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.

Seismological constraints obtained from receiver function (RF) analysis provide important information about the crust and mantle structure. Here, we explore the utility of the free‐surface multiple of the P‐wave (PP) and the corresponding conversions in RF analysis. Using earthquake records, we demonstrate the efficacy of PPs‐RFs before illustrating how they become especially useful when limited data is available in typical planetary missions. Using a transdimensional hierarchical Bayesian deconvolution approach, we compute robust P‐to‐S (Ps)‐ and PPs‐RFs with InSight recordings of five marsquakes. Our Ps‐RF results verify the direct Ps converted phases reported by previous RF analyses with increased coherence and reveal other phases including the primary multiple reverberating within the uppermost layer of the Martian crust. Unlike the Ps‐RFs, our PPs‐RFs lack an arrival at 7.2 s lag time. Whereas Ps‐RFs on Mars could be equally well fit by a two‐ or three‐layer crust, synthetic modeling shows that the disappearance of the 7.2 s phase requires a three‐layer crust, and is highly sensitive to velocity and thickness of intra‐crustal layers. We show that a three‐layer crust is also preferred by S‐to‐P (Sp)‐RFs. While the deepest interface of the three‐layer crust represents the crust‐mantle interface beneath the InSight landing site, the other two interfaces at shallower depths could represent a sharp transition between either fractured and unfractured materials or thick basaltic flows and pre‐existing crustal materials. PPs‐RFs can provide complementary constraints and maximize the extraction of information about crustal structure in data‐constrained circumstances such as planetary missions.

We investigate the scattering attenuation characteristics of the Martian crust and uppermost mantle to understand the structure of the Martian interior. We examine the energy decay of the spectral envelopes for 21 high-quality Martian seismic events from Sol 128 to Sol 500 of InSight operations. We use the model of Dainty et al. (1974b) to approximate the behavior of energy envelopes resulting from scattered wave propagation through a single diffusive layer over an elastic half-space. Using a grid search, we mapped the layer parameters that fit the observed InSight data envelopes. The single diffusive layer model provided better fits to the observed energy envelopes for High Frequency (HF) and Very High Frequency (VF) than for the Low Frequency (LF) and Broadband (BB) events. This result is consistent with the suggested source depths (Giardini et al., 2020) for these families of events and their expected interaction with a shallow scattering layer. The shapes of the observed data envelopes do not show a consistent pattern with event distance, suggesting that the diffusivity and scattering layer thickness is non-uniform in the vicinity of InSight at Mars. Given the consistency in the envelope shapes between HF and VF events across epicentral distances and the tradeoffs between the parameters that control scattering, the dimensions of the scattering layer remain unconstrained but require that scattering strength decreases with depth and that the rate of decay in scattering strength is fastest near the surface. This is generally consistent with the processes that would form scattering structures in planetary lithospheres.

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 to seismic recordings on Earth, ground motion measurements acquired by SEIS on Mars are made under dramatically different ambient noise conditions, but 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 which 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. While the origins of 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 paper may not be exhaustive, we provide guidance on best practices for enhancing the robustness of structural interpretations.

Single seismometer structure Because of the lack of direct seismic observations, the interior structure of Mars has been a mystery. Khan et al., Knapmeyer-Endrun et al., and Stähler et al. used recently detected marsquakes from the seismometer deployed during the InSight mission to map the interior of Mars (see the Perspective by Cottaar and Koelemeijer). Mars likely has a 24- to 72-kilometer-thick crust with a very deep lithosphere close to 500 kilometers. Similar to the Earth, a low-velocity layer probably exists beneath the lithosphere. The crust of Mars is likely highly enriched in radioactive elements that help to heat this layer at the expense of the interior. The core of Mars is liquid and large, ∼1830 kilometers, which means that the mantle has only one rocky layer rather than two like the Earth has. These results provide a preliminary structure of Mars that helps to constrain the different theories explaining the chemistry and internal dynamics of the planet. Science, abf2966, abf8966, abi7730, this issue p. 434, p. 438, p. 443 see also abj8914, p. 388 Data from the InSight mission on Mars help constrain the structure and properties of the martian interior. For 2 years, the InSight lander has been recording seismic data on Mars that are vital to constrain the structure and thermochemical state of the planet. We used observations of direct (P and S) and surface-reflected (PP, PPP, SS, and SSS) body-wave phases from eight low-frequency marsquakes to constrain the interior structure to a depth of 800 kilometers. We found a structure compatible with a low-velocity zone associated with a thermal lithosphere much thicker than on Earth that is possibly related to a weak S-wave shadow zone at teleseismic distances. By combining the seismic constraints with geodynamic models, we predict that, relative to the primitive mantle, the crust is more enriched in heat-producing elements by a factor of 13 to 20. This enrichment is greater than suggested by gamma-ray surface mapping and has a moderate-to-elevated surface heat flow.

Single seismometer structure Because of the lack of direct seismic observations, the interior structure of Mars has been a mystery. Khan et al., Knapmeyer-Endrun et al., and Stähler et al. used recently detected marsquakes from the seismometer deployed during the InSight mission to map the interior of Mars (see the Perspective by Cottaar and Koelemeijer). Mars likely has a 24- to 72-kilometer-thick crust with a very deep lithosphere close to 500 kilometers. Similar to the Earth, a low-velocity layer probably exists beneath the lithosphere. The crust of Mars is likely highly enriched in radioactive elements that help to heat this layer at the expense of the interior. The core of Mars is liquid and large, ∼1830 kilometers, which means that the mantle has only one rocky layer rather than two like the Earth has. These results provide a preliminary structure of Mars that helps to constrain the different theories explaining the chemistry and internal dynamics of the planet. Science, abf2966, abf8966, abi7730, this issue p. 434, p. 438, p. 443 see also abj8914, p. 388 Data from the InSight mission on Mars help constrain the structure and properties of the martian mantle. A planet’s crust bears witness to the history of planetary formation and evolution, but for Mars, no absolute measurement of crustal thickness has been available. Here, we determine the structure of the crust beneath the InSight landing site on Mars using both marsquake recordings and the ambient wavefield. By analyzing seismic phases that are reflected and converted at subsurface interfaces, we find that the observations are consistent with models with at least two and possibly three interfaces. If the second interface is the boundary of the crust, the thickness is 20 ± 5 kilometers, whereas if the third interface is the boundary, the thickness is 39 ± 8 kilometers. Global maps of gravity and topography allow extrapolation of this point measurement to the whole planet, showing that the average thickness of the martian crust lies between 24 and 72 kilometers. Independent bulk composition and geodynamic constraints show that the thicker model is consistent with the abundances of crustal heat-producing elements observed for the shallow surface, whereas the thinner model requires greater concentration at depth.

Single seismometer structure Because of the lack of direct seismic observations, the interior structure of Mars has been a mystery. Khan et al., Knapmeyer-Endrun et al., and Stähler et al. used recently detected marsquakes from the seismometer deployed during the InSight mission to map the interior of Mars (see the Perspective by Cottaar and Koelemeijer). Mars likely has a 24- to 72-kilometer-thick crust with a very deep lithosphere close to 500 kilometers. Similar to the Earth, a low-velocity layer probably exists beneath the lithosphere. The crust of Mars is likely highly enriched in radioactive elements that help to heat this layer at the expense of the interior. The core of Mars is liquid and large, ∼1830 kilometers, which means that the mantle has only one rocky layer rather than two like the Earth has. These results provide a preliminary structure of Mars that helps to constrain the different theories explaining the chemistry and internal dynamics of the planet. Science, abf2966, abf8966, abi7730, this issue p. 434, p. 438, p. 443 see also abj8914, p. 388 Data from the InSight mission on Mars help constrain the structure and properties of the martian interior. Clues to a planet’s geologic history are contained in its interior structure, particularly its core. We detected reflections of seismic waves from the core-mantle boundary of Mars using InSight seismic data and inverted these together with geodetic data to constrain the radius of the liquid metal core to 1830 ± 40 kilometers. The large core implies a martian mantle mineralogically similar to the terrestrial upper mantle and transition zone but differing from Earth by not having a bridgmanite-dominated lower mantle. We inferred a mean core density of 5.7 to 6.3 grams per cubic centimeter, which requires a substantial complement of light elements dissolved in the iron-nickel core. The seismic core shadow as seen from InSight’s location covers half the surface of Mars, including the majority of potentially active regions—e.g., Tharsis—possibly limiting the number of detectable marsquakes.

<p>The scattering of seismic waves is the signature of random heterogeneities, present in the lithospheric structure of a terrestrial planet. It is the result of refraction and reflection of the seismic waves generated by a quake, when they cross materials with different shear rigidity, bulk modulus, and density and therefore different seismic wave velocities, compared to the ambient space. &#160;On Earth, the seismic waves show relatively weak scattering, identified in later arriving coda waves that follow the main arrivals of body waves and decay with time. In contrast, seismic wave scattering is much more significant on the Moon, where the high heterogeneous structure of the lunar megaregolith, produced through millions of years of impact bombardment, is a structure that creates an extreme scattering environment.</p> <p>The landing of the NASA InSight mission on Mars in 2018, which carried and deployed a seismometer for the first time on the Martian ground, offered a pristine dataset for the investigation and analysis of the characteristics of the scattering attenuation of the Martian crust and uppermost mantle which is important for understanding the structure of the Martian interior. Lognonn&#233; et al. (2020) used a methodology based on the radiative transfer model (Margerin et al., 1998) to offer the first constraints for the scattering and attenuation in the Martian crust. In this study, we performed a further examination based on more and newer events of the Martian Seismic Catalog (InSight Marsquake Service, 2021).</p> <p>The Marsquake Catalog contains events that are categorized according to the frequency content of the seismic signal (Clinton et al., 2021). In this study, we used 19 events of 4 different families, namely the Low Frequency, Broadband, High Frequency, and Very High Frequency events, for our investigation. We focused our investigation on the characteristics of the S-coda waveforms and for this reason, we worked on the respective energy envelopes. We manually picked the envelopes, defining the time window of the S-coda waves, as well the frequency range for each event, directly from the spectrograms of the events' signals, using an appropriately developed visual tool.</p> <p>We used a modeling approach (Dainty et al., 1974) that was developed for the computation of the energy envelopes of shallow events (Lunar impacts) and a diffusive, highly scattering layer, sitting over an elastic half-space. The energy envelope depends on the thickness of the diffusive layer, the range of the seismic ray, the diffusivity and the attenuation in the top layer, and the seismic wave velocity in the underneath elastic half-space. We analyzed all the tradeoffs between the terms of the modeling equation, namely the geometrical relationship of the velocity contrast between the diffusive layer and the elastic half-space with the seismic ray range and the diffusive layer thickness, the diffusivity with the diffusive layer thickness, and between the diffusivity and the velocity contrast of the two examined layers.</p> <p>The presence of the aforementioned tradeoffs made the definition of a unique model a very hard task, as the information for the azimuthal characteristics of the signal is not available for the examined events. This is a limitation that exists in seismology only while working with one station, with the InSight seismometer being the only station on a planet, and the amplitude of the seismic signal is not big enough to perform a specific polarization analysis and derive the azimuthal origin of the recorded signal. For this reason, we reviewed the fit between the modeling and the data, depending on the frequency content of the events.</p> <p>The Low Frequency and the Broadband events, which have a frequency content mainly below the tick noise detected at 1 Hz, could not satisfy the modeling approach of a simple diffusive layer. The spectral envelopes of the S-coda waves of these events are decaying very rapidly, which suggests an origin in a more elastic environment. This is in agreement with previous studies (Giardini et al., 2020) that suggest that these events are generated deeper in the Martian mantle. For this reason, we applied another approach to these signals, with an energy envelope equation designed for deep moonquakes (Dainty et al.,1974), but it was not either capable to fit the examined data envelopes, suggesting the absence of a very thick megaregolith structure on Mars.</p> <p>Based on the results of the High Frequency (HF) and Very High Frequency (VF) events we observed a range of possible paths and diffusivities that can satisfy the data and we investigated the tradeoffs between the parameters of a modeling equation that control the shape of the energy envelope for the events. The analysis of these tradeoffs does not permit us to make any assumptions about the depth of the diffusive region in the Martian crust and the upper mantle as their azimuthal characteristics are unknown and therefore it is not feasible to tell if the difference in the result analysis reflects vertical or lateral variations of the uppermost diffusive layer in the Martian lithosphere.</p> <p>The results of this study illustrate one of the challenges in working with single-station seismic data where event location information, including distance, azimuth, and depth are crucial for understanding the lateral variation in seismic properties of a planet. The existence of a seismic network on the planetary scale will improve the ability of phase peaking and location identification of the events and therefore it will give additional constraints for a similar analysis.</p> <p>References</p> <p>Clinton, J. F. et al. (2021). The Marsquake catalogue from InSight, sols 0&#8211;478.Phys Earth Planet In, 310:106595.</p> <p>Dainty, &#160;A. M. et al. (1974). Seismic scattering and shallow structure of the Moon in Oceanus Procellarum.The Moon,9(1-2):11&#8211;29.</p> <p>Giardini, D. et al. (2020). The seismicity of Mars.Nat Geosci, 13(3):205&#8211;212.InSight Marsquake Service (2021).&#160;</p> <p>Mars Seismic Catalogue, InSight Mission; V5 2021-01-04.</p> <p>Lognonn&#233;, P. et al. (2020). &#160;Constraints on the shallow elastic and anelastic structure of Mars from InSight seismic data.NatGeosci, 13(3):213&#8211;220.</p> <p>Margerin, L. et al. (1998). &#160;Radiative transfer and diffusion of waves in a layered medium: new insight into coda Q.GeophysJ Int, 134(2):596&#8211;612.</p>

The critical zone (CZ) is the region of the Earth’s surface that extends from the bottom of the weathered bedrock to the tree canopy and is important because of its ability to store water and support ecosystems. A growing number of studies use active source shallow seismic refraction to explore and define the size and structure of the CZ across landscapes. However, measurement uncertainty and model resolution at depth are generally not evaluated, which makes the identification and interpretation of CZ features inconclusive. To reliably resolve seismic velocity with depth, we implement a Transdimensional Hierarchical Bayesian (THB) framework with reversible‐jump Markov Chain Monte Carlo to generate samples from the posterior distribution of velocity structures. We also perform 2D synthetic tests to explore how well THB traveltime inversion can resolve different subsurface velocity structures. We find that THB recovers both sharp changes in velocity as well as gradual velocity increases with depth. Furthermore, we explore the velocity structure in a series of ridge‐valley systems in northern California. The posterior velocity model shows an increasing thickness of low velocity material from channels to ridgetops along a transect parallel to bedding strike, implying a deeper weathering zone below ridgetops and hillslopes than below channels. The THB method enhances the ability to reliably image CZ structure, and the model uncertainty estimates it yields provides an objective way to interpret deep CZ structure. The method can be applied across other near‐surface studies, especially in the presence of significant surface topography.

Mars is the first extraterrestrial planet with seismometers (Seismic Experiment for Interior Structure, SEIS) deployed directly on its surface in the framework of the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) 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 a P‐wave reflection, would correspond to a discontinuity at about 21 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 6.15 s that can be interpreted as the P‐wave reflection from the mid‐crust at about 9.5 km depth.

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