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C I Fassett

Publications and source records attributed to C I Fassett.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

A Lack of Spin-Orbit Signal in the Morphology of Mercury's Large Craters

The spatial structure of Mercury's thermal lithosphere depends on the balance between internal heat and surface temperature as controlled by solar insolation. For bodies not in a spin-orbital resonance, observed spatial temperature variations are due to internal heating variations. However, for Mercury's present 3:2 spin-orbit coupling a notable difference of ~150 K exists for the sub-skin depth temperature of the crust as a function of longitude (Fig 1B.; [1,2]). Mercury's longitudinal "hot poles" and "cold poles", in addition to the standard poles (i.e. North and South), have large temperature contrasts that could lead to systematic differences in crater size, morphology, or morphometry, especially for large impacts.

Mercury↗
NASA NTRS2015

The Age of Lunar South Circumpolar Craters Haworth, Shoemaker, Faustini, and Shackleton: Implications for Regional Geology, Surface Processes, and Volatile Sequestration

The interiors of the lunar south circumpolar craters Haworth, Shoemaker, Faustini, and Shackleton contain permanently shadowed regions (PSRs) and have been interpreted to contain sequestered volatiles including water ice. Altimetry data from the Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar Reconnaissance Orbiter provide a new means of examining the permanently shadowed interiors of these craters in unprecedented detail. In this study, we used extremely high-resolution gridded LOLA data of Haworth, Shoemaker, Faustini, and Shackleton to determine the size-frequency distributions and the spatial density of craters superposing their rims, inner slopes, and floors. Based on their population of superposed D greater than or equal to 2 km craters, Haworth, Shoemaker, and Faustini have pre-Nectarian formation ages. Shackleton is interpreted as having a Late Imbrian age on the basis of craters with diameter D greater than or equal to 0.5 km superposed on its rim. The local density of craters with sub-km diameters across our study area is strongly dependent on slope; because of its steep interior slopes, the lifetime of craters on the interior of Shackleton is limited. The slope-dependence of the small crater population implies that the population in this size range is controlled primarily by the rate at which craters are destroyed. This is consistent with the hypothesis that crater removal and resurfacing is a result of slopedependent processes such as diffusive mass wasting and seismic shaking, linked to micrometeorite and meteorite bombardment. Epithermal neutron flux data and UV albedo data show that these circumpolar PSRs, particularly Shoemaker, may have approximately 1-2% water ice by mass in their highly porous surface regolith, and that Shoemaker may have approximately 5% or more water ice by mass in the near subsurface. The ancient formation ages of Shoemaker, Faustini and Haworth, and the Late Imbrian (approximately 3.5 Ga) crater retention ages of their floors suggests that any water ice that might have been deposited in their permanently shadowed areas was insufficient to modify the superposed crater population since that time.

Circumpolar↗
NASA NTRSDate not supplied

Exploring the Neotectonics of the Moon: The Young, Rocky, Dorsa Aldrovandi Wrinkle Ridge Northwest of Taurus-Littrow

Lunar Reconnaissance Orbiter Camera (LROC) observations have established that some of the small wrinkle ridges on the Moon are certainly Copernican, and possibly active within a few Ma or even active at present. Recently, Valantinas and Schultz pointed out that some of these wrinkle ridges show an excess abundance of meter-scale blocks in Diviner rock abundance data, which supports the idea that tectonic activity must be recent, because the half-life of meter-scale blocks on the Moon is only 40-80 Ma. These wrinkle ridges also have sharp topography and cross-cutting relationships with fresh craters that also speak to their youth. We argue here that these block-rich, young wrinkle ridges are interesting exploration targets. We first describe some objectives and concepts for exploring these landforms in a general sense, and then focus on one candidate landing area, Dorsa Aldrovandi.

Moon↗
NASA NTRSDate not supplied

Exploring Controls on the Fluvial Breaching of Degraded Impact Craters

Many lakes on early Mars were hosted in degraded impact craters. Past work has suggested that landscape erosion and crater degradation were very active prior to the valley network (VN)-forming era on Mars. Rim relief reduction during degradation may have primed craters for subsequent inlet incision during the VN-forming era. However, not all old, rimless craters on Mars are breached, so extensive rim degradation could not have been the only control on VN inlet formation. In this study, we ask: Why were some, but not all, degraded craters breached during the VN-forming era? Here, we focus on testing whether there are any measurable distinctions between VN-breached and non-breached degraded Noachian craters.

E R Bamber↗
NASA NTRSDate not supplied

Using Secondary Craters to Assess Strength Differences Between Neighboring Surface Units

Introduction: An issue with Martian remote sensing studies is the difficulty directly inferring surface and crustal material properties. For direct measurements we currently rely on the instruments of landed missions, like InSight. However, these are limited to specific locations (i.e., Elysium Planitia for InSight). To understand how results from in situ missions apply to other parts of Mars’ crust, we would like to apply a methodology that uses remote sensing. We are exploring using secondary craters, which are often thought of as nuisances when finding accurate relative ages, to get at these properties. An upside of secondary craters is that they form by ejecta blocks moving at relatively slow speeds (compared to primary impacts), which lets them reside in a different crater scaling regime [1], one that is more sensitive to the crust’s effective strength (Yeff) and its porosity. Secondary Crater Scaling: There are multiple parameters that influence the size of secondary craters, but we can measure craters in ways that isolate the dependence of crater size to just Yeff. We focus on secondary clusters that have impacted into distinct but bordering geological units. These secondaries can be used to determine Yeff thanks to one important fact: all the secondaries in this cluster are from the same distant primary, and as a result, the ejecta velocity, median block sizes, and densities are expected to be the same (at least to first order). Thus, variations between crater size on the two units are due to material property differences (i.e. Yeff). Thanks to the steep size frequency distributions (SFD) of secondaries [2], observable secondary crater sizes are tightly clustered, and we can simply use differences in median secondary sizes between two geologic units to constrain the ratio of Yeff. We are also able to use the SFD of our counting areas to check if our counted craters are secondaries (i.e. have a steep distribution), and if the two crater counting areas are from the same source (and thus have the same power law slope), but are offset from each other at around the same factor as the median difference.

Mars; Craters; Crustal Strength↗
NASA NTRSDate not supplied

Age Estimates for Permanently Shadowed Craters in the VIPER Mission Area Based On Their Topography

A primary objective of the VIPER [1] mission is to characterize the distribution and physical state of volatiles at the lunar poles, including within permanently shadowed regions (PSRs) where water ice has been inferred to be stable [e.g.. 2,3]. A mission area for VIPER has been defined that enables this scientific objective near Nobile crater (Fig. 1)[4]. This location enables a traverse that can both meet VIPER’s engineering constraints (Earth-direct communication, adequate power, etc.)as well as accomplish the planned scientific exploration. In this abstract, we describe observations of crater topography that provide insight into the age of several craters that host PSRs within the planned VIPER mission area. The role that the age of PSRs plays in controlling the presence or absence of polar volatiles is of substantial interest for discerning volatile history [e.g., 5-7]. The physical state, depth distribution, and spatial distribution of volatile deposits may also vary as a function of PSR age due to gardening and/or differing emplacement mechanisms [8]. Understanding the age of PSRs that VIPER may explore is thus a useful goal.

C I Fassett↗
NASA NTRSDate not supplied

Application of Support Vector Regression to Derive Crater Depth/Diameter From Satellite Images

Through the study of impact crater shapes, one can draw important conclusions about the nature and evolution of planetary surfaces [e.g., 1-4].In particular, studying the depth (d) to diameter (D)ratio (d/D) of a population of impact craters, in combination with crater count statistics, can yield valuable insights regarding rates of erosion and burial[5]. Motivated by the great abundance of available planetary surface image data, the goal of this project is to develop an efficient way to estimate d/D from satellite images of impact craters for which stereo information is not available [6]. We set out to develop and train a machine learning algorithm to extract d/D from a dataset of synthetic impact crater images for which model d/D is known. The applications of machine learning to planetary science are numerous and diverse [7], including automatic planetary surface mapping [8] and the detection of impact craters [9]. Our algorithm makes use of Support Vector Regression (SVR), which is a type of Support Vector Machine (SVM) [10, 11].SVMs are a branch of supervised machine learning valued for their straightforward implementation and versatility in solving both classification and regression problems. In regression analysis, an SVR algorithm produces a hyperplane function to fit the training data points, as well as an ε-tube that surrounds the hyperplane. Tunable hyperparameters include the width of the ε-tube (ε) and the amount an algorithm is penalized for points which fall outside the ε-tube.

L R Chin↗