Venus and Mars have operated as one-plate planets for some or all of their history and intraplate magmatic activity on Earth has been suggested as an analogue for the observed volcanic activity on these bodies. Flipping the question around, what can we learn about intraplate magmatism on Earth from other planets? Volcanic features, including extensive lava flows and vast lava plains, cover large portions of the Martian surface. Mars has two large volcanic provinces: Tharsis and Elysium. While the continent-sized region of elevated terrain called the Tharsis rise receives most of the attention, Elysium—the second largest volcanic province on Mars—is larger than the Ontong-Java plateau—the largest LIP on Earth. Activity detected by the InSight seismometer near Cerberus Fossae (located in Elysium Planitia, southeast of the Elysium volcanic province) is consistent with fluid flow at depth. Cerberus Fossae is among the youngest tectonic structures on Mars and large discharges of water and lava have been proposed to explain the geomorphic structures observed at Cerberus Fossae. The regional gravity and topography, volcanic history, and seismic activity at Cerberus Fossae are consistent with a present-day 2,000-km-radius plume head beneath Elysium Planitia. The characteristics of the Elysium Planitia plume are comparable to terrestrial plumes proposed to explain the formation of terrestrial LIPs. Plumes on Mars appear to be spatially stable for long periods of time, reflecting the stabilizing influence of a thick stagnant lid and sluggish mantle convection. While Venus is nearly the same size as Earth, there is no evidence supporting Earth-like plate tectonics for the past 250-750 Myrs. The similarity in size invites comparison of present-day volcanic activity between the two planets. This is complicated by the presence of plate tectonics where volcanic activity at ridges and subduction zones has no clear analogue on Venus. Expanding intraplate volcanism on Earth suggests as many as 100 active volcanic events per year on Venus. While detecting surface changes is one goal of the upcoming NASA and ESA Venus missions, surface change associated with volcanic activity has already been found in the Magellan image archive. Herrick and Hemsley identified a 2 km2 volcanic vent that changed shape in the eight months between two Magellan radar images. While sulfuric acid clouds obscure our view of the surface, those same clouds provide the best evidence for ongoing volcanic activity. Assuming the primary mechanism removing atmospheric SO2 is a reaction between calcium minerals on the surface and SO2, an SO2 residence time of ~2 Myrs is required. This requires an outgassing rate of ~6x1010 kg SO2/year—about the same yearly SO2 outgassing rate measured on Earth over the past decade. Converting this outgassing rate to erupted lava, an eruption rate on Venus of ~1 km3/yr is obtained.

Coronae are crown-like, tectono-volcanic features found on Venus that typically range in diameter from 100-700 km. Diapirs of warm upwelling material impinging on the lithosphere are often invoked to explain coronae formation. With more than 500 coronae identified on the surface of Venus, if these diapirs are individually linked to a mantle plume, Venus must have a very different mantle structure than Earth. I consider three cases designed to assess the potential relationship between large-scale, long-wavelength lower mantle structure and smaller-scale upper mantle structure that could potentially form diapirs consistent with those that are envisioned to interact with the lithosphere and form coronae. I use the geoid and topography to identify the large-scale pattern of convection because the geoid contains and integral of the temperature anomalies over the depth of the mantle. Plume tails—narrow vertical conduits—integrate to give a positive geoid anomaly while small-scale, time-dependent drips or upwellings are minimized in the depth integration. The first case—the reference case—has a small, stepwise decreases in viscosity between the lower mantle (1022 Pa s), transition zone (1021 Pa s), and upper mantle (5x1020 Pa s) with no phase transformations. This led to 20 ~1000-km diameter mantle plumes that remained stationary for more than 1.5 Gyr. This calculation is consistent with a number of geophysical observations it does not support the formation of coronae by plume-lithosphere interaction. To decouple the lower and upper mantle, I further decrease both the upper mantle and transition zone viscosities to 1020 Pa s while leaving all other parameters unchanged. In this calculation the same 20 ~1000-km diameter mantle plumes formed and remained stationary for more than 1.5 Gyr. The geoid and topography are anti-correlated, inconsistent with the observed values on Venus and, the spatial scale, number, and topographic evolution of the plumes are not consistent with coronae. This calculation does not support the formation of coronae by plume-lithosphere interaction. In an attempt to further decouple the upper and lower mantle I add an endothermic phase transformation at the ringwoodite-bridgmanite boundary in addition to the decreased upper mantle and transition zone viscosities while leaving all other parameters unchanged. Unique to this calculation, a very large number of ~100-km diameter small topographic upwellings form some associated with the large-scale geoid high but never associated with the large-scale geoid low. The inclusion of a phase transformation decoupling the upper and lower mantle has the potential to create diapir-like structures in the upper mantle consistent with coronae formation.

Low-velocity anomalies in the upper mantle beneath eastern North America, including the Northern Appalachian Anomaly (NAA), the Central Appalachian Anomaly (CAA), and the weaker Southern Coastal Anomaly (SCA), have been characterized by many continent-scale and regional seismic studies. Different models have been proposed to explain their existence beneath the passive margin of eastern North America, variously invoking the past passage of hot spot tracks, modern upwelling due to edge-driven convection, or other processes. Depending on the nature and origin of these anomalies, they may influence, and/or be influenced by, the mantle transition zone (MTZ) structure beneath them. Previous receiver function studies have identified an overall thinner MTZ beneath the eastern margin of the US than beneath the continental interior. In this study, we resolve the MTZ geometry beneath these low-velocity anomalies in unprecedented detail using the scattered wavefield migration technique. We find substantially thinned MTZ beneath the NAA and the CAA, and a moderately thinned MTZ beneath the SCA. In all cases, the thinning is achieved via a minor depression of the 410-km discontinuity and a major uplift of the 660-km discontinuity, which suggests the presence of a series of MTZ-penetrating deep upwellings beneath eastern North America. The upwellings beneath eastern North America and a similar style upwelling beneath Bermuda may initiate from ponded thermally buoyant materials below the MTZ fed by hot return flows from the descending Farallon slab in the deep mantle.

The interior of Venus remains a mystery and, it is challenging to reconcile the available meager observations. The leading theory for the absence of a Venusian magnetic field is that heat within Venus remains trapped beneath the stagnant lid, raising the mantle temperature and limiting flow through the core-mantle boundary. While there are only three surface compositions from the Venera and Vega landers, they are consistent with Mid-Ocean Ridge Basalts (MORB) or Ocean Island Basalts (OIB) implying that in the melting region the mantle of Venus is nothotter than the mantle of Earth. This is a surprising result because stagnant (or squishy) lid planets have hotter interiors than mobile lid planets implying that Venus has not been in the stagnant (or squishy) lid mode of convection for much of its evolution or, another heat transport mechanism—such as heat piping—has played a critical role in the flux of heat through the lithosphere of Venus. We see little change in the geoid or topography power spectra between the calculations suggesting that the presence or absence of lithospheric mobility has only a modest impact on the large-scale geoid or topography. While the patterns of the geoid or topography are not likely to be matched by any convection calculation, the power spectrum is independent of coordinate system and thus, a more robust comparison between calculation and planet. The cases we have found predict a significant heat flux from the core to the mantle—as long as 1000 Myr after an overturn event—inconsistent with the absence of a present-day magnetic field and the estimated age of the surface from cratering, the next step will be to consider a Basal Magma Ocean (BMO) to sequester heat within the core.

SUMMARY Dynamic topography is defined as the deflection of Earth's surface due to the convecting mantle. ASPECT (Advanced Solver for Planetary Evolution, Convection, and Tectonics) is a continually evolving, finite element code that uses modern numerical methods to investigate problems in mantle convection. With ASPECT version 2.0.0 a consistent boundary flux (CBF) algorithm, used to calculate radial stresses at the model boundaries, was implemented into the released version of ASPECT. It has been shown that the CBF algorithm improves the accuracy of dynamic topography calculations by approximately one order of magnitude. We aim to evaluate the influence of the CBF algorithm and explore the geophysical implications of these improved estimates of dynamic topography changes along the East Coast of the United States. We constrain our initial temperature conditions using the tomography models SAVANI, S40RTS and TX2008, and combine them with a corresponding radial viscosity profile (2 for TX2008) and two different boundary conditions for a total of eight experiments. We perform simulations with and without the CBF method, which takes place during post-processing and does not affect the velocity solution. Our dynamic topography calculations are spatially consistent in both approaches, but generally indicate an increase in magnitude using the CBF method (on average ∼15 and ∼76 per cent absolute change in present-day instantaneous and rate of change of dynamic topography, respectively). This enhanced accuracy in dynamic topography calculations can be used to better evaluate the effects of mantle convection on surface processes including vertical land motions, sea level changes, and sedimentation and erosion. We explore results along the US East Coast, where a Pliocene shoreline has been deformed by dynamic topography change. An increased accuracy in estimates of dynamic topography can improve Pleistocene and Pliocene sea level reconstructions, which allow for a better understanding of past sea level changes and ice sheet stability.

Introduction: One of the science priorities of the EnVision mission is to infer the interior structure of Venus, including the properties and thicknesses of its crust, mantle, and core [1]. Measurements of the moment of inertia and length-of-day variations provide critical geodetic constraints to understand the bulk interior structure of the planet. The polar moment of inertia of Venus is inversely proportional to the precession rate. Measuring the precession rate directly from orbit is challenging. For Magellan, spacecraft ephemeris errors dominated the measurement errors and for EnVision very similar challenges are expected. The precession rate itself depends on a number of geodetic parameters, namely the orbital mean motion, the spin rate, the second-degree gravity coefficient, the total mass of the planet, the radius, and the obliquity. To constrain the inertia tensor of Venus and hence to meet the EnVision objectives, the gravity field information must be complemented by measurements of the rotational state. Therefore, augmenting the gravity science solution with surface feature tracking and/or altimetry – abilities that VenSAR offers – can be critical in achieving EnVision science objectives. VenSAR has the capability to make use of globally distributed VenSAR altimetry data and ground-track intersections (cross-over points) to create a dense geodetic net. These observations can be used in concert with gravity observations and SAR images (Figure 1) to improve the a posteriori orbit determination, to solve for the rotation state of Venus including spin axis orientation and precession, and to allow the co-registration of other data products generated by the EnVision mission via improving the overall reference frame of Venus. The precise measurement of the rotation state allows us to infer constraints on the interior structure (e.g., by inferring the moment of inertia) as has been recently demonstrated by ground-based radar observations [2]. However, these recent measurements still provide a weak constraint on the internal density profile and core size, and improved measurements (reducing the uncertainty in the moment of inertia from currently 7% to about 4% and a measurement of the tidal Love number k2) are needed to quantify the interior structure of Venus with precision [3].Figure 1: Second acquisition for VenSAR stereo at each Region of Interest (RoI). Adapted from [1].Methods: As outlined in the ESA red book, in addition to stereo SAR data, VenSAR will acquire a global network of altimetry mode tracks with a vertical resolution of 2.5 m, potentially providing a far better constraint than any previous dataset. The aim of this work is to establish the link between VenSAR and EnVision’s geodetic measurements and the interior structure of Venus. We analyzed the currently planned EnVision orbit and operational profile, identifying regions with repeated coverage, and altimetry intersections. For the interior models, we adapted previous Mercury interior models [4] to Venus, updated with convective thermal profiles for Venus’ mantle. Two sets of models consistent with different geodetic constraints are generated. One set with an inner core, and one without. The set of inner core models is controlled by the inner core radius as a free parameter, in absence of the inner core, the temperature above the melting point is iterated.Figure 2: Zoomed in view on VenSAR altimetry groundtrack spacing and intersections.Results: The ML007 tour has been analyzed, showing promising spatial and temporal coverage. A zoomed in view on the altimetry groundtracks is shown in Figure 3. Further, an initial set of interior models has been assembled for future investigation. An example model without inner core is shown in Figure 4. For the set of forward computed models, we further computed the tidal Love numbers for different mantle rheologies.Figure 3: Example model of Venus interior structure for a model with an inner core.Outlook: Future work we will use the tour analysis of EnVision and the estimated performance of VenSAR to infer the accuracy of the geodetic constraints from VenSAR. These constraints can then be combined with our interior models to refine the expected constraints on Venus interior structure. The assembled framework will help optimizing operation scenarios and to maximize the science return of the EnVision mission.References: [1] ESA (2024). EnVision Definition Study Report (Red Book). [2] Margot, J.-L., et al. (2021). Spin state and moment of inertia of venus. Nature Astronomy, 5(7):676–683. [3] Cascioli, et al. (2021). The determination of the rotational state and interior structure of Venus with VERITAS. PSJ, 2(6):220. [4] Steinbrügge et al. 2021, Challenges on Mercury's interior structure posed by the new measurements of its obliquity and tides, GRL, 48;3.Acknowledgements: A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). This work was supported by NASA’s VENSAR program Grant NNH21ZDA001.

We investigate the geodynamic and melting history of Mars using 3D spherical shell models of mantle convection, constrained by the recent InSight mission results. The Martian mantle must have produced sufficient melt to emplace the Tharsis rise by the end of the Noachian–requiring on the order of 1–3×109 km3 of melt after accounting for limited (~10\%) melt extraction. Thereafter, melting declined, but abundant evidence for limited geologically recent volcanism necessitates some melt even in the cool present-day mantle inferred from InSight data. We test models with two mantle activation energies, and a range of crustal Heat Producing Element (HPE) enrichment factors and initial core-mantle boundary temperatures. We also test the effect of including a hemispheric (spherical harmonic degree-1) step in lithospheric thickness to model the Martian dichotomy. We find that a higher activation energy (350 kJ mol−1) rheology produces present-day geotherms consistent with InSight results, and of those the cases with HPE enrichment factors of 5–10x produce localized melting near or up to present-day. 10x crustal enrichment is consistent with both InSight and geochemical results, and those models also produce present-day geoid power spectra consistent with Mars. However, it is very difficult to produce sufficient melt to form Tharsis in a mantle that also matches the present-day geotherm, without assuming extremely efficient extraction of melt to the surface. The addition of a degree-1 hemispheric dichotomy, as an equatorial step in lithospheric thickness, does not significantly improve upon melt production or the geoid.

Atmospheric AI modeling is increasingly reliant on complex machine learning (ML) techniques and high-dimensional gridded inputs to develop models that achieve high predictive skill. Complex deep learning architectures such as convolutional neural networks and transformers are trained to model highly non-linear atmospheric phenomena such as coastal fog [1], tornadoes [2], and severe hail [3]. The input data is typically in the form of gridded spatial data composed of multiple channels of satellite imagery, numerical weather prediction output, reanalysis products, etc. In many studies, the use of complex architectures and high-dimensional inputs were shown to substantially outperform simpler alternatives. A major challenge when using complex ML techniques is that it is very difficult to understand how the trained model works. The complexity of the model obfuscates the relationship between the input and prediction. It is often of interest to understand a model’s decision-making process. By exposing the model’s behavior, users could verify that the model has learned physically realistic predictive patterns. This information can be used to calibrate trust in the model. The model may have also learned novel patterns within the data that could be used to gain new insights into the atmospheric process. Extracting learned patterns could be used to generate hypotheses for scientific discovery. The rapid adoption of complex ML models and the need to understand how they work has led to the development of a broad class of techniques called eXplainable Artificial Intelligence (XAI). These methods probe the models in various ways to reveal insights into how they work. Correlations among input features can make it challenging to produce meaningful explanations. The gridded spatial data common in atmospheric modeling applications typically have extensive correlation. Spatial autocorrelation is present among the cells of each spatial grid, but autocorrelation may exist across the gridded data volume due to spatial or temporal relationships between adjacent channels. In addition, there may be correlations between distant locations due to teleconnections between them. Correlated input features may cause high variance among the trained models. If grid cells are highly correlated, then the target function that the network is attempting to learn is ill-defined and an infinite number of models can be generated that achieve approximately equal performance. Even assuming a perfect XAI method exists, the attribution reflects only the patterns learned for a given model. It is arbitrary which of the correlated features are used by a given model. This can lead to a misleading understanding of the actual relationship between the input features and target. A potential solution is to group the correlated features before applying XAI. Attribution can be assigned to each group rather than to individual cells. In this case, all the correlated cells will be permuted at the same time to analyze their collective impact on the output. The purpose is to reveal the contribution of each group of related cells toward the model output. Ideally, the explanations are insensitive to the random choice among correlated features learned by the model. Without grouping, the user can be misled to consider a feature as not being related to the target because of the presence of correlated features. With grouping, the explanations should better reveal the learned patterns. Grouping features based on correlation can be challenging. The correlation rarely equals one and the strength of the correlation influences the variance among trained models. Calculating the correlation can be difficult because of partial correlations and fuzzy, continuous boundaries. The choice of groups can greatly influence the explanations. Another challenge is that it is not straight-forward to assess the quantitative accuracy of an XAI technique. This is because there is rarely a ground truth explanation to compare to. If we knew the attribution, we would not need XAI methods. Synthetic benchmarks for analyzing XAI have been proposed as a solution [4]. It is possible to define a non-linear function such that the contribution of each grid cell’s value to the function output can be derived. This attribution map represents the ground truth for comparison the the output of XAI methods that are applied to a model that very closely approximates the hand-crafted function. In this research, we develop a set of benchmarks to investigate the influence of correlated features on the variation in XAI outputs for a set of trained models. We then explore how features can be grouped to reduce the explanation variance so that users have improved insight into the learned patterns. First, we create a set of very simple mathematical demonstrations that precisely demonstrate the influence of correlated features and how grouping features provides a solution. Using insights from these experiments, we develop a tool for detecting when correlated features are likely to cause misleading explanations. We then create a set of more realistic benchmarks that are based on atmospheric modeling problems such as sea surface temperature and coastal fog prediction. By defining benchmarks with known ground truth explanations, we can analyze various techniques for grouping the grid cells based on their correlations. Based on our findings, we offer recommendations for strategies to group correlated data so that users can better leverage XAI results toward model development and scientific insights. [1] Kamangir, H., Collins, W., Tissot, P., King, S. A., Dinh, H. T. H., Durham, N., & Rizzo, J. (2021). FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction. Machine Learning with Applications, 5, 100038. [2] Lagerquist, R. (2020). Using Deep Learning to Improve Prediction and Understanding of High-impact Weather. [3] Gagne II, D. J., Haupt, S. E., Nychka, D. W., & Thompson, G. (2019). Interpretable deep learning for spatial analysis of severe hailstorms. Monthly Weather Review, 147(8), 2827-2845. [4] Mamalakis, A., Ebert-Uphoff, I., & Barnes, E. A. (2022). Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset. Environmental Data Science, 1, e8.

Abstract Using recently collected high‐resolution seismic data along a dense linear transect across Ohio, West Virginia, and Virginia (called Mid‐Atlantic Geophysical Integrative Collaboration (MAGIC) profile), we analyze P‐to‐S receiver functions to investigate the undulations of the mantle transition zone (MTZ) discontinuities (410‐ and 660‐km) beneath the central Appalachian region. Our results incorporating the effects of local crustal and mantle structures suggest shallowing of both the 410‐ and the 660‐km discontinuities from the northwest (inland) to the southeast (coast) along MAGIC profile. Hydro‐thermal upwelling beneath the eastern U.S. coastal plain due to a hydrated MTZ and hot upwelling return flow associated with the descending lower mantle Farallon slab is consistent with our observations of MTZ structure considering 3D velocity heterogeneity. The inferred hydrous hot upwelling rising into the upper mantle may trigger dehydration melting atop the 410‐km discontinuity, which may help to explain the presence of a low velocity upper mantle anomaly beneath the region today.

Due to the increasing availability of high-performance computing over the past few decades, numerical models have become an important tool for research in geodynamics. Several generations of mantle convection software have been developed, but due to their differing methods and increasing complexity it is important to evaluate the accuracy of each new model generation to ensure published geodynamic research is reliable and reproducible. Here we explore the accuracy of the open-source, finite-element codes ASPECT and CitcomS as a function of mesh spacing using low to moderate-Rayleigh-number models in steady-state thermal convection. ASPECT (Advanced Solver for Problems in Earth's ConvecTion) is a new-generation mantle convection code that enables modeling global mantle convection with realistic parameters and complicated physical processes using adaptive mesh refinement . We compare the ASPECT results with calculations from the finite-element code CitcomS , which has a long history of use in the geodynamics community. We find that the globally averaged quantities, i.e., root-mean-square (rms) velocity, mean temperature, and Nusselt number at the top and bottom of the shell, agree to within 1 % (and often much better) for calculations with sufficient mesh resolution. We also show that there is excellent agreement of the time evolution of both the rms velocity and the Nusselt numbers between the two codes for otherwise identical parameters. Based on our results, we are optimistic that similar agreement would be achieved for calculations performed at the convective vigor expected for Earth, Venus, and Mars.

Abstract The heat flow and physical properties package measured soil thermal conductivity at the landing site in the 0.03–0.37 m depth range. Six measurements spanning solar longitudes from 8.0° to 210.0° were made and atmospheric pressure at the site was simultaneously measured using InSight's Pressure Sensor. We find that soil thermal conductivity strongly correlates with atmospheric pressure. This trend is compatible with predictions of the pressure dependence of thermal conductivity for unconsolidated soils under martian atmospheric conditions, indicating that heat transport through the pore filling gas is a major contributor to the total heat transport. Therefore, any cementation or induration of the soil sampled by the experiments must be minimal and soil surrounding the mole at depths below the duricrust is likely unconsolidated. Thermal conductivity data presented here are the first direct evidence that the atmosphere interacts with the top most meter of material on Mars.

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.

SUMMARY The SEIS (seismic experiment for the interior structure of Mars) experiment on the NASA InSight mission has catalogued hundreds of marsquakes so far. However, the detectability of these events is controlled by the weather which generates noise on the seismometer. This affects the catalogue on both diurnal and seasonal scales. We propose to use machine learning methods to fit the wind, pressure and temperature data to the seismic energy recorded in the 0.4–1 and 2.2–2.6 Hz bandwidths to examine low- (LF) and high-frequency (HF) seismic event categories respectively. We implement Gaussian process regression and neural network models for this task. This approach provides the relationship between the atmospheric state and seismic energy. The obtained seismic energy estimate is used to calculate signal-to-noise ratios (SNR) of marsquakes for multiple bandwidths. We can then demonstrate the presence of LF energy above the noise level during several events predominantly categorized as HF, suggesting a continuum in event spectra distribution across the marsquake types. We introduce an algorithm to detect marsquakes based on the subtraction of the predicted noise from the observed data. This algorithm finds 39 previously undetected marsquakes, with another 40 possible candidates. Furthermore, an analysis of the detection algorithm’s variable threshold provides an empirical estimate of marsquake detectivity. This suggests that events producing the largest signal on the seismometer would be seen almost all the time, the median size signal event 45–50 per cent of the time and smallest signal events 5−20 per cent of the time.

Analyses of seismic data from the InSight mission have provided the first in situ constraints on the thickness of the crust of Mars. These crustal thickness constraints are currently limited to beneath the lander that is located in the northern lowlands, and we use gravity and topography data to construct global crustal thickness models that satisfy the seismic data. These models consider a range of possible mantle and core density profiles, a range of crustal densities, a low‐density surface layer, and the possibility that the crustal density of the northern lowlands is greater than that of the southern highlands. Using the preferred InSight three‐layer seismic model of the crust, the average crustal thickness of the planet is found to lie between 30 and 72 km. Depending on the choice of the upper mantle density, the maximum permissible density of the northern lowlands and southern highlands crust is constrained to be between 2,850 and 3,100 kg m−3. These crustal densities are lower than typical Martian basaltic materials and are consistent with a crust that is on average more felsic than the materials found at the surface. We argue that a substantial portion of the crust of Mars is a primary crust that formed during the initial differentiation of the planet. Various hypotheses for the origin of the observed intracrustal seisimic layers are assessed, with our preferred interpretation including thick volcanic deposits, ejecta from the Utopia basin, porosity closure, and differentiation products of a Borealis impact melt sheet.

While volcanic landforms attest to the numerous and varied volcanic processes on Venus, estimates of the frequency of volcanic eruptions are lacking. Constraints from volcanic resurfacing volumes can be equally satisfied by infrequent large eruptions or numerous smaller events. Recently, Byrne and Krishnamoorthy (2022), https://doi.org/10.1029/2021JE007040, used the 40‐year period from 1980 to 2020 from the Smithsonian Global Volcanism Program (GVP) database (Global Volcanism Program, 2013) to extrapolate the frequency of volcanic events on Earth to Venus. They evaluated the tectonic settings from the GVP database and provided estimates that consider the differences in those settings between Earth and Venus. Byrne and Krishnamoorthy extrapolated the rate of volcanism between Earth and Venus using the mass/volume ratios, freeing their results from the uncertain tectonic evolution of Venus. The assumption that Venus is in a stagnant‐lid tectonic regime with a straight‐forward geodynamic evolution has been challenged by Weller and Kiefer (2020), https://doi.org/10.1029/2019je005960, who showed that a planetary surface may reflect different styles of convection with highly active and sluggish and inactive regions occurring at the same time. The straight‐forward scaling allowed Byrne and Krishnamoorthy to estimate that as many as 120 eruptions might take place on Venus every year, a frequency that should be detectable by upcoming Venus missions.

Abstract With a small fraction of marginal subduction zones, the driving mechanism for the North American plate motion is in debate. We construct global mantle flow models simultaneously constrained by geoid and plate motions to investigate the driving forces for the North American plate motion. By comparing the model with only near‐field subducting slabs and that with global subducting slabs, we find that the contribution to the motion of the North American plate from the near‐field Aleutian, central American, and Caribbean slabs is small. In contrast, other far‐field slabs, primarily the major segments around western Pacific subduction margins, provide the dominant large‐scale driving forces for the North American plate motion. The coupling between far‐field slabs and the North American plate suggests a new form of active plate interactions within the global self‐organizing plate tectonic system. We further evaluate the extremely slow seismic velocity anomalies associated with the shallow partial melt around the southwestern North America. Interpreting these negative seismic shear‐velocity anomalies as purely thermal origin generates considerably excessive resistance to the North American plate motion. A significantly reduced velocity‐to‐density scaling for these negative seismic shear‐velocity anomalies must be incorporated into the construction of the buoyancy field to predict the North American plate motion. We also examine the importance of lower mantle buoyancy including the ancient descending Kula‐Farallon plates and the active upwelling below the Pacific margin of the North American plate. Lower mantle buoyancy primarily affects the amplitudes, as opposed to the patterns of both North American and global plate motions.

While we now know much about the volatile‐rich world of Ceres from the Dawn mission, the deep interior remains something of an enigma, shrouded by a crust composed of water ice, carbonates, phyllosilicates, salts and clathrate hydrates. While smaller than most active moons or planets, Ceres has many features commonly associated with active, icy bodies including: hydrothermal, cryovolcanic, and tectonic features. Yet on active icy moons tidal heating is a significant component of the thermal budget; it is unclear whether radiogenic heating alone would be sufficient to supply the heat necessary for Ceres' interior to undergo solid‐state convection. Here we show that transient asymmetric convection develops as the temperature within the body rises from heat generated by the decay of long‐lived radionuclides (e.g., U, Th, K). The onset of transient asymmetric convection may reconcile a number of puzzling features on Ceres including: the missing large craters, Hanami Planum—the region of thickened crust, the gravity and crustal thickness, and the lithospheric stress state represented by the Samhain Catenae. Hemispheric‐scale instabilities may also be important in the evolution of small bodies with small cores throughout the solar system, including the small icy moons of Saturn and Uranus as well as Kuiper belt objects.