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Measuring impact crater depth throughout the solar system
Author(s) -
Robbins Stuart J.,
Watters Wesley A.,
Chappelow John E.,
Bray Veronica J.,
Daubar Ingrid J.,
Craddock Robert A.,
Beyer Ross A.,
Landis Margaret,
Ostrach Lillian R.,
Tornabene Livio,
Riggs Jamie D.,
Weaver Brian P.
Publication year - 2018
Publication title -
meteoritics and planetary science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.09
H-Index - 100
eISSN - 1945-5100
pISSN - 1086-9379
DOI - 10.1111/maps.12956
Subject(s) - impact crater , variety (cybernetics) , geology , field (mathematics) , solar system , set (abstract data type) , data science , computer science , earth science , remote sensing , astrobiology , artificial intelligence , mathematics , physics , pure mathematics , programming language
One important, almost ubiquitous, tool for understanding the surfaces of solid bodies throughout the solar system is the study of impact craters. While measuring a distribution of crater diameters and locations is an important tool for a wide variety of studies, so too is measuring a crater's “depth.” Depth can inform numerous studies including the strength of a surface and modification rates in the local environment. There is, however, no standard data set, definition, or technique to perform this data‐gathering task, and the abundance of different definitions of “depth” and methods for estimating that quantity can lead to misunderstandings in and of the literature. In this review, we describe a wide variety of data sets and methods to analyze those data sets that have been, are currently, or could be used to derive different types of crater depth measurements. We also recommend certain nomenclature in doing so to help standardize practice in the field. We present a review section of all crater depths that have been published on different solar system bodies which shows how the field has evolved through time and how some common assumptions might not be wholly accurate. We conclude with several recommendations for researchers which could help different data sets to be more easily understood and compared.