Tag Archives: Topography

Moon

An Artificial Intelligence identifies the Lunar craters

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Hi there! Today we discuss about an algorithm. I guess you have already heard of Artificial Intelligence, and especially of some futuristic anticipations in which the world would be governed by robots… Fortunately, we are very far from that.

An Artificial Intelligence (AI), Google DeepMind’s AlphaGo, beat the Go professional player Lee Sedol in March 2016. Another AI, IBM’s DeepBlue, beat the then chess world champion Garry Kasparov in May 1997. These realizations of specific tasks belong to Narrow AI, while General AI would be Star Trek’s Data, i.e. a man-made system able to interact with its environments, and react better than a human would do.

Brent Spiner performing Data in Star Trek. © CBS Television Distribution
Brent Spiner performing Data in Star Trek. © CBS Television Distribution

In the context of planetary sciences, AI has recently permitted the discovery of exoplanets, from data analysis. The study I present today, Lunar crater identification via Deep Learning, by Ari Silburt et al., is another example of the way Artificial Intelligence may assist science. In this paper, the author challenge the computer to identify craters from images of the Moon and of Mercury, and the results appear to be very promising. This study has recently been accepted for publication in Icarus.

Outline

Craters in the Solar System
Artificial Intelligence, Machine Learning, and Deep Learning
Use of Digital Elevation Models of the Moon
Algorithm of crater identification
The relevant parameters
Success for the Moon
Successful transfer-learning to Mercury
The study and its authors

Craters in the Solar System

Let us forget AI for a short while. This is a blog of planetary sciences, remember? AI is a tool, not a goal. This study challenges a tool, which goal will be to identify craters. Hence, the goal is the craters.

Craters are ubiquitous in the Solar System, since it is intensively bombarded. This is especially true for the inner Solar System, since the impactors, i.e. small rocky bodies, are gravitationally attracted by the Sun. And while passing by, they may hit us, or the Moon, Mercury, Mars,… And we are lucky, since the current bombardment is much less intense than it was in the youth of the Solar System. Anyway, we have been intensively bombarded, we still are, but our atmosphere protects us in destroying the impactors, which reach the terrestrial surface as meteorites. A huge impactor is thought to be responsible for the extinctions of the dinosaurs, and may be one day… no, better not to think about it.

There are not so many craters at the surface of the Earth, partly because our atmosphere has eroded them, and partly because the geophysical activity relaxed them. But what about atmosphereless bodies like our Moon? Craters are everywhere!

And craters are the clock of the surface. If you see only craters, it means that the surface has not changed since the impact. The surface did not heat, did not melt, there was no geophysical activity creating failures, ridges,… For instance, in the satellites of Jupiter, you see almost no craters on Io and Europa, since there are active bodies. You see some on Ganymede, which is less active, and much more on Callisto, which is quieter.

The surfaces of the Galilean satellites of Jupiter Europa (left), Ganymede (middle), and Callisto (right), seen by Galileo. © NASA
The surfaces of the Galilean satellites of Jupiter Europa (left), Ganymede (middle), and Callisto (right), seen by Galileo. © NASA

This is why it is worthwhile to catalogue the craters of a given planetary body. But since it is a difficult and exhausting task, it is probably a good idea to tell a computer how to do it. This is where AI and Deep Learning come into play.

Artificial Intelligence, Machine Learning, and Deep Learning

As I told you, we are here interested in narrow AI: we want a computer to perform a specific task, and only that task, better than a human would do. And we want the computer to learn how to do it: this is Machine Learning. We give images of craters, tell the computer that they are craters, and we hope it to identify craters on images, which have not been studied yet, i.e. new data. Very well.

A common algorithm for that is Deep Learning. I do not want to go into specifics, but this uses several layers of neurons. Here, neurons should be understood as computing nodes performing a specific sub-tasks, and interacting with each others. The analogy with the human brain is obvious. In this specific case, the authors used convolutional neural networks, in which neurons layers are structured to perform a discrete convolution (a mathematical operation) between their inputs and a filter, which is represented by weights. These weights permit to ponder the relative roles of the different inputs of the system, i.e. which input is more relevant than another one…

The inputs are the planetary data.

Use of Digital Elevation Models of the Moon

Fortunately, we dispose of numerous topographic data of the Moon, which make it the ideal target for elaborating such an algorithm.

The authors used digital elevation maps (DEM) of the Moon, resulting from 2 different missions:

  1. the Lunar Reconnaissance Orbiter (LRO). This is an American mission, which orbits the Moon since 2009. It has made a 3-D map of the Moon’s surface at 100-meter resolution and 98.2% coverage, thanks to the Lunar Orbiter Laser Altimeter (LOLA), and the Lunar Reconnaissance Orbiter Camera (LROC).
  2. The Japanese mission SELENE (Selenological and Engineering Explorer), which is also known as Kaguya. It was composed of 3 spacecraft, i.e. a main orbiter and two satellites. It operated during 20 months, between September 2007 and June 2009. It was then intentionally crashed near the crater Gill.

The authors used such data to train the system, i.e. to make itself an expert in crater identification.

Algorithm of crater identification

Historically, the first identifications of craters were made by visually examining the images. Of course, this is an exhaustive task, and the human being has failures. Moreover, if a small crater looks like a circle, larger ones, i.e. with a diameter larger than 20 km, may have a central peak, may contain other craters, and/or may be altered by the topography (mountains…).

The consequence is that beside the time spent to perform this task, you would have false detections (you think this is a crater, but it is not), and miss some craters, especially the smallest ones. If someone else does the same task, from the same data, (s)he would get a significantly different list. Comparing these lists would be a way to estimate the identification errors.

So, you can see that the use of the numerical tool is inescapable. But how would you do that? Some algorithms identify the circles on the images thanks to a Hough transform (I do not want to go into specifics, but this is a mathematical transformation of your images which tells you “there is a circle there!”), some identify the edges of the craters, some do both… And Deep Learning is learning by itself how to identify craters.

This consists essentially of 3 steps:

  1. training,
  2. validation,
  3. tests.

The algorithm detects crater rims from their pixel intensities, then fits a circle on them, and give as outputs the coordinates of the center and the mean radius of the crater. The authors then compared the results with existing catalogues.

The relevant parameters

The detection of the craters uses parameters, for instance the threshold for the detection of variation of pixel intensities. And the efficiency of the algorithm is measured with

  1. the true positives Tp (the algorithm tells you there is a crater, and you know there is actually one),
  2. the false positives Fp (the algorithm tells you there is a crater, but there is none),
  3. the false negatives Fn (the algorithm tells you nothing, while you know there is a crater),

and these quantities are recombined as

  • the precision P = Tp/(Tp+Fp) (if the algorithm tells you there are 100 craters, how many are actually present?),
  • the recall R = Tp/(Tp+Fn) (over 100 craters, how many are detected by the algorithm?)
  • F1 = 2PR/(P+R), which permits to use a single-parameter metric.

    • The goal is of course to maximize F1.

    Beside this, the authors also compared the coordinates and radii of the detected craters with the ones present in the catalogues, i.e. which had been previously determined by other methods. And all of this works pretty well!

    Success for the Moon

    The algorithm detected 92% of the known craters. Moreover, it also announced to the authors the detection of 361 new craters, and showed to be particularly efficient for craters with a diameter smaller than 5 km. Not only these small craters are a challenge for the human eye, but their regular shape makes the automatic detection more reliable. So, you here have an example of a task, for which the computer could be more efficient than the human. Among these 361 new craters, the authors estimate 11% of them to be false positives (Fp). This last number has some uncertainty, since the validation of a crater is made by a human eye, and the outcome depends on the brain, i.e. the human, behind the eye.

    This is very promising but would that work on another body? It seems so…

    Successful transfer-learning to Mercury

    Finally, the authors asked the computer to identify craters on the surface of Mercury. Remember that the computer was trained with Lunar data. This is called a domain shift, and this is a challenge, since the surface of Mercury has not exactly the same properties of the Lunar one. The bombardment activity was different, Mercury was possibly partly resurfaced, the material itself is different…

    The Moon is on the left, and Mercury on the right.
    The Moon is on the left, and Mercury on the right.

    But the results are pretty good, i.e. many craters are actually detected.

    The algorithm needs some refinements. For instance, it may be lured by circular depressions, which are not true craters (false positives). But the results are very encouraging, in particular for identifying craters on bodies, for which no catalogue exists at this date. The last space missions have given Digital Elevation Models for Venus, Mars, Vesta, and Ceres, and this algorithm may prove very useful to identify their craters.

    Deep Learning is the future!

    The study and its authors

    And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and Pinterest.

Planets

There was water on Mars

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Hi there! Well, we already knew that there has been liquid water on the surface of Mars, a long time ago. Indeed, the space mission Mariner 9 imaged valley networks in 1972. Since then, several missions refined the data. The study I present today, Estimate of the water flow duration in large Martian fluvial systems, by Vincenzo Orofino, Giulia Alemanno, Gaetano Di Achille and Francesco Mancarella, uses the most recent observations to estimate the length and depth of former Martian rivers, and their duration of formation and erosion. This study has recently been accepted for publication in Planetary and Space Science.

Outline

Evidences of liquid water in the past
The geologic history of Mars
Use of recent data
Dynamics of formation of a river bed
Intermittence and humidity
No icy Noachian
The study and its authors

Evidences of liquid water in the past

The current atmosphere of Mars is pretty thin, its pressure being on average 0.6% the one of the Earth. Such a small atmospheric pressure prevents the existence of liquid water at the surface. Water could survive only as ice, otherwise would be just vaporized. And ice water has been found, particularly in the polar caps. But if the atmosphere were thicker in the past, then liquid water would have survived… and we know it did.

We owe to Mariner 9 a map of 85% of the Martian surface, which revealed in particular river beds, deltas, and lake basins. The study we discuss today focused on valley networks, which are particularly present in the southern highlands of Mars. These valleys are typically less than 5 km wide, but may extend over thousands of kms, and they reveal former rivers.

Nirgal Vallis seen by Mariner 9. © NASA
Nirgal Vallis seen by Mariner 9. © NASA

The history of these rivers is inseparable from the geological history of Mars.

The geologic history of Mars

We distinguish 3 mains eras in the geological history of Mars: the Noachian, the Hesperian, and the Amazonian.

The Noachian probably extended between 4.6 and 3.7 Gyr ago, i.e. it started when Mars formed. At that time, the atmosphere of Mars was much thicker that it is now, it generated greenhouse effect, and liquid water was stable on the surface. It even probably rained on Mars! During that era, the bombardment in the inner Solar System, including on Mars, was very intense, but anyway less intense than the Late Heavy Bombardment, which happened at the end of the Noachian. Many are tempted to consider it to be the cause of the change of era. Anyway, many terrains of the south hemisphere of Mars, and craters, date from the Noachian. And almost all of the river beds as well.

After the Noachian came the Hesperian, probably between 3.7 and 3.2 Gyr ago. It was a period of intense volcanic activity, during which the bombardment declined, and the atmosphere thinned. Then came the Amazonian, which is still on-going, and which is a much quieter era. The volcanic activity has declined as well.

So, almost all of the valley networks date from the Noachian. Let us now see how they formed.

Use of recent data

We owe to the space missions accurate maps of Mars. From these maps, the authors have studied a limited data set of 63 valley networks, 13 of them with a interior channel, the 50 remaining ones without. The interior channel is the former river bed, while the valley represents the area, which has been sculpted by the river. The absence of interior channel probably means that either they are too narrow to be detectable, or have been eroded.

These valley networks are located on sloppy areas, most of them close to the equator. The authors needed the following information:

  1. area,
  2. eroded volume,
  3. valley slopes,
  4. width and depth of the interior channel.

To get this information, they combined topographic data from the instrument MOLA (for Mars Orbiter Laser Altimeter) on board Mars Global Surveyor (1997-2006) with THEMIS (THermal Emission Imaging System, on board Mars Odyssey, still operating). MOLA permits 3-D imagery, with a vertical resolution of 30 cm/pixel (in other words, the accuracy of the altitude) and a horizontal one of 460 m/pixel, while the THEMIS data used by the authors are 2D-data, with a resolution of 100 m /pixel. When the authors judged necessary, they supplemented these data with CTX data (ConTeXt camera, on board Mars Reconnaissance Orbiter, still ongoing), with a resolution up to 6 m/pixel.

These information are very useful to estimate the formation time and the erosion rate of the valley network.

Dynamics of formation of a river bed

They estimated these quantities from the volume of sediments, which should have been transported to create the valley networks. The idea is, while water is flowing, assisted by the Martian surface gravity (fortunately, this number is very well known, and is roughly one-third of the gravity on Earth) and by the slope, it transports material. The authors assumed in their calculations that this material was only sediments, i.e. they neglected rock transport, and they did the maths.

Several competing models exist for sediment transport. This is actually difficult to constrain, given the uncertainties on the sediments themselves. Such phenomena also exist on Earth, but the numbers are very different for instance if you are in Iceland or in the Atacama Desert.

It also depends on the intermittence: is the water flow constant? You can say yes to make your life easier, but is it true? On Earth, you have seasonal variations… why not on Mars? A constant water flow means an intermittence of 100%, while no water means 0%.

And keep also in mind that the water flow depends on the atmospheric conditions: is the air wet or pretty arid? We can answer this question for the present atmospheric conditions, but how was it in the Noachian?

No icy Noachian

And this is one result of the present study: there must have been some evaporation in the Noachian, which means that it was not cold and icy. The authors show that such a Noachian would be inconsistent with the valley networks, as we presently observe them.

However, they get large uncertainties on the formation timescales of the valley networks, i.e. between 500 years and almost twice the age of the Solar System. They have anyway median numbers, i.e.

  • 30 kyr for a continuous sediment flow,
  • 500 kyr with an intermittence of 5%,
  • 3 Myr with an intermittence of 1%,
  • 30 Myr with an intermittence of 0.1%.

And from the data, they estimate that the intermittence should be in the range 1%-5%, which corresponds humid (5%) and semiarid/arid environments. This is how they can rule out the cold and icy Noachian.

The study and its authors

And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and Pinterest.

Planets

Evolution of Venus’ crust

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Hi there! Of course, you know Venus. This planet is sometimes nicknamed the twin sister of the Earth, but beside its size, it does not look like the Earth. Venus is closer to the Sun than us, and it has a very thick atmosphere, which is essentially composed of carbon dioxide. This atmosphere has a pressure of 93 bar at the surface of the planet, to be compared with 1 bar for the Earth, and the temperature reaches there 470°C. Definitely hostile.

Anyway, I do not speak of the atmosphere today, but of the surface. I present Inferences on the mantle viscosity structure and the post-overturn evolutionary state of Venus, by T. Rolf and collaborators, which has recently been published in Icarus.

Outline

The interior of Venus
Gravity and topography
Modeling the crustal evolution
Stagnant-lid vs. overturn
Overturn should have happened
The study and its authors

The interior of Venus

Given its size, i.e. a diameter of 12,000 km, which is 95% of the one of the Earth, Venus must be differentiated. It has a crust, a mantle, and core, with increasing densities when you go deeper below the surface. We think the crust to be essentially basaltic, while the core must contain heavy elements. Surprisingly, the space missions did not detect any magnetic field, which means that the core may be not solid, or may be not cooling…

The outer part of the mantle should be fluid, which means that a fluid layer separates the core from the mantle. We know very few of the thicknesses and the compositions of these different layers. Actually, these could only be guessed from the measurements we dispose on, which are the gravity and the topography (see just below). Once you know the gravity field of Venus and its topography, you can elaborate interior models, which would be consistent with your data.

Gravity and topography

First, gravity. When a small body, like an artificial satellite, orbits a spherical planetary body, the gravitational perturbation affecting its motion depends only on the distance between the satellite and the planet. Now, if the planet is not spherical, and has mass anomalies, then the perturbation will not only depend on the distance, but also on the direction planet-satellite. You can determine the gravity field from the orbital deviation of your spacecraft.

It is convenient to write the gravity field as a sum of spherical harmonics. The first term (order 0) is a spherical one, then the order 2 (you have no order 1 if the center of your reference frame is the center of mass) represents the triaxiality of the planet, i.e. the planet seen as a triaxial ellipsoid. And the higher order terms will represent anomalies, with increasing resolutions. These resolutions are modeled as spatial periods. Such a representation has usually an efficient convergence, except for highly elongated bodies (see here).

We use such a representation for the topography as well. The difference is that the result is not the gravity field in any direction, but the altitude of the surface for a given point, i.e. a latitude and a longitude. The spacecraft measure the topography with a laser, which echo gives you the distance between the spacecraft and the surface. The altitude is directly deduced from this information.

Topography of Venus. The altitude variations are about 13 km with respect to a reference ellipsoid. © Calvin Hamilton, Johns Hopkins University Applied Physics Laboratory
Topography of Venus. The altitude variations are about 13 km with respect to a reference ellipsoid. © Calvin Hamilton, Johns Hopkins University Applied Physics Laboratory

The best representations we dispose on for Venus come from the American spacecraft Magellan, which orbited Venus between 1990 and 1994. These representations go to the order 180.

Modeling the crustal evolution

In this study, the authors simulated possible evolutionary paths for the crust of Venus, and compared their results with the present Venus, i.e. the gravity and topography as we know them.

For that, they simulated the thermochemical evolution of Venus in using a numerical code, StagYY. This is a 3D-code, which models convection in the mantle, i.e. internal motions. This code is based on finite elements, i.e. the interior of Venus is split into small elements. This splitting is made following a so-called Yin-Yang grid, which is appropriate for spherical geometries. This code includes several features like phase transition (i.e. from solid to fluid, and conversely), compositional variations, partial melting and melt migration. Moreover, it is implemented for parallel computing.

In other words, these are huge calculations. The authors started with 10 simulations in which the crust was modeled as a single plate, i.e. a stagnant lid. The simulations differed by the modeling of the viscosity, and by the radiogenic heating rate. This is the heating of Venus by the decay of the radiogenic elements, which was most effective in the early Solar System.

Once these 10 simulations have run, the authors kept the one, which resulted in the closest Venus to the actual one, and introduced episodic overturns in it.

Stagnant-lid vs. overturn

Venus does not present any tectonic activity. Did it have some in the past? This is a question this study tried to answer.

An overturn is a sudden peak in the heat transfer from the core to the crust through the mantle, due to a too strong difference of temperature, i.e. when the mantle gets colder. Such an episodic phenomenon is triggered by a too thick crust, and results in a melting of this crust, in heating it. In other words, it regulates the thickness of the crust.

Overturns should have happened

And here are the results: the best stagnant-lid scenario, called S2 in the study, presents some discrepancy between the simulated present Venus and the observed one. These discrepancies are present in the topography, in the gravity field, and in the age of the surface. The surface is estimated to be between 0.3 and 1 Gyr old, while the best stagnant-lid scenario predicts that the most probable age is 0.25 Gyr… a little too young.

However, episodic overturns give a surface, which is 0.6 Gyr old. Moreover, the gravity and topography are much better fit. The only remaining problem is that this scenario should result in much detections of plumes than actually detected.

As the authors honestly recall, some physical phenomena were not considered, in particular the influence of the dense atmosphere, and intrusive volcanism. Anyway, this study strongly suggests that episodic overturn happened.

Further data will improve our understanding of Venus. Recently, the European Space Agency (ESA) has pre-selected 3 potential future space missions, including EnVision, i.e. an orbiter around Venus. The final decision is expected in 2021.

The study and its authors

And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and Pinterest.

Satellites of Jupiter

Fracturing the crust of an icy satellite

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Hi there! You may know that the space missions to the systems of giant planets have revealed that the surface of several of theirs satellites are fractured. We dispose of images of such structures on Jupiter’s Europa and Ganymede, Saturn’s Enceladus (the famous tiger stripes at its South Pole), and even on Uranus’ Miranda, which has been visited by Voyager II. These satellites are thought to be icy bodies, with an icy crust enshrouding a subsurface, global ocean (maybe not for Miranda, but certainly true for the other guys).

The study I present you today, Experimental constraints on the fatigue of icy satellite lithospheres by tidal forces, by Noah P. Hammond, Amy C. Barr, Reid F. Cooper, Tess E. Caswell, and Greg Hirth, has recently been accepted for publication in Journal of Geophysical Research: Planets. The authors particularly tried to produce in labs the process of fatigue, which would weaken a material after a certain number of solicitations, i.e. it would become easier to break.

Outline

Cycloids on Europa
Tides can stress the surface
The phenomenon of fatigue crack growth
Lab experiments
No fatigue observed
Why fatigue may still be possible
How to fracture without fatigue
The study and its authors

Cycloids on Europa

The Galilean satellite of Jupiter Europa may be the most interesting satellite to focus on, since it is the most fractured, at least to the best of our knowledge. The observation of the surface of Europa, first by Voyager I and II in 1979, and after by Galileo between 1995 and 2003, revealed many structures, like lineae, i.e. cracks, due to the geophysical activity of the satellite. This body is so active that only few craters are visible, the surface having been intensively renewed since the impacts. Something particularly appealing on Europa is that some of these lineae present a cycloidal pattern, which would reveal a very small drift of the orientation of the surface. Some interpret it has an evidence of super-synchronous rotation of Europa, i.e. its rotation would not be exactly synchronous with its orbital motion around Jupiter.

Cycloids on Europa, seen by the spacecraft Galileo. © NASA
Cycloids on Europa, seen by the spacecraft Galileo. © NASA

Beside Europa, fractures have also been observed on Ganymede, but with less frequency. For having such fractures, you need the surface to be brittle enough, so that stress will fracture it. This is a way to indirectly detect a subsurface ocean. But you also need the stress. And this is where tides intervene.

Fractures on Ganymede. © Paul M. Schenk
Fractures on Ganymede. © Paul M. Schenk

Tides can stress the surface

You can imagine that Jupiter exerts a huge gravitational action on Europa. But Europa is not that small, and its finite size results in a difference of Jovian attraction between the point which is the closest to Jupiter, and the furthest one. The result of this differential attraction is stress and strain in the satellite. The response of the satellite will depend on its structure.

A problem is that calculations suggest that the tidal stress may be too weak to generate alone the observed fractures. This is why the authors suggest the assistance of another phenomenon: fatigue crack growth.

The phenomenon of fatigue crack growth

The picture is pretty intuitive: if you want to break something… let’s say a spoon. You twist it, bend it, wring it… once, twice, thrice, more… Pretty uneasy, but you do not give up, because you see that the material is weakening. And finally it breaks. Yes you did it! But what happened? You slowly created microcracks in the spoon, which weakened it, the cracks grew… until the spoon broke.

For geophysical materials, it works pretty much the same: we should imagine that the tides, which vary over an orbit since the eccentricity of the orbit induces variations of the Jupiter-Europa distance, slowly create microcracks, which then grow, until the cracks are visible. To test this scenario, the authors ran lab experiments.

Lab experiments

The lab experiments consisted of Brazil Tests, i.e. compression of circular disks of ice along their diameter between curved steel plates. The resulting stress was computed everywhere in the disk thanks to a finite-element software named Abaqus, and the result was analyzed with acoustic emissions, which reflections would reveal the presence of absence of microcracks in the disk. The authors ran two types of tests: both with cyclic loading, i.e. oscillating loading, but one with constant amplitude, and the other one with increasing amplitude, i.e. a maximum loading becoming stronger and stronger.

But wait: how to reproduce the conditions of the real ice of these satellites? Well, there are things you cannot do in the lab. Among the problems are: the exact composition of the ice, the temperature, and the excitation frequency.

The authors conducted the experiments in assuming pure water ice. The temperature could be below 150 K (-123°C, or -189°F), which is very challenging in a lab, and the main period of excitation is the orbital one, i.e. 3.5 day… If you want to reproduce 100,000 loading cycles, you should wait some 1,000 years… unfeasible…

The authors bypassed these two problems in constraining the product frequency times viscosity to be valid, the viscosity itself depending on the temperature. This resulted in an excitation period of 1 second, and temperatures between 198 and 233 K (-75 to -40°C, or -103 to -40°F). The temperature was maintained thanks to a liquid nitrogen-cooled, ethanol bath cryostat.

And now the results!

No fatigue observed

Indeed, the authors observed no fatigue, i.e. no significant microcracks were detected, which would have altered the material enough, to weaken it. This prompted the authors to discuss the application of their experiments for understanding the crust of the real satellites, and they argue that fatigue could be possible anyway.

Why fatigue may still be possible

As the authors recall, these experiments are not the first ones. Other authors have had a negative result with pure water ice. However, fatigue has been detected on sea ice, which could mean that the presence of salt favors fatigue. And the water ice of icy satellite may not be pure. Salt and other chemical elements may be present. So, even if these experiments did not reveal fatigue, there may be some anyway.

But the motivation for investigating fatigue is that a process was needed to assist the tides to crack the surface. Why necessarily fatigue? Actually, other processes may weaken the material.

How to fracture without fatigue

The explanation is like the most (just a matter of taste) is impacts: when you impact the surface, you break it, which necessarily weakens it. And we know that impacts are ubiquitous in the Solar System. In case of an impact, a megaregolith is created, which is more likely to get fractured. The authors also suggest that the tides may be assisted, at least for Europa, by the super-synchronous rotation possibly suggested by the geometry of the lineae (remember, the cycloids). Another possibility is the large scale inhomogeneities in the surface, which could weaken it at some points.

Anyway, it is a fact that these surfaces are fractured, and the exact explanation for that is still in debate!

The study and its authors

And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and Pinterest.

Planets

Ice on Mercury

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Hi there! You know Mercury, the innermost planet of the Solar System. It has recently been explored during 4 years by the American spacecraft MESSENGER, which gave us invaluable data on its surface, its magnetic field, its interior…
Today I present you a study on the ice on Mercury. It is entitled Constraining the thickness of polar ice deposits on Mercury using the Mercury Laser Altimeter and small craters in permanently shadowed regions, by Ariel N. Deutsch, James W. Head, Nancy L. Chabot & Gregory A. Neumann, and has recently been accepted for publication in Icarus.
We know that there is some ice at the surface of Mercury, and the study wonders how much. Since Mercury is close to the Sun, its surface is usually hot enough to sublimate the ice… except in permanently shadowed regions, i.e. in craters. For that, the authors compared the measured depth of small craters, and compared it with the expected depth from the excavation of material by an impactor. The difference is supposed to be ice deposit.

Outline

Mercury and MESSENGER
Ice on Mercury
Ice is still present in craters
Results: how much ice?
The origin of ice
Another spacecraft soon
The study and its authors

Mercury and MESSENGER

The planet Mercury is known at least since the 14th century BC. It was named after the Roman messenger god Mercurius, or Hermes in Greek, since the messengers saw it at dawn when they left, and at dusk when they arrived. The reason is that Mercury is in fact pretty close to the Sun, i.e. three times closer than our Earth. So, usually the Sun is so bright that it prevents us from observing it. Unless it is below the horizon, which happens at dawn and at dusk.

Mercury makes a full revolution around the Sun in 88 days, and a full rotation in 58 days. This 2/3 ratio is a dynamical equilibrium, named 3:2 spin-orbit resonance, which has been reached after slow despinning over the ages. This despinning is indeed a loss of energy, which has been favored by the tidal (gravitational) action of the Sun. This resulting spin-orbit resonant configuration is a unique case in the Solar System. A consequence is that the Solar day on Mercury lasts 176 days, i.e. if you live on Mercury, the apparent course of the Sun in the sky lasts 176 days.

The proximity of the Sun makes Mercury a challenge for exploration. Mariner 10 made 3 fly-bys of it in 1974-1975, mapping 45% of its surface, and measuring a tiny magnetic field. We had to wait until 2011 for the US spacecraft MESSENGER (MErcury Surface, Space ENvironment, GEochemistry and Ranging) to be the first human-made object inserted into orbit around Mercury. The orbital phase lasted 4 years, and gave us a full map of the planet, gravity data, accurate measurements of its rotation, a list of craters, measurements of the magnetic field,…

The instrument of interest today is named MLA, for Mercury Laser Altimeter. This instrument used an infrared laser (wavelength: 1,064 nanometers) to estimate the height of the surface from the reflection of the laser: you send a laser signal, you get it back some time later, and from the time you have the distance, since you know the velocity, which is the velocity of the light. And in applying this technique all along the orbit, you produce a map of the whole planet. This permits for instance to estimate the size and depth of the craters.

The Mercury Laser Altimeter (MLA).
The Mercury Laser Altimeter (MLA).

Ice on Mercury

The discovery of ice at the poles of Mercury was announced in 1992. It was permitted by Earth-based radar imagery made at Goldstone Deep Space Communications Complex in the Mojave desert, in California (USA). Ice is pretty easy to uncover, because of its high reflectivity. But this raises some questions:

  1. How can ice survive on Mercury?
  2. How much ice is there?
  3. How did it arrive?
The Goldstone facilities in 2018. © Google
The Goldstone facilities in 2018. © Google

The first question is not really a mystery. Because of its long Solar day and its absence of atmosphere (actually Mercury has a very tenuous exosphere, but we can forget it), Mercury experiences huge variations of temperature between day and night, i.e. from 100K to 700K, or -173°C to 427°C, or -279°F to 801°F (it is in fact not accurate at the 1°F level…). So, when a region is illuminated, the water ice is definitely not stable. However, there are regions, especially at the poles, which are never illuminated. There ice can survive.

The last two questions are answered by this study.

Ice is still present in craters

For not being illuminated, it helps to be close to a pole, but the topography can be helpful as well. The surface of Mercury is heavily cratered, and the bottoms of some of these craters are always hidden from the Sun. This is where the authors looked for ice. More precisely, they investigated 10 small craters within 10 degrees of the north pole. And for each of them, they estimated the expected depth from the diameter, and compared it with the measured depth. If it does not match, then you have water ice at the bottom. Easy, isn’t it?

The Carolan crater, one of the craters studied. © NASA
The Carolan crater, one of the craters studied. © NASA

Well, it is not actually that easy. The question is: did the water ice arrive after or before the excavation of the crater? If it arrived before, then the impactor just excavated some ice, and the measurements do not tell you anything.

Another challenge is to deal with the uncertainties. MLA was a wonderful instrument, with an accuracy smaller than the meter. Very well. But you are not that accurate if you want to predict the depth of a crater from its diameter. The authors used an empirical formula proposed by another study: d=(0.17±0.04)D0.96±0.11, where d is the depth, and D the diameter. The problem is the ±, i.e. that formula is not exact. This uncertainty is physically relevant, since the depth of the crater might depend on the incidence angle of the impact, which you don’t know, or on the material at the exact location of the impact… and this is a problem, since you cannot be that accurate on the theoretical depth of the crater. The authors provide a numerical example: a 400-m diameter crater has an expected depth between 21.2 and 127.7 m… So, there is a risk that the thickness of ice that you would measure would be so uncertain that actual detection would be unsure. And this is what happens in almost of all the craters. But the detection is secured by the fact that several craters are involved: the more data you have, the lower the uncertainties. And the ice thickness derived from several craters is more accurate than the one derived from a single crater.

Results: how much ice?

And the result is: the ice thickness is 41+30-14m. The uncertainty is large, but the number remains positive anyway, which means that the detection is positive! Moreover, it is consistent with previous studies, from the detection of polar ice with Goldstone facilities, to similar studies on other regions of Mercury. So, there is ice on Mercury.
An extrapolation of this result suggests that the total mass of water ice on the surface of Mercury is “1014-1015 kg, which is equivalent to ~100-1,000 km3 ice in volume, assuming pure water ice with no porosity” (quoted from the study).

The origin of ice

Mercury is a dense planet, i.e. too dense for such a small planet. It is widely accepted that Mercury as we see it constituted a core of a proto-Mercury, which has been stripped from its mantle of lighter elements. Anyway, Mercury is too dense for the water ice to originate from it. It should come from outside, i.e. it has been brought by impactors. The authors cite studies stating that such a quantity could have been brought by micrometeorites, by Jupiter-family comets, and even by a single impactor.

Another spacecraft soon

Such a study does not only exploit the MESSENGER data, but is also a way to anticipate the future measurements by Bepi-Colombo. This mission will be constituted of two orbiters, one supervised by the European Space Agency (ESA), and the other one by the Japanese agency JAXA. Bepi-Colombo should be launched in October 2018 from Kourou (French Guiana), and inserted into orbit around Mercury in April 2026. Its accuracy is expected to be 10 times better than the one of MESSENGER, and the studies inferring results from MESSENGER data can be seen as predictions for Bepi-Colombo.

The study and its authors

And that’s it for today! Please do not forget to comment. You can also subscribe to the RSS feed, and follow me on Twitter, Facebook, Instagram, and (NEW) Pinterest.