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The analysis of seismic events recorded by NASA's InSight seismometer remains challenging, given their commonly low magnitudes and large epicentral distances, and concurrently, strongly varying background noise. These factors collectively result in low signal-to-noise ratios (SNR) across most event recordings. We use a deep learning denoising approach to mitigate the noise contamination, aiming to enhance the data analysis and the seismic event catalogue. Our systematic tests demonstrate that denoising performs comparable to fine-tuned bandpass filtering at high SNRs, but clearly outperforms it at low SNRs with respect to accurate waveform and amplitude retrieval, as well as onset picking. We review the denoised waveform data of all 98 low-frequency events in the Marsquake Service catalogue version 14, and improve their location when possible through the identification of phase picks and backazimuths, while ensuring consistency with the raw data. We demonstrate that several event waveforms can be explained by marsquake doublets—two similarly strong quakes in spatio-temporal proximity that result in overlapping waveforms at InSight—and we locate them in Cerberus Fossae (CF). Additionally, we identify and investigate aftershocks and an event sequence consisting of numerous relatively high magnitude marsquakes occurring within hours at epicentral distances beyond CF. As a result of this review and interpretation, we extend the catalogue in event numbers (⁠|$+$| 8 per cent), in events with epicentral distances and magnitudes (⁠|$+$| 50 per cent), and events with backazimuths and a resulting full locations (⁠|$+$| 46 per cent), leading to a more comprehensive description of Martian seismicity. [ABSTRACT FROM AUTHOR]

In this paper, we present an unscented Kalman filter (UKF) for fusion of information from an accelerometer, global navigation satellite system (GNSS) instrumentation, and rotational sensor recordings of structural motion. Seismic and structural motions do not only include translations, but further incorporate torsion and twisting of the ground and/or structural components. Accelerometer and GNSS positions are known to be prone to errors introduced by rotation, such as (1) gravitational leakage, (2) misorientation, and (3) antenna pole tilt. In alleviating such effects, we propose fusion of information from six component (6C) data—3C translation and 3C rotation—and demonstrate its applicability for motion tracking on a flexible pedestrian bridge. To simulate a variety of load effects, the bridge was subjected to various sources of excitation such as hammer impulses, jumping, twisting, and running, as well as a combination thereof named the "artificial coupled forcing." The rotation errors of both the accelerometer and GNSS-estimated positions are corrected via a UKF-based fusion. We further identify the modal properties of the monitored bridge, excited by the different excitation sources, using a covariance driven stochastic subspace identification. The twisting of the bridge is shown to be a primary source of rotation errors. These errors ought to be corrected because their order of magnitude can be as large as the actual signal in the case of GNSS positions and up to 10% for accelerometer sensors. We compare the proposed UKF-based fusion for 6C motion tracking against a simplified linear Kalman filter and demonstrate the potential of the former for real-time, broadband, rotation-free displacement, velocity, and rotations tracking. [ABSTRACT FROM AUTHOR]

This qualitative study aimed to delve into the nuanced experiences, challenges, coping mechanisms, and the acceptance journey of parents raising children diagnosed with cerebral palsy. Employing a qualitative methodology, the research involved conducting indepth interviews with parents referred by the Maharlika Charity Foundation. The findings illuminated diverse levels of comprehension regarding cerebral palsy among the participants, with acceptance of the condition seen to evolve gradually over time. Financial constraints emerged as a significant hurdle for parents, compounded by societal stigma and misconceptions surrounding the condition. Despite these obstacles, parents demonstrated remarkable resilience, actively engaging in caregiving and seeking support from their social networks. Family support played a pivotal role in ameliorating both the emotional and financial burdens associated with raising a child with cerebral palsy, emphasizing the importance of a robust support system. The journey towards acceptance of the child's condition emerged as a critical milestone in the parental experience, fostering a sense of adaptation and strength. Through their narratives, parents showcased a spectrum of coping strategies, ranging from practical caregiving approaches to seeking solace in religious faith. The study underscores the necessity of tailored support systems to assist families in navigating the multifaceted challenges inherent in raising a child with cerebral palsy. Ultimately, it sheds light on the resilience and resourcefulness of parents in facing the complexities of caring for a child with special needs within their familial and societal contexts. [ABSTRACT FROM AUTHOR]

Scientists from different disciplines at ETH Zurich are developing a dynamic, harmonised, and user-centred earthquake risk framework for Switzerland, relying on a continuously evolving earthquake catalogue generated by the Swiss Seismological Service (SED) using the national seismic networks. This framework uses all available information to assess seismic risk at various stages and facilitates widespread dissemination and communication of the resulting information. Earthquake risk products and services include operational earthquake (loss) forecasting (OE(L)F), earthquake early warning (EEW), ShakeMaps, rapid impact assessment (RIA), structural health monitoring (SHM), and recovery and rebuilding efforts (RRE). Standardisation of products and workflows across various applications is essential for achieving broad adoption, universal recognition, and maximum synergies. In the Swiss dynamic earthquake risk framework, the harmonisation of products into seamless solutions that access the same databases, workflows, and software is a crucial component. A user-centred approach utilising quantitative and qualitative social science tools like online surveys and focus groups is a significant innovation featured in all products and services. Here we report on the key considerations and developments of the framework and its components. This paper may serve as a reference guide for other countries wishing to establish similar services for seismic risk reduction. [ABSTRACT FROM AUTHOR]

In this article, we demonstrate that a single station can be used to measure the dynamic properties of a structure. The station includes a collocated accelerometer and rotational sensor, hence, can record both three-component translation and three-component rotation and is referred to as the 6C-station within this study. The key advantage of this approach is to provide a fast and simple path to a comprehensive structural health monitoring characterization that is comparable to the use of a traditional approach using a horizontal array of three-component accelerometers. The deployment of newly developed high-quality rotational sensors allows the direct measurement of structural rotations, facilitating the extraction of structural mode shapes. In this work, we show how an established system identification tool, stochastic subspace identification, can be applied to the 6C-station data and characterize modal properties and structural response. Our results are verified and contrasted against standard horizontal and vertical array configurations. The Prime Tower, a high-rise structure in Zürich, serves as a case study. A structural characterization of this building is presented for the first time. We show that a 6C-station is capable of defining the frequencies of this stiff high-rise building with a fidelity that is on par with a five-sensor horizontal array. The mode shapes of the roof can be precisely determined with a confidence margin that is comparable to conventional sensing array solutions. However, the effectiveness of using only a 6C-station is determined by the noise level of the sensors--in particular, the rotational seismometer needs to be of high quality. The results indicate that, owing to the collocation measurement of translation and rotation, a 6C-station can deliver a comprehensive structural monitoring solution with minimum time, effort, and footprint. [ABSTRACT FROM AUTHOR]

The S1222a marsquake detected by InSight on 4 May 2022 was the largest of the mission, at MwMa ${M}_{w}^{Ma}$ 4.7. Given its resemblance to two other large seismic events (S1000a and S1094b), which were associated with the formation of fresh craters, we undertook a search for a fresh crater associated with S1222a. Such a crater would be expected to be ∼300 m in diameter and have a blast zone on the order of 180 km across. Orbital images were targeted and searched as part of an international, multi‐mission effort. Comprehensive analysis of the area using low‐ and medium‐resolution images reveals no relevant transient atmospheric phenomena and no fresh blast zone. High‐resolution coverage of the epicentral area from most spacecraft are more limited, but no fresh crater or other evidence of a new impact have been identified in those images either. We thus conclude that the S1222a event was highly likely of tectonic origin. Plain Language Summary: During its time on Mars, NASA's InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission recorded over 1,300 seismic events, known as "marsquakes." Of these, a number were identified as coming from meteoroid impact cratering events on the surface. The largest event identified by InSight, labeled S1222a, bore some similarities to two large impact events recorded earlier in the mission. In order to investigate whether the S1222a event might also have been caused by an impact event, we undertook a comprehensive search of the region in which the marsquake occurred. We did not identify any fresh craters in the area, implying that the marsquake was likely caused by geological processes. Key Points: The S1222a marsquake detected by InSight on 4 May 2022 somewhat resembled previous impact‐generated eventsWe performed an image search in the estimated source region, using data from multiple Mars orbiter missionsNo new impact crater has been discovered in this area, pointing to a tectonic origin for the quake [ABSTRACT FROM AUTHOR]

Scientists at ETH Zurich from different disciplines are developing a dynamic, harmonised and user-centred earthquake risk framework for Switzerland, relying on a continuously evolving earthquake catalogue generated by the SED using the national seismic networks. This framework uses all available information to assess seismic risk at various stages and facilitates widespread dissemination and communication of the resulting information. Earthquake risk products and services include Operational Earthquake (Loss) Forecasting (OE[L]F), Earthquake Early Warning (EEW), ShakeMaps, Rapid Impact Assessment (RIA), Structural Health Monitoring (SHM), as well as Recovery and Rebuilding Efforts (RRE). Standardisation of products and workflows across various applications is essential for achieving broad adoption, universal recognition, and maximum synergies. In the Swiss dynamic earthquake risk framework, the harmonisation of products into seamless solutions that access the same databases, workflows, and software is a crucial component to ensure standardisation. A user-centred approach utilising quantitative and qualitative social science tools like online surveys and focus groups is a significant innovation featured in all products and services. Here we report on the key considerations and developments of the framework and its components. [ABSTRACT FROM AUTHOR]

We present the first observations of seismic waves propagating through the core of Mars. These observations, made using seismic data collected by the InSight geophysical mission, have allowed us to construct the first seismically constrained models for the elastic properties of Mars' core. We observe core-transiting seismic phase SKS from two farside seismic events detected on Mars and measure the travel times of SKS relative to mantle traversing body waves. SKS travels through the core as a compressional wave, providing information about bulk modulus and density. We perform probabilistic inversions using the core-sensitive relative travel times together with gross geophysical data and travel times from other, more proximal, seismic events to seek the equation of state parameters that best describe the liquid iron-alloy core. Our inversions provide constraints on the velocities in Mars' core and are used to develop the first seismically based estimates of its composition. We show that models informed by our SKS data favor a somewhat smaller (median core radius = 1,780 to 1,810 km) and denser (core density = 6.2 to 6.3 g/cm3) core compared to previous estimates, with a P-wave velocity of 4.9 to 5.0 km/s at the core-mantle boundary, with the composition and structure of the mantle as a dominant source of uncertainty. We infer from our models that Mars' core contains a median of 20 to 22 wt% light alloying elements when we consider sulfur, oxygen, carbon, and hydrogen. These data can be used to inform models of planetary accretion, composition, and evolution. [ABSTRACT FROM AUTHOR]

Earthquake early warning systems (EEWSs) aim to rapidly detect earthquakes and provide timely alerts, so that users can take protective actions prior to the onset of strong ground shaking. The promise and limitations of EEWSs have both been widely debated. On one hand, an operational EEWS could mitigate earthquake damage by triggering potentially cost- and life-saving actions. These range from automated system responses such as slowing down trains to the actions of individuals that receive the alerts and take protective measures. On the other hand, the effectiveness of an EEWS is conditional on the ability to issue warnings that are sufficiently accurate and timely to facilitate an appropriate action. The refinement of earthquake early warning (EEW) algorithms and the installation of denser and faster seismic networks have improved performance; however, the benefit in risk reduction that an EEWS could achieve remains unquantified. In this study, we leverage upon regional event-based probabilistic seismic risk assessment to devise a quantitative and fully customizable framework for evaluating the effectiveness of EEW in mitigating seismic risk. We demonstrate this framework using Switzerland as a testbed, for which we compute and contrast human loss exceedance curves with and without EEW. [ABSTRACT FROM AUTHOR]

NASA's InSight has detected a large magnitude seismic event, labeled S1222a. The event has a moment magnitude of MWMa ${\mathrm{M}}_{\mathrm{W}}^{\text{Ma}}$4.7, with five times more seismic moment compared to the second largest event. The event is so large that features are clearly observed that were not seen in any previously detected events. In addition to body phases and Rayleigh waves, we also see Love waves, minor arc surface wave overtones, and multi‐orbit surface waves. At long periods, the coda event exceeds 10 hr. The event locates close to the North‐South dichotomy and outside the tectonically active Cerberus Fossae region. S1222a does not show any evident geological or tectonic features. The event is extremely rich in frequency content, extending from below 1/30 Hz up to 35 Hz. The event was classified as a broadband type event; we also observe coda decay and polarization similar to that of very high frequency type events. Plain Language Summary: After 3 years of seismic monitoring of Mars by InSight Seismic Experiment for Interior Structure instrument, we detected a marsquake largest ever observed during the mission. The event is larger by factor of 5 in seismic moment compared to previously detected events. With such an energetic event, we discovered various seismic features that was never observed before. For the first time, we were able to detect body waves and surface waves with their overtones. The large variety of detected seismic phases will enable us to probe the internal structure of Mars. Second, the event was located outside a well‐known seismically active region of Cerberus Fossae. This might indicate that event do not come from the same fault system with other major marsquakes. Finally, this event shows simultaneously features of marsquakes that were previously classified into different types. S1222a is classified as a broadband event with a wide frequency range of seismic energy. At the same time, the coda shape and decay at high frequency resembles that of very high frequency type events. It was an open question how different types of marsquakes are excited of what makes such differences and such event will be a key to uncover such mystery of marsquakes. Key Points: InSight detected on 4 May 2022 a MWMa ${\mathrm{M}}_{\mathrm{W}}^{\text{Ma}}$4.7 marsquake, S1222a, which is the largest seismic event detected so farThe exceptional signal‐to‐noise allows multiple phases to be identified, with a rich collection of surface wavesS1222a was located 37° southeast of the InSight landing site and close to the Martian dichotomy boundary [ABSTRACT FROM AUTHOR]

Summary: We take advantage of the new large AlpArray Seismic Network (AASN) as part of the AlpArray research initiative (www.alparray.ethz.ch), to establish a consistent seismicity-catalogue for the greater Alpine region (GAR) for the time period 2016 January 1–2019 December 31. We use data from 1103 stations including the AASN backbone composed of 352 permanent and 276 (including 30 OBS) temporary broad-band stations (network code Z3). Although characterized by a moderate seismic hazard, the European Alps and surrounding regions have a higher seismic risk due to the higher concentration of values and people. For these reasons, the GAR seismicity is monitored and routinely reported in catalogues by a 11 national and 2 regional seismic observatories. The heterogeneity of these data set limits the possibility of extracting consistent information by simply merging to investigate the GAR's seismicity as a whole. The uniformly spaced and dense AASN provides, for the first time, a unique opportunity to calculate high-precision hypocentre locations and consistent magnitude estimation with uniformity and equal uncertainty across the GAR. We present a new, multistep, semi-automatic method to process ∼50 TB of seismic signals, combining three different software. We used the SeisComP3 for the initial earthquake detection, a newly developed Python library ADAPT for high-quality re-picking, and the well-established VELEST algorithm both for filtering and final location purposes. Moreover, we computed new local magnitudes based on the final high-precision hypocentre locations and re-evaluation of the amplitude observations. The final catalogue contains 3293 seismic events and is complete down to local magnitude 2.4 and regionally consistent with the magnitude 3+ of national catalogues for the same time period. Despite covering only 4 yr of seismicity, our catalogue evidences the main fault systems and orogens' front in the region, that are documented as seismically active by the EPOS-EMSC manually revised regional bulletin for the same time period. Additionally, we jointly inverted for a new regional minimum 1-D P -wave velocity model for the GAR and station delays for both permanent station networks and temporary arrays. These results provide the base for a future re-evaluation of the past decades of seismicity, and for the future seismicity, eventually improving seismic-hazard studies in the region. Moreover, we provide a unique, consistent seismic data set fundamental to further investigate this complex and seismically active area. The catalogue, the minimum 1-D P -wave velocity model, and station delays associated are openly shared and distributed with a permanent DOI listed in the data availability section. [ABSTRACT FROM AUTHOR]

NASA's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) seismometer has been recording Martian seismicity since early 2019, and to date, over 1,300 marsquakes have been cataloged by the Marsquake Service (MQS). Due to typically low signal‐to‐noise ratios (SNR) of marsquakes, their detection and analysis remain challenging: while event amplitudes are relatively low, the background noise has large diurnal and seasonal variations and contains various signals originating from the interactions of the local atmosphere with the lander and seismometer system. Since noise can resemble marsquakes in a number of ways, the use of conventional detection methods for catalog curation is limited. Instead, MQS finds events through manual data inspection. Here, we present MarsQuakeNet (MQNet), a deep convolutional neural network for the detection of marsquakes and the removal of noise contamination. Based on three‐component seismic data, MQNet predicts segmentation masks that identify and separate event and noise energy in time‐frequency domain. As the number of cataloged MQS events is small, we combine synthetic event waveforms with recorded noise to generate a training data set. We apply MQNet to the entire continuous 20 samples‐per‐second waveform data set available to date (>1,000 Martian days), for automatic event detection and for retrieving denoised amplitudes. The algorithm reproduces all high quality, as well as majority of low quality events in the manual, carefully curated MQS catalog. Furthermore, MQNet detects ∼60% additional events that were previously unknown with mostly low SNR, that are verified in manual review. Our analysis on the event rate confirms seasonal trends and shows a substantial increase in the second Martian year. Plain Language Summary: Interior Exploration using Seismic Investigations, Geodesy and Heat Transport's seismometer on Mars has recorded over 1,300 marsquakes since its full deployment in early 2019. Marsquakes are often weak compared to the seismic background noise, which makes their detection and analysis challenging. For this reason, the current event catalog relies on identifying events in manual data review, which can result in an inconsistent event catalog with weak events being missed and quality standards changing over time. In this study, we use a type of artificial neural network for the automatic detection of marsquakes and to separate even signal and background noise. Since these artificial neural networks usually require many examples to learn from but the number of known marsquakes is low, we generate synthetic marsquake examples to train the network. We run the detection algorithm across the mission and compare its performance to the manually compiled event catalog: the algorithm can also detects the majority of identified marsquakes and additionally finds many weaker, previously missing events, thereby extending the number of known marsquakes by ∼60%, from 1,297 to 2,079. Further, our results show substantial variations in event numbers throughout the mission. Key Points: Marsquakes recorded by Interior Exploration using Seismic Investigations, Geodesy and Heat Transport's seismometer are challenging to detect and analyze due to typically low signal‐to‐noise‐ratioWe present MarsQuakeNet—a convolutional neural network for marsquake detection and denoising—trained on synthetic dataOur catalog is consistent with existing manual catalog, extends it by 60% and confirms significant changes in event rate across Martian years [ABSTRACT FROM AUTHOR]

With the availability of low-cost, mass-market dual-frequency GNSS (Global Navigation Satellite System) receivers, standalone processing methods such as Precise Point Positioning (PPP) are no longer restricted to geodetic-grade GNSS equipment only. However, with cheaper equipment, data quality is expected to degrade. This same principle also affects low-cost GNSS antennas, which usually suffer from poorer multipath mitigation and higher antenna noise compared to their geodetic-grade counterparts. This work assesses the quality of a particular piece of low-cost GNSS equipment for real-time PPP and high-rate dynamic monitoring applications, such as strong-motion seismology. We assembled the u-blox ZED-F9P chip in a small and light-weight data logger. With observational data from static experiments—which are processed under kinematic conditions—we assess the precision and stability of the displacement estimates. We tested the impact of different multi-band antenna types, including geodetic medium-grade helical-type (JAVAD GrAnt-G3T), as well as a low-cost helical (Ardusimple AS-ANT2B-CAL) and a patch-type (u-blox ANN-MB) antenna. Besides static tests for the assessment of displacement precision, strong-motion dynamic ground movements are simulated with a robot arm. For cross-validation, we collected measurements with a JAVAD SIGMA G3T geodetic-grade receiver. In terms of precision, we cross-compare the results of three different dual-frequency, real-time PPP solutions: (1) an ambiguity-float solution using the Centre National d'Études Spatiales (CNES) open-source software, (2) an ambiguity-float and an AR (ambiguity-resolved) solution using the raPPPid software from TU Vienna, and (3) and a PPP-RTK solution using the u-blox PointPerfect positioning service. We show that, even with low-cost GNSS equipment, it is possible to obtain a precision of one centimeter. We conclude that these devices provide an excellent basis for the densification of existing GNSS monitoring networks, as needed for strong-motion seismology and earthquake-early-warning. [ABSTRACT FROM AUTHOR]

The goal of earthquake early warning (EEW) is to issue an alert before the damaging seismic waves of an earthquake hit a given exposure. We develop a framework to evaluate the EEW performance in a loss-based context. We use warning time as a key performance indicator and determine statistics of warning time by loss severity to assess the rate and consistency with which an EEW system can deliver timely alerts. In the second part of this work, we develop a Genetic Algorithm approach to optimize an existing sensor network by proposing sites for new stations to enhance the EEW performance in damaging earthquakes. We demonstrate this framework for Switzerland using 2000 realizations of a 50-yr-long stochastic earthquake catalog, which samples the earthquake rate forecast of the Swiss Hazard Model in space and time. For each of the almost 24k earthquake scenario ruptures (5.0 ≤ M ≤ 7.4), we predict shaking intensities and losses (here, fatalities and injuries) at the largest Swiss cities. We find that the current Swiss Seismic Network could provide positive warning times to the affected sites for about 80% of very damaging earthquakes (≥100 fatalities) and for around 85% of earthquakes with ≥10 fatalities. Warning times of >5 s could be achieved for about 40%-55% of very damaging earthquakes and >10 s for about 35%. For around 50% of events with ≥1 fatality (≥10 injuries), EEW could provide >15 s of warning. The greatest benefit of EEW is expected in Zürich, where the population density and, consequently, absolute long-term expected losses are highest and the warning times for damaging earthquakes are often long (>15 s). Densifying the Swiss Seismic Network with additional stations can increase warning times in selected scenarios by up to 5 s. However, because it is already very dense (7 ± 5 km interstation distance), decreasing data latencies (currently ∼2 s) may be more important. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

In preparation for the National Aeronautics and Space Administration Interior exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) Discovery Program mission, Böse et al. (2018) calibrated magnitude scales for marsquakes that incorporated prelaunch knowledge of Mars' interior structure and the expected ambient and instrumental noise. Now, using data collected during the first two years after the successful deployment of the InSight very-broadband seismometer on the Martian surface, we revise these relations to account for the seismic and noise characteristics observed on Mars. The data collected so far (until 12 October 2020) include 485 seismic event detections and suggest that (1) marsquakes are characterized by energy between ~0.1 and 10 Hz; (2) whereas first arriving P- and S-wave phases are regularly identified and assigned, both surface waves and secondary phase arrivals are extremely challenging to identify; (3) the majority of identified events include a strong excitation of an unexpected 2.4 Hz ground resonance; and (4) so-called high-frequency (HF) events exist that are visible mainly as guided Pg/Sg wave trains. In view of these observations, we update our scaling relations for the spectral and body-wave magnitudes, ..., for HF events. We use these scales to determine that the magnitudes of events in the current InSight version 5 catalog range between 1.1 and 3.7, with event-specific uncertainties sM ranging from 0.2 to 0.4. Because of the currently unclear interpretation of HF events, magnitude estimates for these events primarily serve as a relative comparison. [ABSTRACT FROM AUTHOR]

The Seismic Experiment for Interior Structure (SEIS) of the InSight mission to Mars has been providing direct information on Martian interior structure and dynamics of that planet since it landed. Compared with seismic recordings on the Earth, ground-motion measurements acquired by SEIS on Mars are not only made under dramatically different ambient noise conditions, but also include idiosyncratic signals that arise from coupling between different InSight sensors and spacecraft components. This work is to synthesize what is known about these signal types, illustrate how they can manifest in waveforms and noise correlations, and present pitfalls in structural interpretations based on standard seismic analysis methods. We show that glitches (a type of prominent transient signal) can produce artifacts in ambient noise correlations. Sustained signals that vary in frequency, such as lander modes that are affected by variations in temperature and wind conditions over the course of the Martian sol, can also contaminate ambient noise results. Therefore, both types of signals have the potential to bias interpretation in terms of subsurface layering. We illustrate that signal processing in the presence of identified nonseismic signals must be informed by an understanding of the underlying physical processes in order for high-fidelity waveforms of ground motion to be extracted. Whereas the origins of the most idiosyncratic signals are well understood, the 2.4 Hz resonance remains debated, and the literature does not contain an explanation of its fine spectral structure. Even though the selection of idiosyncratic signal types discussed in this article may not be exhaustive, we provide guidance on the best practices for enhancing the robustness of structural interpretations. [ABSTRACT FROM AUTHOR]

The Seismic Experiment for Interior Structures (SEIS) was deployed on Mars in November 2018 and began science operations in March 2019. SEIS is the primary instrument of the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission, which was launched by the National Aeronautics and Space Administration (NASA). The acquisition and control (AC) electronics is a key element of SEIS. The AC acquires the seismic signals of the two sets of seismic sensors with high resolution, stores the data in its local nonvolatile memory for later transmission by the lander, and controls the numerous functions of SEIS. In this article, we present an overview of the AC with its connections to the sensors and to the lander, as well as its functionality. We describe the elements of the acquisition chains and filters, and discuss the performance of the seismic and temperature channels. Furthermore, we outline the safety functions and health monitoring, which are of paramount importance for reliable operation on Mars. In addition, we analyze an artefact affecting the seismic data referred to as the "tick-noise" and provide a method to remove this artefact by post-processing the data. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration's (NASAs) Interior exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) lander successfully touched down on Mars in November 2018, and, for the first time, a seismometer was deployed on the surface of the planet. The seismic recordings reveal diurnal and seasonal changes of the broadband noise level that are consistent with variations of the local atmospheric conditions. The seismic data include a variety of spectral peaks, which are interpreted as wind-excited, mechanical resonances of the lander, resonances of the subsurface, or artifacts produced in the measurement system. Understanding the origin of these signals is critical for the detection and characterization of marsquakes as well as for studies investigating the ambient noise. We identify the major spectral peaks up to 9 Hz, corresponding to the frequency range the most relevant to observed marsquakes. We track the variations in frequency, amplitude, and polarization of these peaks over the duration of the mission so far. The majority of these peaks can readily be classified as measurement artifacts or lander resonances (lander modes), of which the latter have a temperature-dependent peak frequency and a wind-sensitive amplitude. Of particular interest is a prominent resonance at 2.4 Hz, which is used to discriminate between seismic events and local noise and is possibly produced by a subsurface structure. In contrast to the lander modes, the 2.4 Hz resonance has distinctly different features: (1) a broad and stable spectral shape, slightly shifted on each component; (2) predominantly vertical energy; (3) temperature-independent peak frequency; (4) comparatively weak amplification by local winds, though there is a slow change in the diurnal and seasonal amplitude; and (5) excitation during all seismic events that excite this frequency band. Based on these observations, we suggest that the 2.4 Hz resonance is the only mode below 9 Hz that could be related to a local ground structure. [ABSTRACT FROM AUTHOR]

In December 2018, the NASA InSight lander successfully placed a seismometer on the surface of Mars. Alongside, a hammering device was deployed at the landing site that penetrated into the ground to attempt the first measurements of the planetary heat flow of Mars. The hammering of the heat probe generated repeated seismic signals that were registered by the seismometer and can potentially be used to image the shallow subsurface just below the lander. However, the broad frequency content of the seismic signals generated by the hammering extends beyond the Nyquist frequency governed by the seismometer's sampling rate of 100 samples per second. Here, we propose an algorithm to reconstruct the seismic signals beyond the classical sampling limits. We exploit the structure in the data due to thousands of repeated, only gradually varying hammering signals as the heat probe slowly penetrates into the ground. In addition, we make use of the fact that repeated hammering signals are sub‐sampled differently due to the unsynchronized timing between the hammer strikes and the seismometer recordings. This allows us to reconstruct signals beyond the classical Nyquist frequency limit by enforcing a sparsity constraint on the signal in a modified Radon transform domain. In addition, the proposed method reduces uncorrelated noise in the recorded data. Using both synthetic data and actual data recorded on Mars, we show how the proposed algorithm can be used to reconstruct the high‐frequency hammering signal at very high resolution. Key Points: Hammering of the InSight heat probe generates high‐frequency seismic signals that exceed the Nyquist frequency of the seismometerWe developed a new data acquisition and reconstruction workflow that allows for the recovery of the full‐bandwidth hammering signalsDuring hammering, we deliberately turned off the seismometer's anti‐aliasing filters and reconstructed the aliased signal using a sparseness‐promoting algorithm [ABSTRACT FROM AUTHOR]

Since February 2019, NASA's InSight lander is recording seismic signals on the planet Mars, which, for the first time, allows to observe ongoing tectonic processes with geophysical methods. A number of Marsquakes have been located in the Cerberus Fossae graben system in Elysium Planitia and further west, in the Orcus Patera depression. We present a first study of the focal mechanisms of three well‐recorded events (S0173a, S0183a, S0235b) to determine the processes dominating in the source region. We infer for all three events a predominantly extensional setting. Our method is adapted to the case of a single, multicomponent receiver and based on fitting waveforms of P and S waves against synthetic seismograms computed for the initial crustal velocity model derived by the InSight team. We explore the uncertainty due to the single‐station limitation and find that even data recorded by one station constrains the mechanisms (reasonably) well. For the events in the Cerberus Fossae region (S0173a, S0235b) normal faulting with a relatively steep dipping fault plane is inferred, suggesting an extensional regime mainly oriented E‐W to NE‐SW. The fault regime in the Orcus Patera region is not determined uniquely because only the P wave can be used for the source inversion. However, we find that the P and weak S waves of the S0183a event show similar polarities to the event S0173, which indicates similar fault regimes. Plain Language Summary: As time passes, the mysterious interior of Mars is slowly being unraveled due to the detection and analysis of Marsquakes recorded with a seismograph carried by the InSight lander. Close to 400 Marsquakes have so far been identified, yet only a handful of those show similarities to earthquakes. Those earth‐like events are located near the Cerberus Fossae and Orcus Patera regions. We take advantage of the similarity between Marsquakes and earthquakes and apply a methodology developed for earthquake characterization before seismic recorders became abundant on Earth. We find that the Marsquakes in these source regions are dominated by extensional rather than compressing features. This is important information to further understand what causes Marsquakes. Key Points: We infer the tectonic setting in Cerberus Fossae on Mars by seismic source inversionWe present a robust inversion strategy for single‐station moment tensor inversionThree Marsquakes recorded by InSight reveal a predominantly normal faulting regime [ABSTRACT FROM AUTHOR]

The ground motion of an earthquake or the ambient motion of a large engineered structure not only has translational motion, but it also includes rotation around all three axes. No current sensor can record all six components, while the fusion of individual instruments that could provide such recordings, such as accelerometers or Global Navigation Satellite System (GNSS) receivers, and rotational sensors, is non-trivial. We propose achieving such a fusion via a six-component (6C) Kalman filter (KF) that is suitable for structural monitoring applications, as well as earthquake monitoring. In order to develop and validate this methodology, we have set up an experimental case study, relying on the use of an industrial six-axis robot arm, on which the instruments are mounted. The robot simulates the structural motion resulting atop a wind-excited wind turbine tower. The quality of the 6C KF reconstruction is assessed by comparing the estimated response to the feedback system of the robot, which performed the experiments. The fusion of rotational information yields significant improvement for both the acceleration recordings but also the GNSS positions, as evidenced via the substantial reduction of the RMSE, expressed as the difference between the KF predictions and robot feedback. This work puts forth, for the first time, a KF-based fusion for all six motion components, validated against a high-precision ground truth measurement. The proposed filter formulation is able to exploit the strengths of each instrument and recover more precise motion estimates that can be exploited for multiple purposes. [ABSTRACT FROM AUTHOR]