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Maren Böse

Bio: Maren Böse is an academic researcher from Swiss Seismological Service. The author has contributed to research in topics: Mars Exploration Program & Warning system. The author has an hindex of 24, co-authored 64 publications receiving 1709 citations. Previous affiliations of Maren Böse include École Polytechnique Fédérale de Lausanne & ETH Zurich.


Papers
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Journal ArticleDOI
TL;DR: The term "earthquake early warning" (EEW) is used to describe real-time earthquake information systems that have the potential to provide warning prior to significant ground shaking as discussed by the authors.
Abstract: The term "earthquake early warning" (EEW) is used to describe real-time earthquake information systems that have the potential to provide warning prior to significant ground shaking. This is possible by rapidly detecting the energy radiating from an earthquake rupture and estimating the resulting ground shaking that will occur later in time either at the same location or some other location. Warning times range from a few seconds to a little more than a minute and are primarily a function of the distance of the user from the earthquake epicenter. The concept has been around for as long as we have had electric communications (e.g., Cooper 1868), but it is only in the last two decades that the necessary instrumentation and methodologies have been developed (e.g., Nakamura 1988; Espinosa-Aranda et al. 1995). The last five years in particular have seen a rapid acceleration in the development and implementation of EEW, fueled by a combination of seismic network expansion, methodological development, and awareness of the increasing threat posed by earthquakes paired with desire by the seismological community to reduce risk.

285 citations

Journal ArticleDOI
TL;DR: The science goals of the experiment and the rationale used to define its requirements are described, and the hardware, from the sensors to the deployment system and associated performance, including transfer functions of the seismic sensors and temperature sensors are described.
Abstract: By the end of 2018, 42 years after the landing of the two Viking seismometers on Mars, InSight will deploy onto Mars’ surface the SEIS (Seismic Experiment for Internal Structure) instrument; a six-axes seismometer equipped with both a long-period three-axes Very Broad Band (VBB) instrument and a three-axes short-period (SP) instrument. These six sensors will cover a broad range of the seismic bandwidth, from 0.01 Hz to 50 Hz, with possible extension to longer periods. Data will be transmitted in the form of three continuous VBB components at 2 sample per second (sps), an estimation of the short period energy content from the SP at 1 sps and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams will be augmented by requested event data with sample rates from 20 to 100 sps. SEIS will improve upon the existing resolution of Viking’s Mars seismic monitoring by a factor of $\sim 2500$ at 1 Hz and $\sim 200\,000$ at 0.1 Hz. An additional major improvement is that, contrary to Viking, the seismometers will be deployed via a robotic arm directly onto Mars’ surface and will be protected against temperature and wind by highly efficient thermal and wind shielding. Based on existing knowledge of Mars, it is reasonable to infer a moment magnitude detection threshold of $M_{{w}} \sim 3$ at $40^{\circ}$ epicentral distance and a potential to detect several tens of quakes and about five impacts per year. In this paper, we first describe the science goals of the experiment and the rationale used to define its requirements. We then provide a detailed description of the hardware, from the sensors to the deployment system and associated performance, including transfer functions of the seismic sensors and temperature sensors. We conclude by describing the experiment ground segment, including data processing services, outreach and education networks and provide a description of the format to be used for future data distribution.

255 citations

Journal ArticleDOI
Philippe Lognonné1, Philippe Lognonné2, William B. Banerdt3, William T. Pike4, Domenico Giardini5, U. R. Christensen6, Raphaël F. Garcia7, Taichi Kawamura2, Sharon Kedar3, Brigitte Knapmeyer-Endrun8, Ludovic Margerin9, Francis Nimmo10, Mark P. Panning3, Benoit Tauzin11, John-Robert Scholz6, Daniele Antonangeli12, S. Barkaoui2, Eric Beucler13, Felix Bissig5, Nienke Brinkman5, Marie Calvet9, Savas Ceylan5, Constantinos Charalambous4, Paul M. Davis14, M. van Driel5, Mélanie Drilleau2, Lucile Fayon, Rakshit Joshi6, B. Kenda2, Amir Khan5, Amir Khan15, Martin Knapmeyer16, Vedran Lekic17, J. B. McClean4, David Mimoun7, Naomi Murdoch7, Lu Pan11, Clément Perrin2, Baptiste Pinot7, L. Pou10, Sabrina Menina2, Sebastien Rodriguez1, Sebastien Rodriguez2, Cedric Schmelzbach5, Nicholas Schmerr17, David Sollberger5, Aymeric Spiga18, Aymeric Spiga1, Simon Stähler5, Alexander E. Stott4, Eléonore Stutzmann2, Saikiran Tharimena3, Rudolf Widmer-Schnidrig19, Fredrik Andersson5, Veronique Ansan13, Caroline Beghein14, Maren Böse5, Ebru Bozdag20, John Clinton5, Ingrid Daubar3, Pierre Delage21, Nobuaki Fuji2, Matthew P. Golombek3, Matthias Grott22, Anna Horleston23, K. Hurst3, Jessica C. E. Irving24, A. Jacob2, Jörg Knollenberg16, S. Krasner3, C. Krause16, Ralph D. Lorenz25, Chloé Michaut26, Chloé Michaut1, Robert Myhill23, Tarje Nissen-Meyer27, J. ten Pierick5, Ana-Catalina Plesa16, C. Quantin-Nataf11, Johan O. A. Robertsson5, L. Rochas28, Martin Schimmel, Sue Smrekar3, Tilman Spohn16, Tilman Spohn29, Nicholas A Teanby23, Jeroen Tromp24, J. Vallade28, Nicolas Verdier28, Christos Vrettos30, Renee Weber31, Don Banfield32, E. Barrett3, M. Bierwirth6, S. B. Calcutt27, Nicolas Compaire7, Catherine L. Johnson33, Catherine L. Johnson34, Davor Mance5, Fabian Euchner5, L. Kerjean28, Guenole Mainsant7, Antoine Mocquet13, J. A Rodriguez Manfredi35, Gabriel Pont28, Philippe Laudet28, T. Nebut2, S. de Raucourt2, O. Robert2, Christopher T. Russell14, A. Sylvestre-Baron28, S. Tillier2, Tristram Warren27, Mark A. Wieczorek18, C. Yana28, Peter Zweifel5 
TL;DR: In this paper, the authors measured the crustal diffusivity and intrinsic attenuation using multiscattering analysis and found that seismic attenuation is about three times larger than on the Moon, which suggests that the crust contains small amounts of volatiles.
Abstract: Mars’s seismic activity and noise have been monitored since January 2019 by the seismometer of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander. At night, Mars is extremely quiet; seismic noise is about 500 times lower than Earth’s microseismic noise at periods between 4 s and 30 s. The recorded seismic noise increases during the day due to ground deformations induced by convective atmospheric vortices and ground-transferred wind-generated lander noise. Here we constrain properties of the crust beneath InSight, using signals from atmospheric vortices and from the hammering of InSight’s Heat Flow and Physical Properties (HP3) instrument, as well as the three largest Marsquakes detected as of September 2019. From receiver function analysis, we infer that the uppermost 8–11 km of the crust is highly altered and/or fractured. We measure the crustal diffusivity and intrinsic attenuation using multiscattering analysis and find that seismic attenuation is about three times larger than on the Moon, which suggests that the crust contains small amounts of volatiles. The crust beneath the InSight lander on Mars is altered or fractured to 8–11 km depth and may bear volatiles, according to an analysis of seismic noise and wave scattering recorded by InSight’s seismometer.

221 citations

Journal ArticleDOI
TL;DR: The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander was deployed in Elysium Planitia on Mars on 26 November 2018 and fully deployed its seismometer by the end of February 2019 as mentioned in this paper.
Abstract: The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission landed in Elysium Planitia on Mars on 26 November 2018 and fully deployed its seismometer by the end of February 2019. The mission aims to detect, characterize and locate seismic activity on Mars, and to further constrain the internal structure, composition and dynamics of the planet. Here, we present seismometer data recorded until 30 September 2019, which reveal that Mars is seismically active. We identify 174 marsquakes, comprising two distinct populations: 150 small-magnitude, high-frequency events with waves propagating at crustal depths and 24 low-frequency, subcrustal events of magnitude Mw 3–4 with waves propagating at various depths in the mantle. These marsquakes have spectral characteristics similar to the seismicity observed on the Earth and Moon. We determine that two of the largest detected marsquakes were located near the Cerberus Fossae fracture system. From the recorded seismicity, we constrain attenuation in the crust and mantle, and find indications of a potential low-S-wave-velocity layer in the upper mantle. Mars is seismically active: 24 subcrustal magnitude 3–4 marsquakes and 150 smaller events have been identified up to 30 September 2019, by an analysis of seismometer data from the InSight lander.

178 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the authors argue that the occurrence of earthquakes is a problem that can be attacked using the fundamentals of statistical physics, and they apply statistical physics associated with phase changes and critical points to a variety of cellular automata models.
Abstract: [1] Earthquakes and the faults upon which they occur interact over a wide range of spatial and temporal scales. In addition, many aspects of regional seismicity appear to be stochastic both in space and time. However, within this complexity, there is considerable self-organization. We argue that the occurrence of earthquakes is a problem that can be attacked using the fundamentals of statistical physics. Concepts of statistical physics associated with phase changes and critical points have been successfully applied to a variety of cellular automata models. Examples include sandpile models, forest fire models, and, particularly, slider block models. These models exhibit avalanche behavior very similar to observed seismicity. A fundamental question is whether variations in seismicity can be used to successfully forecast the occurrence of earthquakes. Several attempts have been made to utilize precursory seismic activation and quiescence to make earthquake forecasts, some of which show promise.

385 citations

Journal ArticleDOI
01 Jul 1962-Nature
TL;DR: Linear Differential Operators By Prof. Cornelius Lanczos as discussed by the authors is a seminal work in the field of linear differential operators, and is a classic example of a linear differential operator.
Abstract: Linear Differential Operators By Prof. Cornelius Lanczos. Pp. xvi + 564. (London: D. Van Nostrand Co., Ltd.; New York: D. Van Nostrand Company, Inc., 1961.) 80s.

366 citations

Journal ArticleDOI
01 Jun 2018-Science
TL;DR: Seismological and geodetic analyses combined to characterize the mainshock and its largest aftershocks, constrain the geometry of this seismic sequence, and shed light on its causal factors found that the earthquake transferred static stress to larger nearby faults, potentially increasing the seismic hazard in the area.
Abstract: The moment magnitude (Mw) 5.5 earthquake that struck South Korea in November 2017 was one of the largest and most damaging events in that country over the past century. Its proximity to an enhanced geothermal system site, where high-pressure hydraulic injection had been performed during the previous 2 years, raises the possibility that this earthquake was anthropogenic. We have combined seismological and geodetic analyses to characterize the mainshock and its largest aftershocks, constrain the geometry of this seismic sequence, and shed light on its causal factors. According to our analysis, it seems plausible that the occurrence of this earthquake was influenced by the aforementioned industrial activities. Finally, we found that the earthquake transferred static stress to larger nearby faults, potentially increasing the seismic hazard in the area.

320 citations

Journal ArticleDOI
W. Bruce Banerdt1, Suzanne E. Smrekar1, Don Banfield2, Domenico Giardini3, Matthew P. Golombek1, Catherine L. Johnson4, Catherine L. Johnson5, Philippe Lognonné6, Philippe Lognonné7, Aymeric Spiga8, Aymeric Spiga6, Tilman Spohn9, Clément Perrin7, Simon Stähler3, Daniele Antonangeli8, Sami W. Asmar1, Caroline Beghein10, Caroline Beghein11, Neil Bowles12, Ebru Bozdag13, Peter Chi11, Ulrich R. Christensen14, John Clinton3, Gareth S. Collins15, Ingrid Daubar1, Véronique Dehant16, Véronique Dehant17, Mélanie Drilleau7, Matthew Fillingim18, William M. Folkner1, Raphaël F. Garcia19, James B. Garvin20, John A. Grant21, Matthias Grott9, Jerzy Grygorczuk, Troy L. Hudson1, Jessica C. E. Irving22, Günter Kargl23, Taichi Kawamura7, Sharon Kedar1, Scott D. King24, Brigitte Knapmeyer-Endrun25, Martin Knapmeyer9, Mark T. Lemmon26, Ralph D. Lorenz27, Justin N. Maki1, Ludovic Margerin28, Scott M. McLennan29, Chloé Michaut6, Chloé Michaut30, David Mimoun19, Anna Mittelholz4, Antoine Mocquet31, Paul Morgan13, Nils Mueller9, Naomi Murdoch19, Seiichi Nagihara32, Claire E. Newman, Francis Nimmo33, Mark P. Panning1, W. Thomas Pike15, Ana-Catalina Plesa9, Sebastien Rodriguez6, Sebastien Rodriguez7, José Antonio Rodríguez-Manfredi34, Christopher T. Russell11, Nicholas Schmerr35, Matthew A. Siegler5, Matthew A. Siegler36, Sabine Stanley37, Eléanore Stutzmann7, Nicholas A Teanby38, Jeroen Tromp22, Martin van Driel3, Nicholas H. Warner39, Renee Weber40, Mark A. Wieczorek 
TL;DR: For example, the first ten months of the InSight lander on Mars revealed a planet that is seismically active and provided information about the interior, surface and atmospheric workings of Mars as mentioned in this paper.
Abstract: NASA’s InSight (Interior exploration using Seismic Investigations, Geodesy and Heat Transport) mission landed in Elysium Planitia on Mars on 26 November 2018. It aims to determine the interior structure, composition and thermal state of Mars, as well as constrain present-day seismicity and impact cratering rates. Such information is key to understanding the differentiation and subsequent thermal evolution of Mars, and thus the forces that shape the planet’s surface geology and volatile processes. Here we report an overview of the first ten months of geophysical observations by InSight. As of 30 September 2019, 174 seismic events have been recorded by the lander’s seismometer, including over 20 events of moment magnitude Mw = 3–4. The detections thus far are consistent with tectonic origins, with no impact-induced seismicity yet observed, and indicate a seismically active planet. An assessment of these detections suggests that the frequency of global seismic events below approximately Mw = 3 is similar to that of terrestrial intraplate seismic activity, but there are fewer larger quakes; no quakes exceeding Mw = 4 have been observed. The lander’s other instruments—two cameras, atmospheric pressure, temperature and wind sensors, a magnetometer and a radiometer—have yielded much more than the intended supporting data for seismometer noise characterization: magnetic field measurements indicate a local magnetic field that is ten-times stronger than orbital estimates and meteorological measurements reveal a more dynamic atmosphere than expected, hosting baroclinic and gravity waves and convective vortices. With the mission due to last for an entire Martian year or longer, these results will be built on by further measurements by the InSight lander. Geophysical and meteorological measurements by NASA’s InSight lander on Mars reveal a planet that is seismically active and provide information about the interior, surface and atmospheric workings of Mars.

299 citations

Journal ArticleDOI
TL;DR: Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning, ground‐motion prediction, seismic tomography, and earthquake geodesy.
Abstract: This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground‐motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data‐driven ML with traditional physical modeling.

287 citations