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Surface ice at the poles of Mercury appears as several-m-thick deposits that are composed of nearly pure water. We provide new age estimates of the surfaces of Mercury's polar deposits from combined analyses of Poisson statistics and direct observations of crater densities within permanently shadowed, radar-bright regions imaged by the MESSENGER spacecraft. These age estimates conservatively suggest that ice was delivered to Mercury within the last ∼330 Myr. The geologically young ages suggest that the surfaces have been recently refreshed, and this may be accomplished by the delivery of ice in a young impactor or impactors. A single, recent impactor is more consistent with the relative purity of the ice, as suggested by the Earth-based radar observations. In contrast to ice on Mercury, observations of the lunar poles are suggestive of a highly patchy distribution of surface frost. The patchiness of lunar polar deposits is consistent with long exposure times to the space weathering environment. Given enough time, the polar deposits on Mercury may age into a more heterogeneous spatial distribution, similar to that on the Moon.

Changes in mass distribution affect the gravitational figure and reorient a planetary body’s surface with respect to its rotational axis. The mass anomalies in the present-day lunar gravity field can reveal how the figure and pole position have evolved over the Moon’s history. By examining sequentially each individual crater and basin, working backward in time order through the catalog of nearly 5200 craters and basins between 1200 and 20 km in diameter, we investigate their contribution to the lunar gravitational figure and reconstruct the evolution of the pole position by extracting their gravitational signatures from the present-day Moon. We find that craters and basins in this diameter range, which excludes South Pole–Aitken, have contributed to nearly 25% of the present-day power from the Moon’s degree-2 gravitational figure and resulted in a total displacement of the Moon’s pole by ∼10° along the Earth–Moon tidal axis over the past ∼4.25 billion years. This also implies that the geographical location of the Moon’s rotational pole has not moved since ∼3.8 Ga by more than ∼2° in latitude owing to impacts, and this has implications for the long-term stability of volatiles in the polar regions.

The surface of Mercury is dominated by extensive, widespread lava plains that formed early in its history. The emplacement of these lavas was accompanied by the release of magmatic volatiles, the bulk of which were lost to space via thermal escape and/or photodissociation. Here we consider the fate of these erupted volatiles by quantifying the volumes of erupted volcanic plains and estimating the associated masses of erupted volatiles. The concentrations and speciation of volatiles in Mercury's magmas are not known with certainty at this time, so we model a wide range of cases, based on existing experimental data and speciation models, at 3–7 log fO2 units below conditions determined by the iron-wustite buffer. Cases range from relatively low gas content scenarios (total exsolved gas mass of 9 × 10 15 kg) to high gas content scenarios (total exsolved gas = 5 × 1019 kg). We estimate that the average duration of a transient volcanic atmosphere resulting from a single eruption would be between ∼250 and ∼210,000 years, depending on the volume, degassed volatile content, and eruption rate of an individual eruption, as well as the fO2 conditions of the planet's interior. If a dense transient atmosphere was ever surface-bound long enough for the released volatiles to be transported to and cold-trapped at Mercury's polar regions, those trapped volatiles are predicted to be well-mixed with the regolith, and at least 16 m beneath the surface given regolith gardening rates. These volatiles would have a composition and age distinctly different from those of the H2O-ice deposits observed at the poles of Mercury today.

In the development of Mars climate models, modeling clouds is an important challenge, and especially for CO2 clouds. This is due to the complexity of the atmospheric processes involved that may imply rethinking microphysical theories, but also to the scarcity of observations. In the late 90’s, Mars Orbiter Laser Altimeter was one of the instruments aboard the Mars Global Surveyor spacecraft. Its first goal was to build a precise map of Mars’ topography through laser altimetry but its sensitivity allowed for cloud observations as well . Thus, previous studies (Neumann & al. 2003 Ivanov & Muhlemann 2001) have shown that some laser returns were cloud signatures coming from the atmosphere. However, at that time, the huge amount of data was analysed using simple distinction criteria. We use K-means clustering algorithms to computationally analyse MOLA data. In order to optimise the method, we first determine the best observed parameters to distinguish the different kinds of returns (surface, noise and clouds). The best number of clusters is determined using three independent methods : elbow, silhouette score and gap statistics. The method is tested on a restricted sample (10 % of the dataset) and then applied to the full raw dataset. Once that cloud cluster identified, we can plot spatial and temporal distributions of the cloud returns and compare them with previous results. As mentioned by Neumann & al. (2003), the product of surface reflectivity and two-way transmissivity of the atmosphere appears as the best parameter discriminating between surface and cloud returns. A unique number of clusters (6) is identified by all three optimisation methods. Among those clusters, one clearly identifies cloud returns, while others represent noise and surface returns. Our methods allows us to identify more clouds than previous studies. Our cloud distribution remains coherent with the ones given in previous studies, showing the viability of our method. We will present a catalog of cloud returns coming from MOLA data. We are now working to separate different kinds of clouds within these returns (absorptive and reflective clouds, CO2 / water clouds, dust …) using machine learning algorithms and a recent MOLA surface reflectivity map (Heavens & al. 2016).

Based on previous applications of laser altimetry to planetary geodesy at GSFC, we use the recently developed PyXover software package to analyze altimetric crossovers from the Mercury Laser Altime...

International audience; Images of asteroids (162173) Ryugu and (101955) Bennu acquired by the Hayabusa2 and OSIRIS-REx missions, respectively, reveal rocky worlds covered in rubble. These two asteroids do not have hydrostatic shapes, indicating that they possess some internal friction and/or cohesion even if they lack tensile strength. Understanding the deviation of the surfaces of these bodies from those of idealized shapes helps constrain the mechanical properties of their interiors. Here, we focus on the feedback between YORP-induced spin-up (in which asymmetric reflection and re-emission of solar radiation from the surface systematically change the rotation rate), long-wavelength topography (which provides a structure to control the orientation), and surface roughness on Ryugu and Bennu. By performing spherical harmonic analyses of the shapes of these two asteroids, we find that although they are superficially similar, they exhibit different long-wavelength topography that implies different internal structures and rotational histories. Bennu's shape and rotation rate require a modest amount of internal strength through some combination of at least 17° of internal friction or a few Pa of cohesion, whereas Ryugu could be nearly strengthless. Bennu's longitudinal ridges make it susceptible to YORP spin-up, consistent with the observed increase in rotation rate that is not seen on Ryugu. These longitudinal ridges also suggest a heterogeneous density structure for Bennu, consistent with gravity data.

Unit operations laboratory courses culminate many topics from the curriculum and are often the first time students apply their knowledge in hands-on settings. However, many instructors do not directly assess key learning objectives, creating a disparity between desired and actual learning. Using multiple assessment tools, we analyzed student survey and test data throughout a unit operations laboratory sequence to reveal student attitudes and perceptions of their learning experience alongside their actual knowledge and abilities gained.

We present the results of the first series of successful two-way laser ranging experiments from a ground station, the Lunar Laser Ranging (LLR) station in Grasse, France, to a spacecraft at lunar distance, the Lunar Reconnaissance Orbiter (LRO). A 15 × 18 × 5 cm, 650-g array of twelve 32-mm diameter solid corner cubes is mounted on its anti-nadir deck. Ranging to this small retro-reflector array onboard a lunar orbiter from a ground station was a challenge compared to ranging to larger lunar surface retro-reflectors. Grasse measured 67 returns in two 6-min sessions on September 4, 2018. Clear returns were also recorded during two additional sessions on August 23–24, 2019 for which active slewing by LRO was performed to bring the array in view of the station. The measured echos yielded range residuals less than 3 cm (two-way time-of-flight RMS

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Based on previous applications of laser altimetry to planetary geodesy at GSFC [Mazarico et al. (2014)], we use the recently developed PyXover software package to analyze altimetric crossovers from the Mercury Laser Altimeter (MLA) and improve geodetic parameters via least squares (LS) minimization of crossover discrepancies.We simultaneously solve for orbital corrections for each MESSENGER track, for the geodetic parameters of the IAU recommended orientation model for Mercury [Archinal et al, 2018], and for the Mercury Love number h2.We calibrate the formal errors of our solution based on closed-loop simulations and on the level of independence from \emph{a priori values and data selection. Data description: From March 2011 to April 2015, the MESSENGER spacecraft orbited Mercury in a highly elliptical, near-polar orbit with a periapsis of ~200-400 km, an apoapsis between ~1.5-2 x 10^4 km, and an orbital period of 12 hrs initially and reduced to 8 hrs after one year. The spacecraft was within ranging distance for the onboard MLA over 15-45 min periods near periapsis, typically at latitudes > 30 deg N. MLA collected over 22 million measurements of surface height with a vertical precision of ~1 m and an accuracy of ~10 m. The total MLA dataset contains ~3,200 tracks and ~3 million crossovers, i.e., instances where two ground-tracks intersect. Because of the elliptical orbit, the laser spot size on the surface varied between ~10 - 100 m, while the average distance between each crossover and its bracketing observations was ~200 m. These crossovers provide an opportunity to measure Mercury’s orientation and rotation (see Fig.1).Independent confirmation and refinement of the IAU libration model, developed from ground-based radar measurements [Margot et al. 2009], is important as it has implications for the moment of inertia of the outer solid shell and thus the mass distribution, internal structure, and thermal evolution of Mercury [Phillips et al. (2018), Genova et al. (2019)]. Processing and solution strategy: Each crossover is the intersection of two separate ground-tracks. It can be thought of as a differential measurement between two distinct observations of the same surface location at two different times. Any difference in height at the crossover point is mainly due to the following effects: (1) Errors in the spacecraft orbit and attitude, or MLA boresight orientation, (2) interpolation errors of the surface topography between MLA footprints, and (3) geophysical signal due, e.g., to mismodeled time-varying planetary rotation or to tidal vertical motions. We perform the analysis of MLA data with the PyXover python code, whose modular structure is sketched in Fig.2.Laser altimetry ranges are geolocated to the planetary surface and partial derivatives of the ground-tracks are computed with respect to the chosen parameters by finite differencing. Initial geolocation is based on the MESSENGER orbit navigation reconstruction by KinetX and on the values provided by the IAU for Mercury orientation [Archinal et al, 2018]. Values for the Love numbers h2 and l2 are set to 0 in our a priori tidal model.Horizontal coordinates of crossover points are recovered in local stereographic projection in a two-step process, to balance computational time.Expected elevations at intersection points are then interpolated from neighbouring points on each track. Their discrepancies w constitute the observation residuals to be minimized in the LS procedure.Huber weighting is then applied to the crossovers depending on the reliability of the orbital tracks involved, on the off-nadir angle of the spacecraft and on inter-point distances. Crossovers with abnormally large discrepancies are strongly down-weighted in our analysis. We minimize the total RMSE of crossover discrepancies within a penalised LS with weights determined by Variance Component Estimation (VCE). The resulting corrections to MESSENGER orbits and Mercury geodetic parameters are then applied to update the a priori for the subsequent iteration, until convergence is reached. Parametrization and error assessment:Formal errors provided by LS and VCE are notoriously under-estimated. We thus perform additional analysis to consider systematic error sources, such as the chosen apriori values and data selection. In particular, we measure the stability of the solutions resulting from different Doppler reconstructions of MESSENGER orbits (KinetX and [Genova et al, 2018]) and from both IAU [Archinal et al, 2018] and [Genova et al, 2019] values for Mercury rotational parameters. Also, we process multiple sub-samples of 500,000 crossovers (max 20% in common, stratified by latitude to conserve the overall geographical distribution) and we verify the dispersion of the solutions at convergence.Moreover, we conduct extensive simulations with time-of-flight ranges consistently generated from realistic topography to analyze the impact of the interpolation error and the reliability of the recovery in different scenarios and parametrizations. Processing of MLA crossovers: Finally, we perform a weighted LS solution of orbit corrections and geodetic parameters based on the presented processing setup.We base our solution on a set of 106 crossovers selected according to their computed weight (i.e., their quality) and to ensure a balanced geographical distribution. We iterate the solution until convergence is reached, i.e., parameters changes are well below formal errors ( We check the reduction of the RMSE of post-fit discrepancies w, as shown in Fig.3, the evolution of VCE derived weights and the consistency of topography maps to evaluate the improvements. Our results for some geodetic parameters are shown in Fig.4 and are consistent with previous solutions provided by other groups using various techniques (camera and altimetry, Doppler, Earth-based radar). Error bars are calibrated based on both closed-loop simulations and the analysis of systematic errors outlined in the previous section.We will discuss the recovered h2 and our determination of Mercury rotational parameters at the meeting. AcknowledgementsS. Bertone acknowledges support by the Swiss National Science Foundation within the Advanced Postdoc Mobility program and by NASA’s Planetary Science Division Research Program through the CRESST II cooperative agreement. The MLA data were processed on the GSFC NCCS ADAPT cluster.

Abstract We propose the co-registration of local laser profile segments to high resolution Digital Terrain Models (DTMs) as an approach for obtaining seasonal CO2 ice cover height variations on Mars. The co-registration is parameterized in instantaneous MOLA pointing angles involving a rigorous laser altimeter geolocation model. Thereby, the height bias of the MOLA footprint produced by the pointing bias could be compensated through an iterative process. The feasibility and advantages of this method are tested in an example region. The ultimate goal is to apply this method to Mars Orbiter Laser Altimeter (MOLA), SHAllow RADar (SHARAD) radar altimetry and high-resolution stereoscopic DTMs to generate long-term seasonal height change time series at the Martian poles with high spatial and temporal resolution. 1 Introduction The dynamic growth and retreat of polar CO2 frost at the Martian poles has always been one of the focuses of planetary scientists [1, 2, 3]. Accurate measurements of seasonal and long-term elevation and volume changes can serve as important constraints in Mars climate models, help tap into the density evolution of the CO2 snow once combined with gravity measurements, and constrain the degree of stability of the polar deposits. The commonly-used approach to this problem is the cross-over analysis of the laser altimetry profiles, but this method may suffer from significant interpolation errors when spacing between footprints is large, also residual pointing, timing and orbit error may translate into lateral shifts of the laser profiles and further undermine the results. Here, we propose and validate the local co-registration between laser profile segments and high resolution DTMs from stereo pairs as a solution to these limitations. 2 Data 2.1 MOLA records The MOLA Precision Experimental Data Record (PEDR) dataset features a total of 8505 profiles, acquired in the mapping and extended phases of Mars Global Surveyor (MGS) from February, 1999 to May, 2001, which spanned approximately a full Martian year [1]. The PEDR dataset was processed using MGS orbit trajectory model and a Mars rotational model by GSFC dating back to 2003. Therefore, we have incorporated a refined orbit model from [4] and IAU2015 Mars rotational model [5] in the MOLA geolocation reprocessing. Meanwhile, to account for the special relativity effect, the pointing aberration correction has also been taken care of in the reprocessing [6]. 2.2 HRSC DTMs The High Resolution Stereo Camera (HRSC) is a pushbroom camera onboard the European Space Agency (ESA) spacecraft Mars Express. A total of 34 HRSC DTM tiles are adopted which feature a grid size of 50 m and covers the majority of the Martian South Pole [7]. 3 Methods First, the reprocessed MOLA profiles are self-registered to each other to form a coherent reference in the Martian South Pole [8, 9]. Subsequently, individual HRSC DTM tiles have been aligned to the MOLA reference data and mosaiced to a self-consistent reference for the co-registration. The co-registration of the reprocessed MOLA profiles to the aligned HRSC DTM mosaic is setup by compensating for two alignment angles of the boresight and height which incorporates an analytical laser altimeter geolocation model. The benefit is that the height offset induced by bias in pointing can be simultaneously compensated during the iterative co-registration process. The height differences with respect to the DTM mosaic at either footprint, cross-overs, or pseudo cross-overs are assigned as the height corrections from the local co-registration process using segmented profiles centered at these feature points. Here, pseudo cross-overs are DTM-based and formed by two track segments that do not have to actually intersect, substantially increasing the available number of cross-overs [10]. Then, CO2 height change time series are obtained by median-binning those temporal height differences with the uncertainty quantified by scaled median absolute deviation. To get rid of a temporal systematic error, the acquired temporal trend is subtracted from the one at 60°S annulus which features unchanging topography. 4 Results Figure 1: Results using height differences at footprints before the adoption of the local co-registration procedure (“F_LC”, red line, bottom) and comparison to previous literature (lime and aqua lines). Figure 2: Results using height differences at footprints after the adoption of the local co-registration procedure (“F_LC”, red line, bottom) and comparison to previous literature (lime and aqua lines). A test has been carried out at an example region situated right on the residual ice cap where largest peak-to-peak height variation would be expected. The uncertainty of the derived height change time series using height differences at footprints decreased from ~2 m to ~0.5 m after the adoption of the local co-registration procedure, roughly a fourfold enhancement (Figures 1 and 2). The maximum height fluctuation is estimated at ~2.5 m which is similar to that of Aharonson et al. (2004) [2]. The suspicious off-season accumulation pointed out by previous results using MOLA has not been resolved. While a sharp pit-shaped feature of ~1 m depth centered at solar longitude 210° is observed. Similar results have been obtained using height differences at cross-overs and pseudo cross-overs. As a byproduct of the co-registration process, MOLA alignment angles have also been examined to follow some specific temporal patterns. 5 Summary We show the feasibility and merits of the local co-registration strategy in the application of retrieving height changes at either footprints, cross-overs, or pseudo cross-overs. As for the next step, we will put the SHARAD radar altimetry and reflectometry into test [11]. Combined with the MOLA laser altimetry, long-term CO2 height change time series spanning two decades could possibly be retrieved. Apart from height change mapping, the proposed method could also be readily adapted to the tidal Love number measuring, orbit refinement with so called “direct altimetry” [12] and others. Acknowledgements This work was supported by a research grant from Helmholtz Association and German Aerospace Center (DLR). We acknowledge the work by the MOLA and HRSC instrument and science teams. References [1] Smith et al.,Science, 2001, 294, 2141-2146. [2] Aharonson et al., JGRPlanets, 2004, 109(E5). [3] Genova, ActaAstronaut., 2020, 166, 317-329. [4] Konopliv et al., Icarus, 2006, 182, 23-50. [5] Konopliv et al., Icarus, 2016, 274, 253-260. [6] Xiao et al., Submitted to JoG. [7] Putri et al., PSS, 2019, 174, 43-55. [8] Stark et al., EPSC2018, Contrib. No. 890. [9] Stark et al., GRL, 2015, 429, 7881-7889. [10] Barker et al., Icarus, 2016, 273, 346-355. [11] Steinbrügge et al., LPSC2019, LPI Contrib. No. 2132. [12] Goossens et al., Icarus, 2020, 336, 113454.

Modeling clouds is a challenge we are currently facing in the development of Mars climate models. In particular, CO2 clouds are exotic components of the Mars’ atmosphere that may imply rethinking some microphysical theories. Moreover, available datasets that could allow a better understand of involved processes are rare and, thus, must be analysed the best we can to acquire more information. The Mars Orbiter Laser Altimeter (MOLA, Smith and al., 1999) was an instrument aboard the Mars Global Surveyor spacecraft for a mission around Mars between 1997 and 2006 (MOLA provided data until 2001). MOLA’s first goal was to study Mars’s topography using laser altimetry in order to draw a precise map. However, range measurements have been made with a better precision than expected, allowing detection of features that were not assignable to the planet surface. Those features include clouds, especially polar winter CO2 ice clouds that MOLA has been the first to detect. Previous studies conducted in the early 2000s (Neumann and al. (2003), Ivanov & Muhlemann (2001)) demonstrated that among all the laser returns, some were clearly clouds signatures coming from the atmosphere. However, the huge amount of data limited the manual analysis and forced the use of very strict distinction criteria, eventually leading to some misses. Our goal is to distinguish different categories of laser returns in MOLA data. A good modern option to computionally analyse MOLA data could be clustering methods, and K-means clustering methods in particular (Ackerman & Ben-David (2009)). For numerical reasons, we proceed to a first pruning to reduce the amount of data, assuming cloud returns could not be too close to the surface, by a typical range of less than ten meters from the MOLA vertical resolution (Abshire and al. (2010)) . We first apply the method on a single data file (that represents about 10 % of total data) then enlarge to the whole data set. We have led preliminary studies through empirical tests in order to define the best observational parameters for clustering among all those available in the raw data, best parameters being those allowing us to separate surface, noise and clouds returns. K-means methods are efficient ways of clustering but they require providing a predetermined number of clusters. That’s why we used three totally independent optimisation methods. Elbow method computes the total intra-cluster variation through total within-cluster sum of squares. Average silhouette method analyses clusters through « silhouette » score from distance of each point to its cluster and to closest one. Finally, gap statistic method (Tibshirani and al. (2001)) determines how far our clustering structure is from a random uniform distribution of points. All of these methods give us an « optimised » number of clusters to work with. The idea in this study is to find one cluster that would be a « cloud » cluster, containing all the non-surface non-noise laser returns. We plot geographical and temporal distribution of the different clusters separately in a first step, then more specifically for the wanted cloud cluster to verify the reliabily by comparison with previous studies. We can then work within the cloud cluster itself with clustering methods, to eventually find different types of cloud (and possibly dust). Following the reference paper for cloud detection in MOLA data (Neumann and al., 2003), the product of surface reflectivity and two-way transmissivity of the atmosphere, rT², appears as the best parameter for distinguishing cloud and surface returns, through the product of each laser return itself but also the average product of previous and next returns. Working on our test case (10 % of the data), our three independent optimisation methods converge to the same number of clusters. Among the clusters, cloud cluster is the one with both low rT² product and low average neighbors rT² (making a well-like shape in the time/rT² figure). Plotting the clusters shows that one of them identifies clouds returns. Another cluster could represent clouds boundaries or thinner clouds, while the other ones identify noise (low rT² product but high average neighbors product) and surface returns (high rT² product and high average neighbors product). We then move on to the whole data by applying our clustering method to each data file (around 10 % of data each time), plotting clusters for every file to eventually adjust the necessary number of clusters. Geographic and temporal plots of our cloud returns are in agreement with previous studies, presenting similar distributions. However, it seems like our method allows to find more clouds due to less stringent detection criteria. So far, we have not been able to find parameters to clearly separate the different type of clouds but a lot of possibilities are still to be tested. Because absorptive clouds can not really be attributed to a precise altitude, some of them may have been eliminated through our first pruning. It would be interesting to be able to cluster directly the whole data set but this requires some optimisation of our program. Moreover, evolution of the equipement capacities during the mission leads to different rT² continuum depending on the orbit : working with a normalised rT² product instead of raw rT² product itself should also be tested. Finally, if K-means methods are not adapted to work within the cloud cluster to distinguish reflective and absorptive clouds for example, it will be necessary to find another clustering method for the second step of our study. However, our results are promising and will hopefully help in complementing the first MOLA cloud climatology (Neumann and al. (2003)). Such observations are very important for comparing with microphysical model results ans helping us understand cloud formation process on Mars. Acknowlegdements : We thank the Agence National de la Recherche for funding (projet MECCOM, ANR-18-CE31-0013).

Four, quasi-circular, positive Bouguer gravity anomalies (PBGAs) that are similar in diameter (~90–190 km) and gravitational amplitude (>140 mGal contrast) are identified within the central Oceanus Procellarum region of the Moon. These spatially associated PBGAs are located south of Aristarchus Plateau, north of Flamsteed crater, and two are within the Marius Hills volcanic complex (north and south). Each is characterized by distinct surface geologic features suggestive of ancient impact craters and/or volcanic/plutonic activity. Here, we combine geologic analyses with forward modeling of high-resolution gravity data from the Gravity Recovery and Interior Laboratory (GRAIL) mission in order to constrain the subsurface structures that contribute to these four PBGAs. The GRAIL data presented here, at spherical harmonic degrees 6–660, permit higher resolution analyses of these anomalies than previously reported, and reveal new information about subsurface structures. Specifically, we find that the amplitudes of the four PBGAs cannot be explained solely by mare-flooded craters, as suggested in previous work; an additional density contrast is required to explain the high-amplitude of the PBGAs. For Northern Flamsteed (190 km diameter), the additional density contrast may be provided by impact-related mantle uplift. If the local crust has a density ~2800 kg/m(3), then ~7 km of uplift is required for this anomaly, although less uplift is required if the local crust has a lower mean density of ~2500 kg/m(3). For the Northern and Southern Marius Hills anomalies, the additional density contrast is consistent with the presence of a crustal complex of vertical dikes that occupies up to ~37% of the regionally thin crust. The structure of Southern Aristarchus Plateau (90 km diameter), an anomaly with crater-related topographic structures, remains ambiguous. Based on the relatively small size of the anomaly, we do not favor mantle uplift, however understanding mantle response in a region of especially thin crust needs to be better resolved. It is more likely that this anomaly is due to subsurface magmatic material given the abundance of volcanic material in the surrounding region. Overall, the four PBGAs analyzed here are important in understanding the impact and volcanic/plutonic history of the Moon, specifically in a region of thin crust and elevated temperatures characteristic of the Procellarum KREEP Terrane.

The large anomalies in the lunar gravity field are in most cases the result of large impacts that occurred more than 3 billion years ago. Today those anomalies provide the stability of the lunar rotation and if removed would cause a change in the position of the intersection of the spin pole with the lithosphere. Thus, extracting a gravity anomaly from today’s gravity field can provide the approximate location of the pole of rotation prior to the impact that caused the anomaly. By removing the gravity field of each anomaly in order of age, youngest first, we can estimate the path of the lunar pole back 3 to 4 billion years, to the beginning of the time of heavy bombardment.Starting from the GRAIL gravity model we selectively remove large gravity anomalies by first determining the center and dimensions of the anomaly from the Bouguer gravity and then deriving the average free air gravity for the Bouguer location and dimensions. The anomaly field is expanded into spherical harmonics and the degree 2 terms used to derive the change in pole position caused by the anomaly. Removing each anomaly in order of increasing age provides an estimate of the pole path from before the time of the first anomaly, SP-A. Since the pole path depends on the order of the gravity anomalies being created it is important to know when each impact induced anomaly occurred. The results suggest the re-constructed motion of the lunar pole of rotation is within approximately 10 dgerees of the present pole.

Mercury has been extensively resurfaced by large, effusive lava plains [1–2]. Similar lava plains on the Moon, the maria, are known to contain volatiles [3–4] and are estimated to have outgassed ~1016 kg of CO and S and ~1014 kg of H2O, with the bulk of volatiles being released during peak mare emplacement ~3.5 Ga ago [5]. If volcanic activity released substantial volatiles on the Moon [6–7], then it is possible that substantial volatiles were also volcanically released on Mercury, albeit with different chemical species [6–9]. Here we seek to understand the potential contribution of outgassing to volatile deposits, specifically for Mercury’s volatile species (S, CH4, Cl, and N-H).We analyze the production function of volcanic plains deposits on Mercury and find that the volume of outgassed basalts on Mercury is 2 to 3 orders of magnitude larger than that predicted for the Moon [8]. We use a variety of experimental petrology studies [10–12] to predict the dominant species and their abundances associated with these eruptions on Mercury, providing estimates for both low-gas and high-gas scenarios for different oxygen fugacities (IW-3 and IW-7). The most prevalent volatile species predicted for Mercury (S, CH4, and Cl) are 1 to 4 orders of magnitude more abundant than what is predicted for the most abundant volatiles outgassed on the Moon (CO, S, and H2O) [5].On the Moon, it has been predicted that volatiles outgassed from the formation of the maria may have been present in sufficient volumes to produce a transient atmosphere capable of aiding in the transport of H2O to cold-trapping regions [5]. At mantle pressures and Mercury’s extremely reducing conditions, H2O is not predicted to be present in the magma [e.g., 6–12]. Therefore, Mercury’s outgassed volatiles are of a different composition from the H2O ice observed at Mercury’s poles today [e.g., 13], and the polar H2O-ice deposits are better explained by some external delivery mechanism (likely cometary impacts). But the fate of large volumes of volatiles other than H2O is an important unanswered question for Mercury.The large volumes of outgassed volatiles calculated here suggest that volcanism on Mercury may have resulted in the transient production of anomalously high atmospheric pressures of short lifetime due to solar proximity. If Mercury’s atmospheric loss rate was insufficient to lose all of the erupted gases, then it is possible that ancient, outgassed volatiles remain trapped in the planet’s subsurface today. The fate of Mercury’s outgassed volatiles is an important open question that we discuss in this work.References: [1] Head et al. (2011). [2] Denevi et al. (2013). [3] Boyce et al. (2010). [4] McCubbin et al. (2010). [5] Needham and Kring (2017). [6] Nittler et al. (2011). [7] Zolotov et al. (2013). [8] Peplowski et al. (2016). [9] Greenwood et al. (2018). [10] Anzures et al. (2017). [11] Armstrong et al. (2015). [12] Libourel et al. (2003). [13] Lawrence et al. (2013).

Evidence in radar, reflectance, and visible imagery indicates that surface and subsurface water ice is present inside permanently shadowed regions in the north polar region of Mercury. The origin of this ice and the time at which it was delivered to the planet are both unknown. Finding the smallest, most easily eroded ice deposits on Mercury can help answer these questions. Here we present evidence for volatiles trapped in cold traps of scales ∼ 1-10 m. We consider two possible delivery methods for these deposits: a gradual, slow accumulation by micrometeorites or solar wind implantation and an episodic deposition, either primordial or by a recent comet impact. We conclude that the mechanism that best explains the presence of volatiles in these micro cold traps is a comet impact that most likely occurred in the last ∼ 100 Ma.

Radar-bright deposits at the poles of Mercury are located in permanently shadowed regions, which provide thermally stable environments for hosting and retaining water ice on the surface or in the near subsurface for geologic timescales. While the areal distribution of these radar-bright deposits is well characterized, their thickness, and thus their total mass and volume, remain poorly constrained. Here we derive thickness estimates for selected water-ice deposits using small, simple craters visible within the permanently shadowed, radar-bright deposits. We examine two endmember scenarios: in Case I, these craters predate the emplacement of the ice, and in Case II, these craters postdate the emplacement of the ice. In Case I, we find the difference between estimated depths of the original unfilled craters and the measured depths of the craters to find the estimated infill of material. The average estimated infilled material for 9 craters assumed to be overlain with water ice is ∼ 41 − 14 + 30 m, where 1−σ standard error of the mean is reported as uncertainty. Reported uncertainties are for statistical errors only. Additional systematic uncertainty may stem from georeferencing the images and topographic datasets, from the radial accuracy of the altimeter measurements, or from assumptions in our models including (1) ice is flat in the bowl-shaped crater and (2) there is negligible ice at the crater rims. In Case II, we derive crater excavation depths to investigate the thickness of the ice layer that may have been penetrated by the impact. While the absence of excavated regolith associated with the small craters observed suggests that impacts generally do not penetrate through the ice deposit, the spatial resolution and complex illumination geometry of images may limit the observations. Therefore, it is not possible to conclude whether the small craters in this study penetrate through the ice deposit, and thus Case II does not provide a constraint on the ice thickness. For Mercury's polar deposits, we argue that Case I of the small craters predating the emplacement of the ice deposits is more likely, given other geologic evidence that suggests that these ice deposits are relatively young. Using the ice thickness estimates from Case I to calculate the total amount of water ice currently contained in Mercury's polar deposits results in a value of ∼10 14 –10 15 kg. This is equivalent to ∼100–1000 km 3 ice in volume. This volume of water ice is consistent with delivery via micrometeorite bombardment, Jupiter-family comets, or potentially a single impactor.