On May 4th, 2022 the InSight seismometer SEIS recorded the largest marsquake ever observed, S1222a, with an initial magnitude estimate of Mw 4.7. Understanding the depth and source properties of this event has important implications for the nature of tectonic activity on Mars. Located ~37 degrees to the southeast of InSight, S1222a is one of the few non-impact marsquakes that exhibits prominent ratio surface waves. We use waveform modeling of body waves (P and S) and surface waves (Rayleigh and Love) to constrain the moment tensor and quantify the associated uncertainty. We find that S1222a likely resulted from dip-slip faulting in the mid-crust (source depth ~18 – 28 km) and estimate a scalar moment of 3.51015 – 5.01015 Nm (magnitude Mw 4.3 – 4.4). The best-fitting focal mechanism is sensitive to the choice of phase windows and misfit weights, as well as the structural model of Mars used to calculate Green’s functions. We find that an E-W to SE-NW striking thrust fault can explain the data well, although depending on the choice of misfit weighting, a normal fault solution is also permissible. The orientation of the best-fitting fault plane solutions suggests that S1222a takes place on a fault system near the martian crustal dichotomy accommodating relative motion between the northern lowlands and southern highlands. Independent constraints on the event depth and improved models of the (an)isotropic velocity structure of the martian crust and mantle could help resolve the ambiguity inherent to single-station moment tensor inversions of S1222a and other marsquakes.

Future mission carrying seismometer payloads to icy ocean worlds will measure global and local seismicity to determine where the ice shell is seismically active. We use two locations, a seismically active site on Gulkana Glacier, Alaska, and a more seismically quiet site on the northwestern Greenland Ice Sheet as geophysical analogs. We compare the performance of a single-station seismometer against a small-aperture seismic array to detect both high (> 1 Hz) and low (< 0.1 Hz) frequency events at each site. We created catalogs of high frequency (HF) and low frequency (LF) seismicity at each location using the automated Short-Term Average/ Long-Term Average technique. We find that with a 2-meter small-aperture seismic array, our detection rate increased (9 % for Alaska, 46% for Greenland) over the single-station approach. At Gulkana, we recorded an order of magnitude greater HF events than the Greenland site. We ascribe the HF events sources to a combination of icequakes, rockfalls, and ice-water interactions, while very high frequency events are determined to result from bamboo poles that were used to secure gear. We further find that local environmental noise reduces the ability to detect low-frequency global tectonic events. Based upon this study, we recommend that future missions consider the value of the expanded capability of a small array compared to a single station, design detection algorithms that can accommodate variable environmental noise, and assess the potential landings sites for sources of local environmental noise that may limit detection of global events.

In 2007, the National Academies designated “understanding the structure & composition of the lunar interior” (to provide fundamental information on the evolution of a differentiated planetary body) as the second highest lunar science priority that needed to be addressed. Here we present the current status of the planned response of the Lunar Geophysical Network (LGN) team to the upcoming New Frontiers-5 AO. The Moon represents an end-member in the differentiation of rocky planetary bodies. Its small size (and heat budget) means that the early stages of differentiation have been frozen in time. But despite the success of the Apollo Lunar Surface Experiment Package (ALSEP), significant unresolved questions remain regarding the nature of the lunar interior and tectonic activity. General models of the processes that formed the present-day lunar interior are currently being challenged. While reinterpretation of the Apollo seismic data has led to the identification of a lunar core, it has also produced a thinning of the nearside lunar crust from 60-65 km in 1974 to 30-38 km today. With regard to the deep mantle, Apollo seismic data have been used to infer the presence of garnet below ~500 km, but the same data have also been used to identify Mg-rich olivine. A long-lived global lunar geophysical network (seismometer, heat flow probe, magnetometer, laser retro-reflector) is essential to defining the nature of the lunar interior and exploring the early stages of terrestrial planet evolution, add tremendous value to the GRAIL and SELENE gravity data, and allow other nodes to be added over time (ie, deliver the International Lunar Network). Identification of lateral and vertical heterogeneities, if present within the Moon, will yield important information about the early presence of a global lunar magma ocean (LMO) as well as exploring LMO cumulate overturn. LGN would also provide new constraints on seismicity, including shallow moonquakes (the largest type identified by ALSEP with magnitudes between 5-6) that have been linked to young thrust fault scarps, suggesting current tectonic activity. Advancing our understanding of the Moon’s interior is critical for addressing these and many other important lunar and Solar System science and exploration questions, including protection of astronauts from the strong shallow moonquakes.

The Seismometer to Investigate Ice and Ocean Structure (SIIOS) project is exploring the science capabilities of seismometers in ocean world analog environments. Ocean worlds, such as Europa, Enceladus and Titan, have thick global icy shells overlying liquid oceans. The icy shells may be seismically active due to tidal stresses. SIIOS tests several seismometers in a small-aperture array in a mock-lander configuration to quantify the ability to detect, locate, and identify seismic sources, as well as constrain local ice structure. The SIIOS experiment was deployed on two terrestrial analogs for ocean worlds. We first deployed on Gulkana Glacier in Alaska in September 2017, and then deployed in Northwestern Greenland, over a subglacial lake from May 2018-August 2018. Both areas serve as analog locations for Europa due to the layering of ice, water and rock. Gulkana was a relatively noisy site due to surface runoff and drainage, higher topographic variation (inducing rockfalls), and proximity to active plate boundaries. Greenland was a quieter site, in part due to its geologic setting high on the ice sheet, as well as from the installation process. During the Greenland deployment, we covered instruments with a large aluminum box that was buried, thus reducing noise from atmospheric and thermal effects. At both analog sites, the instruments passively recorded seismicity and seismic background noise. The passive data was used to create power spectral density (PSDs) and then probability density functions (PDFs), of the background noise. The PDFs of Gulkana showed higher noise levels compared to those of Greenland. Using the passive data, we detected and identified events originating from ice quakes, and in the case of Gulkana; rockfalls and drainage from a nearby moulin. A frequency-dependent polarization analysis was also conducted to indicate the dominant directionality of the background signals through time. The results indicate how background or ambient signals could be used on ocean worlds to characterize the local seismicity.

Mars is the first extraterrestrial planet with seismometers (SEIS) deployed directly on its surface in the framework of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission. The lack of strong Marsquakes, however, strengthens the need of seismic noise studies to additionally constrain the Martian structure. Seismic noise autocorrelations of single-station recordings permit the determination of the zero-offset reflection response underneath SEIS. We present a new autocorrelation study which employs state-of-the-art approaches to determine a robust reflection response by avoiding bias from aseismic signals which are recorded together with seismic waves due to unfavorable deployment and environmental conditions. Data selection and segmentation is performed in a data-adaptive manner which takes the data root-mean-square amplitude variability into account. We further use the amplitude-unbiased phase cross-correlation and work in the 1.2-8.9 Hz frequency band. The main target are crustal scale reflections, their robustness and convergence. The strongest signal appears at 10.6 s, and, if interpreted as P-wave reflection, would correspond to a discontinuity at about 24 km depth. This signal is a likely candidate for a reflection from the base of the Martian crust due to its strength, polarity, and stability. Additionally we identify, among the stable signals, a signal at about 6.85 s that can be interpreted as a P-wave reflection from the mid-crust at about 9.5 km depth.

Introduction: Ocean Worlds are of high interest to the planetary community [1, 2] due to the potential habitability of their subsurface oceans [3–5]. Over the next few decades several missions will be sent to ocean worlds including the Europa Clipper [6], Dragonfly [7], and possibly a Europa lander [8]. The Dragonfly and Europa lander missions will carry seismic payloads tasked with detecting and locating seismic sources. The Seismometer to Investigate Ice and Ocean Structure (SIIOS) is a NASA PSTAR funded project that investigates ocean world seismology using terrestrial analogs. One goal of the SIIOS experiment is characterizing the local seismic environment of our field sites. Here we present an analysis of detected local events at our field sites at Gulkana Glacier in Alaska and in Northwest Greenland approximately 80 km North of Qaanaaq, Greenland (Fig. 1a). Both field sites passively recorded data for about two weeks. We deployed our experiment on Gulkana Glacier in September 2017 (Fig. 1b) and in Greenland in June 2018 (Fig. 1c). At Gulkana there was a nearby USGS weather station [9] which recorded wind data. Temperature data was collected using the MERRA satellite [10]. In Greenland we deployed our own weather station to collect temperature and wind data. Gulkana represents a noisier and more active environment: Temperatures fluctuated around 0C, allowing for surface runoff to occur during the day. The glacier had several moulins, and during deployment we heard several rockfalls from nearby mountains. In addition to the local environment, Gulkana is located close to an active plate boundary (relative to Greenland). This meant that there were more regional events recorded over two weeks, than in Greenland. Greenland’s local environment was also quieter, and less active: Temperatures remained below freezing. The Greenland ice was much thicker than Gulkana (~850 m [11] versus ~100 m [12, 13]) and our stations were above a subglacial lake. Both conditions can reduce event detections from basal motion. Lastly, we encased our Greenland array in an aluminum vault and buried it beneath the surface unlike our array in Gulkana where the instruments were at the surface and covered with plastic bins. The vault further insulated the array from thermal and atmospheric events. Event Detection and Clustering: To detect local events we filtered the data between 5-20 Hz. Using the Obspy module in python [14], we performed a short-term average/long-term average (STA/LTA) approach to determine where amplitudes spiked. For short term we used 1.5 seconds and 40 seconds and a ratio of 20 to detect events [15]. Through this approach we detect-ed 104 events at our Greenland site and 2252 events at our Gulkana site. The Gulkana site showed a strong correlation with both temperature and changes in temperature, while Greenland did not show this relationship [16]. Once we had a catalog of events, we performed a hierarchal cluster analysis to cluster events.

Typical seismic waveform datasets comprise from hundreds of thousands to several millions records. Compilation is performed by time-consuming handpicking of phase arrival times, or signal processing algorithms such as cross-correlation. The latter generally underperform compared to handpicking. However, inconsistencies across and within handpicked datasets creates disagreement between observations and interpretation of Earth’s structure. Here, we exploit the pattern recognition capabilities of Convolutional Neural Networks (CNN). Using a large global handpicked dataset, we train a CNN model to identify the seismic shear phase SS. This accelerates, automates, and makes consistent data compilation. The CNN model is then employed to identify precursors to SS generated by mantle discontinuities. The model identifies precursors in stacked and individual seismograms, producing new measurements of the mantle transition zone with quality comparable to handpicked data. The capability to rapidly obtain new, high-quality observations has implications for automation of future seismic tomography inversions and body wave studies.

The Earth’s mantle transition zone (MTZ) plays a key role in the thermal and compositional interactions between the upper and lower mantle. Seismic anisotropy provides useful information about mantle deformation and dynamics across the MTZ. However, seismic anisotropy in the MTZ is difficult to obtain from surface wave or shear wave splitting measurements. Here, we investigate the sensitivity to anisotropy of a body wave method, SS precursors, through 3-D synthetic modeling. Our study shows that the SS precursors can distinguish the anisotropy originating from three depths: shallow upper mantle (80-220 km), deep upper mantle above 410-km, and MTZ (410-660 km). Synthetic resolution tests indicate that SS precursors can resolve 3% azimuthal anisotropy where data have an average signal to noise ratio (SNR=7) when azimuthal coverage is sufficient. To investigate regional sensitivity, we apply the stacking and inversion methods to two densely sampled areas: Japan subduction zone and a central Pacific region around the Hawaiian hotspot. We find evidence for a trench-perpendicular fast direction (Θ=87°) of MTZ anisotropy in Japan, but the strength of anisotropy is poorly constrained due to limited azimuthal coverage. We attribute the azimuthal anisotropy to lattice-preferred orientation of wadsleyite induced by trench-parallel mantle flow near the stagnant slab. In the central Pacific study region, there is a non-detection of MTZ anisotropy, although modeling suggests the data coverage should allow us to resolve up to 3% anisotropy. Therefore, the Hawaiian mantle plume does not produce detectable azimuthal anisotropy in the MTZ.

The SEIS seismometer of the InSight mission was deployed on the ground of Elysium Planitia, on 19 December 2018. Interferometry techniques can be used to extract information on the internal structure from the autocorrelation of seismic ambient noise and coda of seismic events. In a single-station configuration, the zero-offset global reflection of the ground vertically below the seismometer can be approximated by the stacked ZZ autocorrelation function (ACF) for P-waves and the stacked EE and NN ACFs for S-waves, assuming a horizontally layered medium and homogeneously distributed and mutually uncorrelated noise sources. We analyze continuous records from the very broadband seismometer (SEIS-VBB), and correct for potential environmental disturbances through systematic preprocessing. For each Sol (martian day), we computed the correlations functions in 24 windows of one martian hour in order to obtain a total correlation tensor for various Mars local times. In addition, a similar algorithm is applied to the Marsquake waveforms in different frequency bands. Both stability analysis and inter-comparison between background noise and seismic event results suggest that the background seismic noise at the landing site is reliably observed only around 2.4 Hz, where an unknown mechanism is amplifying the ground shaking, and only during early night hours, when the noise induced by atmospheric disturbances is minimum. Seismic energy arrivals are consistently observed across the various data-sets. Some of these arrivals present multiples. These observations are discussed in terms of Mars’ crustal structure.