On 4 May 2022 the InSight seismometer SEIS‐VBB recorded the largest marsquake ever observed, S1222a, with an initial magnitude estimate of MWMa ${M}_{W}^{\mathrm{M}\mathrm{a}}$ 4.6. Understanding the depth and source properties of this event has important implications for the nature of tectonic activity on Mars. Located ∼37° to the southeast of InSight, S1222a is one of the few non‐impact marsquakes that exhibits prominent surface waves. We use waveform modeling of body waves (P and S) and surface waves (Rayleigh and Love) to constrain the focal mechanism, assuming a double‐couple source, and find that S1222a likely resulted from reverse faulting in the crust (source depth near 22 km). We estimate the scalar moment to be 2.5 × 1015–3.5 × 1015 Nm (magnitude MW 4.2–4.3). Our results suggest active compressional tectonics near the dichotomy boundary on Mars, likely due to thermal contraction from planetary cooling.

Gravity inversions have contributed greatly to our knowledge of the interior of planetary bodies and the processes that shaped them. However, previous global gravity inversion methods neglect the inference of mantle density anomalies when using techniques to decrease the non‐uniqueness of the inversion. In this work, we present a novel global gravity inversion algorithm, named THeBOOGIe, suited to inferring global‐scale density anomalies within the crust and mantle of planetary bodies. The algorithm embraces the nonuniqueness inherent in gravity inversions by not prescribing at the outset a density interface or depth range of interest. Instead, the method combines a Bayesian approach with a flexible incorporation of prior geological or geophysical information to infer density anomalies at any depth. A validation test using synthetic lunar‐like gravity data shows that THeBOOGIe can constrain the lateral location of crustal density anomalies but tends to overestimate their thicknesses. Importantly, THeBOOGIe can detect deep mantle density anomalies and quantify the level of confidence in the inferred density models. Our results show that THeBOOGIe can provide complementary information to one‐dimensional seismic models of the interior of the terrestrial planets and the Moon by constraining density anomalies that are not spherically symmetric. Additionally, THeBOOGIe is specially suited to constraining the interior of partially differentiated bodies where these large‐scale density anomalies are more likely to exist. Finally, thanks to the flexible use of priors, THeBOOGIe is an essential tool to understand the interior of planetary bodies lacking additional constraints.

We report observations of Rayleigh waves that orbit around Mars up to three times following the S1222a marsquake. Averaging these signals, we find the largest amplitude signals at 30 and 85 s central period, propagating with distinctly different group velocities of 2.9 and 3.8 km/s, respectively. The group velocities constraining the average crustal thickness beneath the great circle path rule out the majority of previous crustal models of Mars that have a >200 kg/m3 density contrast across the equatorial dichotomy between northern lowlands and southern highlands. We find that the thickness of the Martian crust is 42–56 km on average, and thus thicker than the crusts of the Earth and Moon. Considered with the context of thermal evolution models, a thick Martian crust suggests that the crust must contain 50%–70% of the total heat production to explain present‐day local melt zones in the interior of Mars.

Significance Mars has a liquid iron alloy core at its center. Using seismic data gathered by the InSight mission, we have made the first observations of seismic waves traveling through Mars’ core. We use the travel times of core-transiting seismic waves, relative to ones which remain in the mantle, to constrain properties of the core and construct the first models of the elastic properties of the entire planet. Our results are consistent with a core rich in sulfur, with smaller fractions of oxygen, carbon and hydrogen.

Using seismic recordings of event S1222a, we measure dispersion curves of Rayleigh and Love waves, including their first overtones, and invert these for shear velocity (VS) and radial anisotropic structure of the Martian crust. The crustal structure along the topographic dichotomy is characterized by a fairly uniform vertically polarized shear velocity (VSV) of 3.17 km/s between ∼5 and 30 km depth, compatible with the previous study by Kim et al. (2022), https://doi.org/10.1126/science.abq7157. Radial anisotropy as large as 12% (VSH > VSV) is required in the crust between 5 and 40 km depth. At greater depths, we observe a large discontinuity near 63 ± 10 km, below which VSV reaches 4.1 km/s. We interpret this velocity increase as the crust‐mantle boundary along the path. Combined gravimetric modeling suggests that the observed average crustal thickness favors the absence of large‐scale density differences across the topographic dichotomy.

The largest seismic event ever recorded on Mars, with a moment magnitude of 4.7 ± 0.2, is the first event to produce both Love and Rayleigh wave signals. We measured their group velocity dispersion between about 15 and 40 s period and found that no isotropic depth‐dependent velocity model could explain the two types of waves wave simultaneously, likely indicating the presence of seismic anisotropy. Inversions of Love and Rayleigh waves yielded velocity models with horizontally polarized shear waves traveling faster than vertically polarized shear waves in the top 10–25 km. We discuss the possible origins of this signal, including the preferred orientation of anisotropic crystals due to shear deformation, alignment of cracks, layered intrusions due to an impact, horizontal layering due to the presence of a large‐scale sediment layer on top of the crust, and alternation of sedimentation and basalt layers deposits due to large volcanic eruptions.

We detected surface waves from two meteorite impacts on Mars. By measuring group velocity dispersion along the impact-lander path, we obtained a direct constraint on crustal structure away from the InSight lander. The crust north of the equatorial dichotomy had a shear wave velocity of approximately 3.2 kilometers per second in the 5- to 30-kilometer depth range, with little depth variation. This implies a higher crustal density than inferred beneath the lander, suggesting either compositional differences or reduced porosity in the volcanic areas traversed by the surface waves. The lower velocities and the crustal layering observed beneath the landing site down to a 10-kilometer depth are not a global feature. Structural variations revealed by surface waves hold implications for models of the formation and thickness of the martian crust. Description An insightful impact On 24 December 2021, the seismometer for the InSight mission on Mars detected a large seismic event with a distinct signature. Posiolova et al. discovered that the event was caused by a meteor impact on the surface of Mars, which was confirmed by satellite observations of a newly formed 150-kilometer crater. The surface nature and size of the impact allowed Kim et al. to detect surface waves from the event, which have yet to be observed on Mars. These surface waves help to untangle the structure of the Martian crust, which has various amounts of volcanic and sedimentary rock, along with subsurface ice, in different regions of the planet (see the Perspective by Yang and Chen). The characteristics of the impact itself are important because they provide a seismic fingerprint of an impact event that is different from the marsquakes observed so far. —BG A new crater formed on the surface of Mars was detected with the seismometer on the InSight mission.

The shallowest intracrustal layer (extending to 8 ± 2 km depth) beneath the Mars InSight Lander site exhibits low seismic wave velocity, which is likely related to a combination of high porosity and other lithological factors. The SsPp phase, an SV‐ to P‐wave reflection on the receiver side, is naturally suited for constraining the seismic structure of this top crustal layer since its prominent signal makes it observable with a single station without the need for stacking. We have analyzed six broadband and low‐frequency seismic events recorded on Mars and made the first coherent detection of the SsPp phase on the red planet. The timing and amplitude of SsPp confirm the existence of the ∼8 km interface in the crust and the large wave speed (or impedance) contrast across it. With our new constraints from the SsPp phase, we determined that the average P‐wave speed in the top crustal layer is between 2.5 and 3.2 km/s, which is a more precise and robust estimate than the previous range of 2.0–3.5 km/s obtained by receiver function analysis. The low velocity of Layer 1 likely results from the presence of relatively low‐density lithified sedimentary rocks and/or aqueously altered igneous rocks that also have a significant amount of porosity, possibly as much as 22%–30% by volume (assuming an aspect ratio of 0.1 for the pore space). These porosities and average P‐wave speeds are compatible with our current understanding of the upper crustal stratigraphy beneath the InSight Lander site.

Significance The depth and sharpness of a midmantle seismic discontinuity, associated with the phase transition from mineral olivine to its higher-pressure polymorphs, provide essential clues to understanding the temperature and composition of Martian mantle. Using data from NASA’s InSight mission, we examined five marsquakes located 3,400 to 4,400 km away from the InSight lander and observed triplications of the P and S waves that resulted from the interaction with a seismic discontinuity produced by the postolivine transition. Our observations indicate that the Martian mantle is more iron rich than Earth,and both planets have a similar potential temperature. Our geodynamic modeling further constrains the mantle composition and surface heat flow and indicates that the mantle was cold in the early Noachian.

Lunar surface activities during Apollo and terrestrial analogue lunar mission simulations have commonly focused on traverses that prioritize surface observations and sample collection activities. Along the way, geophysical measurements are often made. However, they are not necessarily made in a way that optimizes information about the physical subsurface properties, which is something that geophysics can provide. In 2010, NASA simulated a high-quality multiweek human lunar rover traverse analogue mission in the San Francisco volcanic field in Arizona. The traverse route and associated science station locations were selected based on addressing surface observation and sampling tasks. Geophysical studies were not included in the simulation. We returned to the same field area and obtained data on 19 active seismic refraction geophone lines from the science station locations accessed during the simulation. We analyzed the data to calculate 1D seismic velocity profiles for each of the lines. Results revealed up to seven distinct seismically defined material types, including a nearly ubiquitous veneer of regolith of variable thickness at the surface. Results also provided depth and thickness of the seven material types in the first 60 m of the subsurface at each of the science station locations. These cannot be obtained by geologic observations of the outcrops. Systematic interpretation of the area's overall subsurface stratigraphy was not feasible due to the geophysically nonsystematic nature of the original traverse's prioritization of the science station locations. The added geophysical understanding of a region could drive additional geologic investigations to locate samples of otherwise unknown material through the location of surface exposures or coring. This emphasizes the importance of synchronizing geologic and geophysical research requirements during lunar traverse planning and execution to optimize addressing scientific and utilization questions.

Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid‐ to late‐twentieth century saw the creation of the World‐Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near‐real time recording of ground motion with state‐of‐art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi‐instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.

For over three Earth years the Marsquake Service has been analyzing the data sent back from the Seismic Experiment for Interior Structure—the seismometer placed on the surface of Mars by NASA’s InSight lander. Although by October 2021, the Mars seismic catalog included 951 events, until recently all these events have been assessed as lying within a radius of 100° of InSight. Here we report two distant events that occurred within days of each other, located on the far side of Mars, giving us our first glimpse into Mars’ core shadow zone. The first event, recorded on 25 August 2021 (InSight sol 976), shows clear polarized arrivals that we interpret to be PP and SS phases at low frequencies and locates to Valles Marineris, 146° ± 7° from InSight. The second event, occurring on 18 September 2021 (sol 1000), has significantly more broadband energy with emergent PP and SS arrivals, and a weak phase arriving before PP that we interpret as Pdiff. Considering uncertain pick times and poorly constrained travel times for Pdiff, we estimate this event is at a distance between 107° and 147° from InSight. With magnitudes of MwMa 4.2 and 4.1, respectively, these are the largest seismic events recorded so far on Mars.

Introduction: The Apollo Lunar Surface Experiment Package (ALSEP) recorded lunar seismic activity continuously between 1969 and 1977 [1]. These seismic data provided observations of deep moonquakes (DMQs) manifest as repeating tidallylinked signals from sources located in geographically tight regions, called nests. Sources from the same nest have similar waveforms that are distinct from events originating in other nests. Here we explore the potential of machine learning algorithms such as convolutional neural nets (CNNs) to differentiate between the multiple DMQ event hypocenters. Background: The ALSEP comprised 4 seismic stations placed on the near side of the moon between 1969 and 1972, and continuously collected seismic data and transmitted it in real-time back to Earth until instrument shut-off in 1977 [1]. In that time, the network detected approximately 12,000 seismic events, the most numerous of which are deep moonquakes on the long period seismometers. Deep moonquakes (DMQs) are repeated lunar seismic events occurring at focal depths between 800 km and 1200 km [2]. These events originate from 319 source regions, or clusters, and are observed to have 13.6-day, 27-day, and 206day periodicities, indicating that the build-up and release of tidal stresses caused by the interaction between the Earth, Moon, and Sun play a role in the DMQ source mechanisms [3]. DMQ events have been valuable for determining lunar interior structure, as their arrival times can be used to derive mantle Pand S-wave velocities[4] and other body waves, such as core reflections [5]. The identification and classification of events in the ALSEP data was initially conducted using visual inspection of day-long seismograms [1]. Computational advancements have enabled the application of new techniques that identified more DMQs: a combination of waveform cross-correlation and cluster analysis positively identified 5905 new moonquakes and 88 new DMQ nests [6], and a crosscorrelation algorithm combined with an algorithm to de-glitch Apollo data resulted in 123 new events for the A1 DMQ cluster alone[7]. The Apollo seismic data is difficult to analyze because of low signal to noise ratio and instrument glitches that create spikes and/or gaps in the data time series. Current work: Previously, we used a convolutional neural net (CNN) to identify and classify deep moonquake data. DMQ events from clusters A1 and A8, identified in the most recently updated lunar seismic event catalog [7, 9] and recorded on the Apollo 12 long period (LP) three-component seismometers. Spectrograms were made from these events and used to train several image classification CNNs to identify the difference between an A1 and an A8 DMQ. Seven different models were trained and tested on the spectrograms; despite various modifications to the CNN architecture, the validation accuracies of the CNNs do not increase beyond 70.1% as shown in Figure 1, indicating that the algorithms are not learning effectively. These results imply that image classifier CNNs are inefficient with spectrograms; therefore, a one-dimensional approach might work better.