (the Interior Exploration using Geodesy and Heat Transport) landed 26 months ago and deployed an ultra sensitive broadband seismometer(SEIS) on the surface of Mars, around 500 seismic events of diverse variety have been detected, making it possible to directly analyze the subsurface properties of Mars for the very first time. One of the primary goals of the mission is to retrieve the crustal structure below the landing site. Current estimates differ by more than 100% for the average crustal thickness. Since data from orbital gravity measurementsprovide information on relative variations of crustal thickness but not absolute values, this landing site measurement could serve as a tie point to retrieve global crustal structure models. To do so, we propose using a joint inversion of receiver functions and apparent incidence angles, which contain information on absolute S-wave velocities of the subsurface. Since receiver function inversions suffer from a velocity depth trade-off, we in addition exploit a simple relation which defines apparent S-wave velocity as a function of observed apparent P-wave incidence angles to constrain the parameter space. Finally we use the Neighbourhood Algorithm for the inversion of a suitable joint objective function. The resulting ensemble of models is then used to derive the full uncertainty estimates for each model parameter. Before its application on data from InSight mission, we successfully tested the method on Mars synthetics and terrestrial data from various geological settings using both single and multiple events. Using the same method, we have previously been able to constrain the S-wave velocity and depth for the first inter-crustal layer of Mars between 1.7 to 2.1 km/s and 8 to 11 km, respectively. Here we present the results of applying this technique on our selected data set from the InSight mission. Results show that the data can be explained equally well by models with 2 or 3 crustal layers with constant velocities. Due to the limited data set it is difficult to resolve the ambiguity of this bi-modal solution. We therefore investigate information theoretic statistical tests as a model selection criteria and discuss their relevance and implications in seismological framework.</p><div></div><div></div><div></div>

P. Moulik ,1, V. Lekic ,1 B. Romanowicz,2,3,4 Z. Ma,5 A. Schaeffer,6 T. Ho ,7 E. Beucler ,8 E. Debayle,9 A. Deuss,10 S. Durand,9 G. Ekström ,11 S. Lebedev ,7,12 G. Masters,13 K. Priestley,7 J. Ritsema,14 K. Sigloch,15 J. Trampert 10 and A.M. Dziewonski16,† 1Department of Geology, University of Maryland, College Park, MD 20742, USA. E-mail: moulik@caa.columbia.edu 2Berkeley Seismological Laboratory, McCone Hall, University of California, Berkeley, CA 94720, USA 3Institut de Physique du Globe de Paris, 1 Rue Jussieu, F-752382 Paris Cedex 05, France 4College de France, 11 Place Marcelin Berthelot, F-75005 Paris, France 5State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China 6Geological Survey of Canada, Pacific Division, Sidney, BC V8L 4B2, Canada 7Department of Earth Sciences, Bullard Laboratories, University of Cambridge, Cambridge CB30EZ, United Kingdom 8Laboratoire de Planetologie et de Geodynamique, Nantes University, UMR-CNRS 6112, BP92208 F-44322 Nantes, France 9Laboratoire de Geologie de Lyon-Terre, Planete, Environnement, CNRS UMR 5276, Ecole Normale Superieure de Lyon, Universite de Lyon, Universite

A. Khan, S. Ceylan, M. van Driel, D. Giardini, P. Lognonne, H. Samuel, N. Schmerr, S. Stähler, A. C. Duran, Q. Huang, Doyeon Kim, C. Charalambous, J. F. Clinton, P. M. Davis, M. Drilleau, F. Karakostas, V. Lekic, R. R. Maguire, C. Michaut, M. P. Panning, W. T. Pike, B. Pinot, M. Plasman, J.-R. Scholz, R. Widmer-Schnydrig, T. Spohn, S. E. Smrekar, W. B. Banerdt; ETH Zürich, Université de Paris, Institut de Physique du Globe de Paris, CNRS, University of Maryland, Imperial College, UCLA, ISAE SUPAERO, Université de Lyon, Jet Propulsion Laboratory, California Institute of Technology, Max Planck Institute for Solar System Research, Black Forest Observatory, University of Stuttgart, Institute of Planetary Research, German Aerospace Center (DLR).

The long‐wavelength geoid is sensitive to Earth's mantle density structure as well as radial variations in mantle viscosity. We present a suite of inversions for the radial viscosity profile using whole‐mantle models that jointly constrain the variations in density, shear‐ and compressional‐wavespeeds using full‐spectrum tomography. We use a Bayesian approach to identify a collection of viscosity profiles compatible with the geoid, while enabling uncertainties to be quantified. Depending on tomographic model parameterization and data weighting, it is possible to obtain models with either positive‐ or negative‐buoyancy in the large low shear velocity provinces. We demonstrate that whole‐mantle density models in which density and VS variations are correlated imply an increase in viscosity below the transition zone, often near 1,000 km. Many solutions also contain a low‐viscosity channel below 650 km. Alternatively, models in which density is less‐correlated with VS—which better fit normal mode data—require a reduced viscosity region in the lower mantle. This feature appears in solutions because it reduces the sensitivity of the geoid to buoyancy variations in the lowermost mantle. The variability among the viscosity profiles obtained using different density models is indicative of the strong nonlinearities in modeling the geoid and the limited resolving power of the geoid kernels. We demonstrate that linearized analyses of model resolution do not adequately capture the posterior uncertainty on viscosity. Joint and iterative inversions of viscosity, wavespeeds, and density using seismic and geodynamic observations are required to reduce bias from prior assumptions on viscosity variation and scalings between material properties.

Since early February 2019, the SEIS (Seismic Experiment for Interior Structure) seismometer deployed at the surface of Mars in the framework of the InSight mission has been continuously recording the ground motion at Elysium Planitia. In this study, we take advantage of this exceptional data set to put constraints on the crustal properties of Mars using seismic interferometry (SI). To carry out this task, we first examine the continuous records from the very broadband seismometer. Several deterministic sources of environmental noise are identified and specific preprocessing strategies are presented to mitigate their influence. Applying the principles of SI to the single‐station configuration of InSight, we compute, for each Sol and each hour of the martian day, the diagonal elements of the time‐domain correlation tensor of random ambient vibrations recorded by SEIS. A similar computation is performed on the diffuse waveforms generated by more than a hundred Marsquakes. A careful signal‐to‐noise ratio analysis and an inter‐comparison between the two datasets suggest that the results from SI are most reliable in a narrow frequency band around 2.4 Hz, where an amplification of both ambient vibrations and seismic events is observed. The average autocorrelation functions (ACFs) contain well identifiable seismic arrivals, that are very consistent between the two datasets. Interpreting the vertical and horizontal ACFs as, respectively, the P‐ and S‐ seismic reflectivity below InSight, we propose a simple stratified velocity model of the crust, which is mostly compatible with previous results from receiver function analysis. Our results are discussed and compared to recent works from the literature.

SEIS, the seismometer of the InSight mission, which landed on Mars on 26 November 2018, is monitoring the seismic activity of the planet. The goal of the Mars Structure Service (MSS) is to provide, as a mission product, the first average 1‐D velocity model of Mars from the recorded InSight data. Prior to the mission, methodologies have been developed and tested to allow the location of the seismic events and estimation of the radial structure, using surface waves and body waves arrival times, and receiver functions. The paper describes these validation tests and compares the performance of the different algorithms to constrain the velocity model below the InSight station and estimate the 1‐D average model over the great circle path between source and receiver. These tests were performed in the frame of a blind test, during which synthetic data were inverted. In order to propagate the data uncertainties on the output model distribution, Bayesian inversion techniques are mainly used. The limitations and strengths of the methods are assessed. The results show the potential of the MSS approach to retrieve the structure of the crust and underlying mantle. However, at this time, large quakes with clear surface waves have not yet been recorded by SEIS, which makes the estimation of the 1‐D average seismic velocity model challenging. Additional locatable events, especially at large epicentral distances, and development of new techniques to fully investigate the data, will ultimately provide more constraints on the crust and mantle of Mars.

Sequencing for seismic structures Structures illuminated by seismic waves at the core-mantle boundary of the Earth are traditionally found by focusing on a specific target area. Kim et al. used an unsupervised manifold learning algorithm called “the Sequencer” to automatically detect anomalies in seismic data (see the Perspective by Miller). Using this technique, they uncovered structures at the core-mantle boundary across the entire Pacific region all at once. They found many structures previously identified, but also a new, ultra-low-velocity zone beneath the Marquesas Islands. Science, this issue p. 1223; see also p. 1183 An unsupervised machine learning algorithm uncovers the structure of the core-mantle boundary region under the Pacific. Scattering of seismic waves can reveal subsurface structures but usually in a piecemeal way focused on specific target areas. We used a manifold learning algorithm called “the Sequencer” to simultaneously analyze thousands of seismograms of waves diffracting along the core-mantle boundary and obtain a panoptic view of scattering across the Pacific region. In nearly half of the diffracting waveforms, we detected seismic waves scattered by three-dimensional structures near the core-mantle boundary. The prevalence of these scattered arrivals shows that the region hosts pervasive lateral heterogeneity. Our analysis revealed loud signals due to a plume root beneath Hawaii and a previously unrecognized ultralow-velocity zone beneath the Marquesas Islands. These observations illustrate how approaches flexible enough to detect robust patterns with little to no user supervision can reveal distinctive insights into the deep Earth.

Seismology provides important constraints on the structure and dynamics of the deep mantle. Computational and methodological advances in the past two decades improved tomographic imaging of the mantle and revealed the fine-scale structure of plumes ascending from the core-mantle boundary region and slabs of oceanic lithosphere sinking into the lower mantle. We discuss the modeling aspects of global tomography including theoretical approximations, data selection, and model fidelity and resolution. Using spectral, principal component, and cluster analyses, we highlight the robust patterns of seismic heterogeneity, which inform us of flow in the mantle, the history of plate motions, and potential compositionally distinct reservoirs. In closing, we emphasize that data mining of vast collections of seismic waveforms and new data from distributed acoustic sensing, autonomous hydrophones, ocean-bottom seismometers, and correlation-based techniques will boost the development of the next generation of global models of density, seismic velocity, and attenuation. ▪  Seismic tomography reveals the 100-km to 1,000-km scale variation of seismic velocity heterogeneity in the mantle. ▪  Tomographic images are the most important geophysical constraints on mantle circulation and evolution.

THE SURFACE OF THE MOON N. Schmerr1, J. Richardson1,4, R. Ghent2, M. Siegler3, K. Young4, M. Wasser4, P. Whelley1,4, D. Buczkowski5, L. Carter6, C. Connor7, L. Connor7, J. Bleacher4, M. Fouch8, D. Baker4, T. Hurford4, L. Jozwiak5, S. Kruse7, V. Lekic1, A. Naids10, R. Porter11, L. Montesi1, D. C. Richardson1, E. Rumpf12, N. Schorghofer3, J. Sunshine1, S. Goossens4,18, N. Whelley1,4, D. Wyrick9, W. Zhu1, E. Bell1, J. DeMartini1, D. Coan10, D. Akin1, B. Cohen4, E. Mazarico4, C. Neal13, M. Panning14, N. Petro4, B. Strauss4, R. Weber15, T. Glotch16, A. Hendrix3, A. Parker17, and S. Wright3, 1University of Maryland, College Park, MD, USA (nschmerr@umd.edu), 2University of Toronto, Toronto, Canada, 3Planetary Science Institute, Tucson, USA, 4Goddard Space Flight Center, Greenbelt, USA, 5Johns Hopkins Applied Physics Laboratory, Laurel, USA, 6University of Arizona, Tucson, USA, 7University of South Florida, Tampa, USA, 8Samara/Data, Washington DC, USA, 9Southwest Research Institute, San Antonio, USA, 10NASA Johnson Space Center, Houston, USA, 11Northern Arizona University, Flagstaff, USA, 12USGS, Flagstaff, USA, 13Univerity of Notre Dame, Notre Dame, USA, 14Jet Propulsion Laboratory, Pasadena, USA, 15NASA Marshall Space Flight Center, Huntsville, USA, 16Stony Brook University, Stony Brook, USA, 17Southwest Research Institute, Boulder, USA. 18University of Maryland Baltimore County, USA.

Thickness and seismic velocities of sedimentary sequences strongly affect their response during earthquakes, which can prolong and amplify groundmotions. We characterize shallow structure of Atlantic Coastal Plain (ACP) sediments using a passive-seismic approach based on high-frequency P-to-S receiver functions. We map the site-specific fundamental frequency for 64 USArray Transportable Array stations and confirm that the method yields results similar to those from traditional spectral ratio techniques, with fundamental frequencies between 0.1 and 1 Hz. In addition, using sediment S-wave reverberations and P-to-S phase arrival times measured directly from the receiver functions, we invert for average Sand P-wave velocity profiles of the ACP sedimentary strata. We find that VS increases with depth following a power-law relationship (VS ∝ z p ) whereas the increase of VP with depth is more difficult to constrain using converted wave methods; therefore, we choose to use the Brocher (2005) relationship to obtain VP through a VP= VS relationship. Finally, we use the variation of measured S-reverberation amplitudes with depth to validate these velocity profiles. These results have implications for seismic shaking across the ACP, which covers large portions of the eastern United States.