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Lunar Launches of 2013

2013 was a fantastic year for Moon missions, providing a new orbiter, lander and even a rover to take a fresh look at the lunar surface and what lies below. These instruments are exploring both previously studied and completely new areas of lunar science, including a detailed in situ analysis of the chemical composition of the regolith, the characteristics and origin of the elusive lunar atmosphere, and even some experiments targeting the Earth! The next few years are also set to be some exciting ones with plenty of upcoming missions from around the world. The following is a brief overview of what the brand new lunar companions are up to, what is coming up in the near future, and how all of this could enhance our current understanding of the Earth’s natural satellite.

As you’ve probably heard, China recently launched their Chang’e 3 mission in December 2013, which safely landed on the Moon and immediately deployed its Jade Rabbit rover to begin exploring the surface. Just a few of months prior to this, in September, NASA sent its Lunar Atmosphere and Dust Environment Explorer (LADEE) mission, which is currently in orbit collecting data. These two missions joined three other operational lunar satellites: ex-Earth satellites ARTEMIS P1 + P2, and the Lunar Reconnaissance Orbiter (LRO) which supplies Moon Zoo with all its high resolution images.

LADEE represents the fifth NASA mission to the Moon since 2009 and is after some interesting new data regarding the tenuous lunar atmosphere. The role of dust transport through the Moon’s exosphere is still very poorly understood, so this satellite is designed to study the process in detail as well as analyse general atmospheric properties. The payload contains a UV and visible spectrometer to study the composition of dust grains, a neutral mass spectrometer to search for noble gases present, and a lunar dust experiment to measure grain impacts at different altitudes. Interestingly, the module also carries a laser communication experiment, which is investigating the viability of using lasers to beam back data to Earth. This technology could be incredibly valuable for future missions farther into space as high volumes of data can be transmitted more efficiently than using radio waves.

Artist impression of NASA's LADEE spacecraft orbiting the Moon. NASA / Ames Reseach Center / Dana Berry

Artist impression of NASA’s LADEE spacecraft orbiting the Moon.
NASA/ Ames Research Centre/ Dana Berry

So far the mission has already detected atmospheric helium, sodium, neon, potassium and argon-40, as well as confirming a lunar dust exosphere which before was only predicted.  The instrument, orbiting between 20 and 150 km, has detected very few dust grains at high altitudes, but this value significantly increased as it descended. LADEE has also measured a few bursts of dust, which are thought to be a consequence of flying through ejecta caused from the impacts of nearby meteorites into the lunar regolith.

Just a few months into LADEE’s mission, the Chinese landed their first craft on another planetary body.  The touchdown of Chang’e 3 came more than 40 years after the US sent the first astronauts to walk upon the lunar surface; nonetheless, this does not diminish the mission’s significance at all. The craft, testing out some really exciting new technology and equipment, will be transferring its findings to manned or unmanned missions in the future, heading for the Moon, Mercury or Mars!

360 degree view taken by Chang'e 3.  Chinese Academy of Sciences

360 degree view taken by Chang’e 3.
Chinese Academy of Sciences

The primary mission objective of Chang’e 3 is to analyse the lunar soil composition in situ in much more detail than previously achieved. Its rover, Yutu, or ‘Jade Rabbit’ in English, thus aims to further our understanding of the Moon’s – and in turn the Earth’s – history. At just 1.5 m across, Jade Rabbit carries ground-penetrating radar, cameras, a telescope and spectroscopic instruments to help achieve this goal.

Image of Yutu/ Jade Rabbit taken by Chang'e 3.  Chinese Academy of Sciences

Image of Yutu/ Jade Rabbit taken by Chang’e 3.
Chinese Academy of Sciences

Chang’e 3’s landing destination on the north edge of Mare Imbrium is a different site to those previously visited by the US and USSR missions, with emphasis on how the surface and sub-surface vary from one location to another.  Over the course of the mission, it is likely that Moon Zoo will also begin a survey of this area using LRO data, so that participants can help build up a more comprehensive understanding of the site.

Alongside analysing the concentration of elements such as titanium, aluminium, iron, potassium and sodium in the surface materials, there will also be a focus on non-geology related science experiments. On the static landing module one investigation will use an extreme UV camera to monitor the structure and dynamics of Earth’s plamasphere, a region of dense, cold, highly ionized particles surrounding the Earth. A near-UV telescope will also observe stars and galaxies outside the Earth’s opaque (at these wavelengths) atmosphere. As the Moon provides a platform for long, uninterrupted observations, this experiment might inform us whether it can be used for future imaging any more effectively than a telescope in orbital space.

So, these are the data the current instruments are after, but what else do the next few years have in store? Well, in late 2014 NASA’s Orion spacecraft is heading for a flight test to orbit around the Earth with a manned crew. This will be the farthest distance from Earth astronauts have flown since the Apollo 17 mission in 1972. Also due to launch in the next few years are the Chandrayaan-2 orbiter and lander, which will mark India’s second lunar exploration mission and first touchdown. However, originally scheduled for 2015, it was revealed last week to be postponed until 2016-2017.

Timeline of Moon missions. Lunar and Planetary Institute

Timeline of Moon missions.
Lunar and Planetary Institute

In fact, it’s probable that the next set of missions to the Moon will not be coming from nation states, but from private companies competing for the Google Lunar XPRIZE (GLXP). The competition, announced in 2007, has opened up a global space race with a first prize award of $20 million. The second craft to reach the Moon will receive a prize of $5 million, and an extra $5 million is on offer in bonuses to any team that achieves certain goals first. The aim of the GLXP is to inspire and encourage humanity to accomplish significant exploration feats and, in doing so, demonstrate that we are within an age where space exploration is not a monopoly of national governments.

With teams in the running from all over the globe it’s hard to tell where the fourth lunar rover will originate from. However, with a closing date of December 31st 2015 and 18 entrants still in the running, I think it is safe to expect a number of exciting new lunar launches in the next couple of years!

Google Lunar XPRIZE contenstants. GLXP

Google Lunar XPRIZE contestants.
GLXP

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Why Study The Moon?

The Moon is seriously old and our objective here at Moon Zoo is to study its surface with the time and detail that cannot be afforded by a small team of scientists. Earth’s closest neighbour and only natural satellite represents the largest and brightest object in the sky second to the Sun, and plays a crucial physical role to life here on Earth. But as the only other body to have been walked upon by man, what do we actually know about the Moon? Why is it so important to continue to study? And what can Moon Zoo’s 47,000 participants do to help?

One of the main focusses at Moon Zoo is to examine the distribution of craters in various regions in terms of their size and frequency. By determining this relationship, we can then independently estimate surface ages of different areas that have been examined by the community. Discerning these surface ages is key to understanding the history of the early Solar System, as the Moon is thought to have formed just 30-50 million years after its birth 4.5 billion years ago. The colossal impact of the Mars-sized body, Theia, into the early Earth stripped the outermost layer of our planet and returned it to a molten state, whilst the ejecta was captured in orbit and accreted to form the Moon. Although not perfect, this hypothesis explains the relatively small size of the Moon’s core and overall lower density, since it coalesced mostly from lighter crustal material. Whilst the Moon’s internal structure is differentiated into a core, mantle and crust in a similar way to the Earth’s, its rapid cooling saw tectonic and volcanic activity cease around 3.5 billion years ago. The combination of the Moon’s inactive geology and highly tenuous atmosphere has enabled its surface to become one of the most ancient, and well-preserved in the Solar System. From this almost perfectly kept record, we are not only able to look into the Moon’s past, but also unravel some of the mysteries surrounding the Earth’s history, the early Sun, and previous Solar System environments.

theia

On Earth, tectonic, volcanic, and weathering activity has destroyed much of its early record, which is why we look to the Moon to understand our own history. We use observations of different surface features and lunar samples to date particular regions, which can help indicate events that occurred in the inner Solar System. This can, for example, be used to date periods of heavy bombardment from asteroids, which enables us to investigate the frequency of such events in Earth’s history. The samples obtained from the Moon originate from its outermost layer, known as the regolith, which is comprised of fine dust formed by impact processes. The Moon’s practically vacuous atmosphere does little to interfere with this layer, so it is able to preserve the impact fragments and provide insight into the composition and origin of colliding bodies. The regolith also incorporates particles from the solar wind, which allows us to examine how the Sun has changed over its lifetime.

Moon Zoo is helping to uncover the lunar geological evolution by analysing high-resolution images from NASA’s Lunar Reconnaissance Orbiter (LRO), in order to expand our knowledge of past impact, tectonic and volcanic activity on the Moon. Alongside crater counting, the other current primary focus is the identification of boulders, which can indicate depth of unconsolidated material. Another outcome of this search will be the production of boulder-density maps, which can be used for future lunar missions to indicate safe (or very hazardous!) landing sites.

Boulder Tracks

To date, the Moon Zoo community has already made over 3.5 million visual classifications with images from LRO, alongside many unusual geological features; the project has also been very successful in identifying spacecraft debris, rover tracks and even astronaut footprints! Data already gathered from the work of Moon Zoo participants on the Apollo 17 landing site are well on their way to producing the first comprehensive paper showing these results, which is scheduled for submission shortly. The current Apollo 12 landing site has also received a great deal of interest and will soon be drawing to a close for full analyses by the Moon Zoo science team. The next step will be analysing an entirely new data set, which will most likely delve into the rare maria on the more mysterious dark side of the Moon.

moon1

There is still a great deal more to understand about our planet’s closest relative and the clues it holds to the history of the bodies around it. Until the next stage of the project is launched, we implore you to keep clicking and help demonstrate the strong impact of citizen science on lunar research!

Apollo 12 – a new challenge is set!

Dear MoonZoo aficionados,

Our next surveying exercise will be centred on the Apollo 12 landing site.

Your previous and successful endeavour saw hundreds of thousands of craters and interesting features noted in the region of the Apollo 17 landing site in the Taurus-Littrow valley. Here we witnessed a chaotic and highly scarred terrain, squeezed between tall mountains and crossed by a deep fault (the Lee-Lincoln Scarp): a rather complex geological setting. Indeed, the landing site was selected based on its geological diversity, with the aim of collecting pre-Imbrian age highland material, mare basalts, and igneous products from potential volcanic edifices.

Apollo 12 – Commander Pete Conrad is working at the equipment bay of Lunar Module ‘Intrepid’ on the Ocean of Storms (©NASA)

Apollo 12 – Commander Pete Conrad is working at the equipment bay of Lunar Module ‘Intrepid’ on the Ocean of Storms (mission patch and image ©NASA)

Now we are turning our attention to the Apollo 12 landing site, and from 9 May all the Moon Zoo images relate to this site. In November 1969 Apollo 12 landed within a vast lunar mare (lava plains) region called Oceanus Procellarum (Ocean of Storms), and in particular an area baptised as Mare Cognitum (Known Sea), so called given that it had already been visited by three unmanned lunar missions (Luna 5, USSR, Surveyor 3 and Ranger 7, US). The landing region was estimated to be younger than the Apollo 11 site based on kilometre-size craters census (2.37 times fewer craters). In the following years, returned sample analyses (i.e. Stöffler and Ryder, 2001; Barra et al., 2006) estimated ages of 3.58 ± 0.01 and 3.80 ± 0.02 Gyr (both Late Imbrian Epoch), for Apollo 11, against 3.15 ± 0.04 Gyr for Apollo 12, (Eratosthenian Period). It will be very interesting to compare these direct age estimates with your high resolution/volume crater count survey, AND also compare them with the results from the Apollo 17 blitz (samples’ age: 3.75 ± 0.01 Gyr).

Obviously, as before we are also going to harvest data generated by the Moon Zoo users regarding bouldernyness and shape of the noted craters in order to build a fuller picture of the impact record in the region. As it happens, the lunar science team based at Birkbeck/UCL, UK, has been looking at the Apollo 12 region for quite sometime, both in terms of geological mapping and analysis of returned samples. We are particularly interested in the different lava flows found in the region and the mapping of small craters; the associated boulder distribution will be employed to estimate the different ages and thickness of these lava flows. Your Moon Zoo measurements of the Apollo 12 site will therefore be greatly appreciated, and they will potentially be incorporated in future scientific publications.

So, let’s start this new and exciting journey together: I will keep you posted on both results from previous efforts (A17, etc.) and the ongoing ones. Go and explore!


Barra F., et al., 2006. 40Ar/39Ar dating of Apollo 12 regolith: Implications for the age of Copernicus and the source of nonmare materials, Geochimica et Cosmochimica Acta 70, 6016-6031.

Stöffler D. and Ryder G. 2001. Stratigraphy and isotope ages of lunar geologic units: chronological standard for the inner solar system. Space Science Reviews 96: 9-54.


Dr. Roberto Bugiolacchi
Moon Zoo science lead
Birkbeck, University of London

Thank you Moon Zoo!

The MoonZoo science team would like to extend a gigantic thank you to all 20,627 users who contributed in counting craters (and more!) relating to the Apollo 17 landing site (Taurus-Littrow)!

Let’s ponder on some astonishing numbers: to date, around 8.5 million craters in total have been marked by MoonZoo citizen scientists, with around 670,000 (~8%) relating to the A17 region (from 21 selected NAC images, Figure 1); further, 3.3% (22,063) of these craters have been classified as containing boulders and 6.9% (45,893) were found to be non-circular.

Figure 1. On the right we see the MoonZoo users crater input. Different colours relate to different NAC images basemaps. On the left we see the A17 landing site (red dot) and the astronauts exploration paths and stations.

Figure 1. On the left we see the MoonZoo users crater input. Different colours relate to different NAC images basemaps. On the right we see the A17 landing site (red dot) and the astronauts exploration paths and stations.

Our next step is to compare your input with the ‘expert’ count looking to validate and quantify your contributions.  The ‘expert’ in question is a professional lunar scientist who has published research including the statistical occurrence of impact craters on planetary surfaces. The logical assumption is that given a more or less constant collision rate of interplanetary bodies (asteroids and comets), a surface will carry the record of impact products (craters and pits) as a function of time, i.e., from the time of resurface (maybe a lava mare flow) the scarring would be proportional to the length of exposure.

As most things in geology, this scenario is true but with caveats… : first, the resurfacing by lava flow or ejecta mantling might have only partially buried ancient craters, or, more probably, only the smaller ones, thus skewing the crater-size statistical record; crater rims erode with time, even on an airless body like the Moon, at a rate of around 0.06-1 cm per million year. This might not seem much, but in the lunar chronology scale, measured in billions of years, this factor becomes significant; in reality, the biggest source of uncertainty is represented by secondary craters: most impacts generate coherent distal ejecta that, when landed, produce smaller craters virtually indistinguishable from space-born ones. And this is fractal, i.e. scaled: big impacts will generate hundreds of smaller craters that will overlap with similar ones from nearby big impacts…

The hard reality is that there are no cast-iron methods to establish the origin of each excavation (although it has been advocated that a secondary crater might be somewhat shallower in comparison to a similarly-sized primary one). So, an ‘expert’ becomes so by developing a ‘sense’ or instinct on what ‘feels’ a statistically significant crater against one that is not. This approach is more akin to ‘artistic interpretation’ than ‘hard’ science, but qualitative investigation of certain geological features is an acceptable compromise when a physical method is either not yet available or even impossible to develop.

These considerations do not stop the development of alternative methodologies though; indeed, we are working closely with a research group at Manchester University which is building an automated pattern recognition software of circular features (and others) based on theoretical models, and actual data: ‘expert’ counts, AND MoonZoo users’ data.

Now, whatever approach brings us closer to a reliable crater counting method this cannot be easily accomplished by even a troupe of crater-counting planetary scientists: the 8.5 million craters noted by the MoonZoo community would have taken years to harvest otherwise!

So, what is going to happen now? Well, the ‘expert’ and pattern recognition software data will be compared with the MoonZoo output, uncertainties and limitations of all approaches established and, hopefully, develop a method that will represent the basis for ‘trusting blind’ the MoonZoo craters stats. In practice this will translate into something like “MoonZoo crater data are consistent with other methods for crater of sizes ‘x’ to ‘y’, in images with resolution higher than ‘z’ meters, and illumination of ‘n’ degrees or higher”.

Ultimately, the crater statistics (Cumulative Crater Frequency) plotted against known crater accumulation functions (i.e. Neukum, 1983, 2010) give us an estimate of the age of the lunar region. Using these data from landing sites allows for comparison with returned samples whose age has been established in the laboratory.

Figure 2. Age estimates based on estimated crater frequency distribution against crater size (diameter)

Figure 2. Age estimates based on estimated crater frequency distribution against crater size (diameter)

Our next journey will focus around the Apollo 12 landing site, in Mare Cognitum. The geology of this region is radically different from the Apollo 17 and it should serve as a perfect complement to our work so far. Elsewhere my colleagues will discuss and introduce the region in more detail, including ulterior scientific reasons behind the choice of this landing site.

We shall keep you informed of all further developments and new projects, and, once again, thanks for your patient and enthusiastic contribution to planetary science!

References:

Michael G.G., Neukum G., Planetary surface dating from crater size-frequency distribution measurements: Partial resurfacing events and statistical age uncertainty, Earth and Planetary Science Letters, 2010, DOI: 10.1016/j.epsl.2009.12.041.

Neukum G., Meteoritenbombardement und Datierung planetarer Oberfl�chen. Habilitation Dissertation for Faculty Membership, Univ. of Munich, 186pp, 1983.

Dr. Roberto Bugiolacchi
Moon Zoo science lead
Birkbeck, University of London
University College London (UCL)

Moon Zoo Science Goals

Here’s a reminder the Moon Zoo science goals- and what our clicks are being used for.

moonzoobanner

 Crater Survey

1. To improve our knowledge of the production of small lunar craters by gathering information about their numbers and dimensions. This can be used to improve lunar maps and coordinates.

2. To calculate the age of different lunar surfaces (e.g., mare, impact melt sheets, highland crust) by comparing the number and sizes of impact craters. The more cratered a region is the older it is. Knowing the age of different surfaces allows us to build up a history of the geological processes on the Moon, in particular its temporal thermal and magmatic history. What we learn about these processes on the Moon we can then apply to other small rocky planetary bodies.

3. Results from Moon Zoo could also assist in the development of automated computer crater counting systems, and to help understand how image viewing geometries influences crater counting studies.

4. To determine variations in lunar regolith thickness by assessing the presence of boulders around crater rims.

5. To identify unique and unusual morphological features that help us to better understand the geological diversity of the Moon. Recording these featured will help to develop a database of interesting morphological features (for example, boulder tracks, fresh white and dark haloed craters, crater chains, elongate craters and pits etc) for the lunar science community to use.

Boulder Wars

To produce a boulder density hazard map to assist in identifying suitable landing sites for future human or robotic lunar missions.

Additionally

  1. To produce peer-reviewed science.
  2. To promote lunar and planetary science through using Moon Zoo as an educational and public outreach tool.
  3. To identify small, highly elliptical craters that may have preserved meteoritic material.
  4. To assess degraded craters according to variations in user measurements and produce maps of crater degradation states.

December 15: Measuring the regolith thickness at the Apollo 17 site

By  Ian Crawford
(Department of Earth and Planetary Sciences, Birkbeck College)

 Estimating the thickness of the unconsolidated lunar regolith is one of the major scientific objectives of Moon Zoo. This is because understanding the thickness of the regolith in different regions of the Moon will address a number of important scientific questions. For example, as regolith thickness increases with time, measuring the regolith thickness in areas which have not been dated by returned samples will help provide additional surface age estimates. Conversely, measuring the regolith thickness on surfaces with well-determined ages (such as the Apollo landing sites) will help us determine the regolith accumulation rate. Improved global regolith thickness maps will also provide important information for future exploration of the Moon, including the quest to identify future lunar resources.

There are three ways in which studies of small craters can be used to estimate regolith thickness. The first is to determine the minimum size of craters which have excavated blocks of bedrock (i.e. boulders) from below the regolith layer (Fig. 1).  If the crater dimensions are known, then an estimate of a maximum depth of excavation can be estimated as about one-tenth of the diameter.

Figure 1. LROC image of a boulder-covered bench crater. The crater has formed in a basaltic regolith close to the Apollo 12 landing site. The impact has punched through the thin regolith cover and into the harder rock, excavating large blocks that have covered the surrounding surface. This example is 130m in diameter, so the regolith here must be less than about 13m deep. By determining the maximum size of craters in this area which have not excavated boulders the actual depth of the local regolith can be determined. (LROC image M114104917L/ASU/NASA).

The second method relies on identifying flat floors or benches within a crater, which also indicates that a crater has penetrated an overlying regolith layer to a stronger layer beneath. Figure 1 again provides an example. For features like this a simple expression has been derived which estimates the regolith thickness from the ratio of the bench diameter to the overall crater diameter. For the example shown in Figure 1 this indicates a regolith depth of about 6 m, consistent with the upper-limit of 13m estimated from the presence of boulders around the rim.

The third method is more subtle, and exploits the process of impact gardening, whereby rocky surfaces are disaggregated and overturned by meteorite impacts, thus destroying the record of previous impact cratering events. The equilibrium diameter is identified when the cumulative number of craters seen on the surface is less than the number actually produced, and can be recognized as a change in slope in a graph which plots number of craters in a given area as a function of their size. Because the number of craters buried under new regolith depends on the regolith thickness, measuring the equilibrium diameter gives a guide to the latter.

In order to test these different methods it is necessary to apply them to areas where the regolith thickness has been directly measured. However, this can only be done at the small number of Apollo landing sites where seismic measurements of regolith thickness were conducted. By far the best estimates have been provided by the Apollo 17 Lunar Seismic Profiling Experiment (LSPE). For this experiment the astronauts deployed eight small explosive packages during their traverses around the Taurus-Littrow Valley (Fig. 2) which, when detonated, provided seismic signals for detectors setup close to the Lunar Module.

Figure. 2. One of eight explosive packages deployed by the Apollo 17 astronauts to provide data for the lunar seismic profiling experiment which measured the thickness of regolith in the Taurus-Littrow Valley. The Apollo 17 LRV is in the foreground and the lunar module, where a geophone detector array was deployed to collect the signals, in the middle distance about 300 m away (NASA)

By measuring the time taken for the seismic signals to travel from the explosive packages to the detector, geophysicists were able to determine the thickness of both the regolith layer and the underlying lava flows at the Apollo 17 landing site. The results are shown in Fig. 3.

Figure. 3. Subsurface structure under the Taurus-Littrow Valley, as determined by the Apollo 17 seismic profiling experiment. The numbers indicate seismic wave speed in meters per second. Yellow represents the lunar crust, which outcrops locally as the South Massif (“LM impact” schematically indicates where the Apollo 17 Lunar Module ascent stage was crashed into the South Massif to provide an additional seismic data point). The green layers indicate the thickness of basaltic lava that has flooded the valley to a depth of about 1.4 km. The thick black line shows the regolith layers (inset). (Image adapted from a paper by M.R. Cooper et al., published in Reviews of Geophysics and Space Physics, Vol. 12, pp. 291 – 308, 1974).

Five separate layers were identified below the surface of the Taurus-Littrow valley:

(i)  The topmost layer, 4 m deep with the very low seismic wave speed of 100 m/s, is interpreted as being due to the local regolith.

(ii)  Beneath the regolith is a layer with a velocity of 327 m/s, which is still too low for solid rock. It may be due to more consolidated regolith, or possible highly fractured lava.

(iii)  At a depth of 32 m the velocity rises to 495 m/s, and this is interpreted to be the fractured and/or vesicular top of the lava flow filling the valley.

(iv)  At a depth of 390 m the velocity rises to 960 m/s. This is interpreted as being due to a more coherent basalt unit.

(v)  Finally, at a depth of 1.4 km the velocity rises sharply to 4.7 km/s, and this is interpreted as being due to crustal bedrock underlying the lava layers.

The deeper layers are too deep to be probed by craters found in the MoonZoo images, although the presence of a lava layer at a depth of about 30m is consistent with the excavation of basaltic blocks from 300-400 m diameter craters in the valley floor. Where MoonZoo can really help is to confirm that the seismic boundary at a depth of 4m (which will be probed by craters about 40 m across), and to determine whether the underlying layer is more consistent with fractured basalt or compact regolith.

In order to address these issues, we need MoonZoo users to look carefully at craters in the images of the Apollo 17 area, determine their sizes accurately, and note the presence of boulders around the rims and/or interior benches or flat floors. Don’t worry that scales are not provided on the MoonZoo images (this is deliberate to avoid the possibility of biasing the results), but users may be sure that the sizes and morphologies of all thecraters in these images are relevant to the task in hand.

 Ian Crawford is based in the Department of Earth and Planetary Sciences, Birkbeck College, London, and is a member of the MoonZoo science team. This blog article is based on a longer article published in the December 2012 issue of the Royal Astronomical Society journal Astronomy and Geophysics.

 

The Scientific Legacy of Apollo

By  Ian Crawford
(Department of Earth and Planetary Sciences,
Birkbeck College, London)

Fig. 1. One of the last two men on the Moon: Harrison Schmitt stands next to a large boulder at the Apollo 17 landing site in December 1972. (NASA).

This December marks 40 years since the last human beings to set foot on the Moon, Gene Cernan and Harrison “Jack” Schmitt of Apollo 17, left the lunar surface and returned safely to Earth. In the three and a half years between Neil Armstrong’s ‘first small step’ in July 1969 and the departure of Cernan and Schmitt from the Taurus-Littrow Valley in December 1972, a total of twelve astronauts explored the lunar surface in the immediate vicinity of six Apollo landing sites.

Fig 2. The Apollo landing sites. Note their restriction to the central part of the nearside – there is a lot more of the Moon to explore! (Image: NASA).

The total cumulative time spent on the lunar surface was 12.5 days, with just 3.4 days spent performing extravehicular activities (EVAs) outside the lunar modules. Yet during this all-too-brief a time samples were collected, measurements made, and instruments deployed which have revolutionised lunar and planetary science and which continue to have a major scientific impact today.

Fig. 3. A view across the Apollo 17 landing site in the Taurus-Littrow Valley. The Apollo 17 Lunar Roving Vehicle is in the foreground, and the Lunar Module is in the middle distance about 300 m away. The black box in the foreground is one of eight explosive packages deployed to provide data for the lunar seismic profiling experiment which measured the thickness of regolith and the underlying lava in the Taurus-Littrow Valley (NASA).

In their cumulative 12.5 days on the lunar surface, the twelve Apollo moonwalkers traversed a total distance of 95.5 km from their landing sites (heavily weighted to the last three missions that were equipped with the Lunar Roving Vehicle), collected and returned to Earth 382 kg of rock and soil samples, drilled three geological sample cores to depths greater than 2 m, obtained over 6000 surface images, and deployed over 2100 kg of scientific equipment.

Fig 4. Jim Irwin next to the Apollo 15 LRV with the 4.6 km high Mt Hadley in the background; note the sample bags attached to the rear of the LRV (NASA).

These surface experiments were supplemented by wide-ranging remote-sensing observations conducted from the orbiting Command/Service Modules.

Fig. 5.The Scientific Instrumentation Module (SIM) bay of the Apollo 15 Command/Service Module (CSM). On Apollo 15 the SIM included mapping cameras, a laser altimeter, and ultraviolet, X-ray and gamma-ray spectrometers (NASA).

 Probably the greatest scientific legacy of Apollo has resulted from analysis of the 382 kg of rock and soil samples returned to Earth. One of the key results has been the calibration of the lunar cratering rate. Only by comparing the density of impact craters on surfaces whose ages have been obtained independently by laboratory analyses of returned samples is it possible to determine the rate at which meteorite impacts have created craters on a planetary surface. Analysis of the Apollo samples (supplemented by those obtained by the Soviet Union’s three Luna robotic sample missions) has enabled this to be done for the Moon, which remains the only planetary body for which such a data-set exists. Not only has this facilitated the dating of lunar surfaces from which samples have yet to be obtained, but it is used, with various assumptions, to estimate the ages of cratered surfaces throughout the Solar System from Mercury to the moons of the outer planets.

Another important result of Apollo sample analysis by seo services uk has been the evidence provided for the origin of the Moon. In particular, the discovery that lunar materials have compositions broadly similar to those of Earth’s mantle, but that the Moon is highly depleted in volatiles compared to the Earth and has only a small iron core, led to the current view that the Moon formed from debris resulting from a giant impact of a Mars-sized planetesimal with the early Earth. It is very doubtful that we would have sufficient geochemical evidence usefully to constrain theories of lunar origins without the quantity and diversity of samples provided by Apollo, and indeed these samples are still being actively exploited for this purpose.

Fig. 6. The current theory of the Moon’s formation from debris produced by a giant impact on the early Earth is largely based on the geochemical analysis of samples collected by the Apollo missions (image: Wikipedia Commons).

Beyond this, the Apollo samples have been vital to our understanding of the Moon’s own geological history and evolution. While lunar geology may at first sight appear to be a relatively parochial area of planetary science, it is important to realise that the Moon’s surface and interior retain records of planetary processes which will have occurred in the early histories of all the terrestrial planets, such as the formation of cores and crusts. In all these respects the Moon acts as a keystone for understanding the geological evolution of all the rocky planets.

Fig. 7. Fragments of Apollo 12 soil sample 12023 at the Lunar Sample Laboratory at the Johnson Space Center, being selected for a study of lunar volcanism in 2009. Forty years after they were collected, Apollo samples like these are still being used for scientific investigations (photo: I.A. Crawford)

In addition, Apollo samples of the lunar regolith have demonstrated the importance of the lunar surface layers as an archive of material which has impacted the Moon throughout its history. These include records of solar wind and cosmic ray particles, and meteoritic fragments. Extracting meteoritic records from lunar regolith samples is especially important for planetary science as it potentially provides a means of determining how the flux and composition of asteroidal material in the inner Solar System has evolved with time.

Last, but not least, the Apollo samples have been used to calibrate remote sensing investigations of the lunar surface. The visible, infrared, X-ray and gamma-ray spectral mapping instruments carried by a host of recent orbital missions to the Moon have produced a wealth of information regarding the chemical and mineralogical nature of the lunar surface. Although these orbital missions post-date Apollo, the reliability of their results largely depends on their calibration against known compositions at the Apollo landing sites. Without the ‘ground truth’ provided by the Apollo samples, it would be difficult to have as much confidence in the results of these remote sensing measurements as we do.

 In addition to study of the Apollo samples, many other areas of scientific investigation were also performed by the Apollo missions, especially geophysical investigations of the Moon’s interior. Key results included the discovery of natural moonquakes and using them to probe the structure of the crust and mantle, geophysical constraints on the existence and physical state of the lunar core, and measurements of the flow of heat from the Moon’s interior. Although these data are over thirty years old, advances in interpretation means that they continue to give new insights into the interior structure of the Moon. For example, only last year an apparently definitive seismic detection of the Moon’s core, and strong evidence that, like the Earth’s, it consists of solid inner and liquid outer layers, was made by a re-examination of Apollo seismic data.

Fig. 8. Apollo 14 seismometer deployed on the lunar surface; the silvery skirt provided thermal stability. These instruments, also deployed at the Apollo 12, 15 and 16 landing sites, constituted the Apollo passive seismic network which remained active until 1978 and yielded valuable data about the interior of the Moon (NASA).

Looking over the totality of the Apollo legacy, I think one could reasonably make the case that Apollo laid the foundations for modern planetary science, certainly as it relates to the origin and evolution of the terrestrial planets. Arguably, the calibration of the lunar cratering rate, and its subsequent extrapolation to estimating surface ages throughout the Solar System, could alone justify this assertion. If one also considers the improvements to our knowledge of lunar origins and evolution, and the records of solar wind, cosmic rays and meteoritic debris extracted from lunar soils, it is clear that our knowledge of the Solar System would be greatly impoverished had the Apollo missions not taken place.

 However, it is also clear that Apollo did little more than scratch the surface, both literally and figuratively, of the lunar geological record. With only six landing sites, all at low latitudes on the nearside, it is clear that much remains to be explored. Therefore, as we pass the 40th anniversary of the last human expedition to the Moon, there are good scientific reasons to start planning for a return.

Fig. 9. Artist’s concept of astronauts supervising a drill on the Moon. Returning humans to the lunar surface later in the 21st Century would facilitate larger scale exploration activities than was possible with Apollo, and will further increase our knowledge of lunar and Solar System evolution (artwork: NASA).


Ian Crawford is based in the Department of Earth and Planetary Sciences, Birkbeck College, London, and is a member of the MoonZoo science team. This blog article is based on a longer article published in the December 2012 issue of the Royal Astronomical Society journal Astronomy and Geophysics.

Computers counting craters

By Anthony Milbourne (Birkbeck College)

In this blog I would like to talk about automated crater counting, which as the name suggests, is the use of computers to identify and count craters.  Computers are very good (and getting better) at many things, but they are still not as good as humans at many important classes of problems.  The human brain is amazingly powerful (you knew that) and the latest generation of super-computers have only just reached the same order of magnitude of computing power.  In other words, there are currently only a handful of machines in the world that can truly rival the human brain in terms of raw processing-power.  Humans are particularly good at pattern recognition and computer scientists have been trying to create computer programs to do this for a long time, but the results generally look rather pathetic compared to humans.  One application of pattern recognition is crater identification, which I will talk about below.

In general there are two approaches to pattern recognition: designed algorithms and machine learning.

A designed algorithm is a set of very specific instructions that allow the computer to solve a specific problem.  It would be like programming a car to drive from point A to point B by defining exactly how much to accelerate or brake at what points and exactly how much to turn the wheel when.  If you know the car will always be running on the same road then this approach is reliable and predictable, but it’s not very flexible.  If a pedestrian steps into the road they are in trouble because the car will take no account of the changed environment, and if the car has to go on a different road it will be close to useless.

In machine learning the computer is still given detailed instructions, not on how to solve a specific task, but on how to learn what works well to solve the task.  This would be like giving the car-driving program instructions on what the brake, accelerator and steering wheel did and then letting it experiment until it found a route from A to B.  Obviously, this requires a training phase, where the algorithm crashes a lot (let’s hope the car wasn’t expensive), but eventually it figures out some general principles about driving and is able to deal with a certain amount of change in its environment.

Designed algorithms are safe and predictable; they don’t need training and are often easier to implement and faster to run, but they are inflexible.  Machine learning may be better at the job and will certainly be more flexible, but it is tricky to train and you may end up training it to take short-cuts that you didn’t want: http://neil.fraser.name/writing/tank/

CHT circles

CHT circles: The points (in red) on the circle (in black) each create a ring of votes (in blue) around themselves. Where vote rings overlap the votes combine (more intense blue) and the greatest vote value indicates the centre of the circle. The radius of the vote rings is determined by the radius of the circle being searched for.

An example of a commonly used designed algorithm is the Circular Hough (pronounced like rough) Transform (CHT).  We assume that the image is taken from above and that the vast majority of craters will be roughly circular.  The program then uses a CHT to look for circular patterns of a set radius (perhaps repeating many times for different radii).  A CHT essentially takes each image pixel that represents an edge and uses it to generate a vote for all possible circles that the edge could be on.  The centres of all these possible image circles form a circle of votes around the edge point.  If you do this for every edge point then the votes tend to build up at points that really are the centres of circles (it’s easier to see in the diagram).

Of course, this is much harder in practice as the images have a lot of noise and other features that generally confuse the algorithm, so various people have come up with various ways of improving it and making it faster.  There are of course many other types of designed algorithm, but I won’t bore you with all of them.

An example of a machine learning approach is to use a neural network.  This is a program that tries to simulate a simplified model of how the brain works.  It consists of a large number of ‘nodes’ which are connected to each other by links of varying strength.  The nodes are normally arranged in layers, and each node combines the inputs from nodes in the preceding layer and sends the result out to the nodes in the next layer.  The nodes in the first layer act as input points and the nodes in the last layer as the output.  The strength of the links can be varied in order to change the behaviour of the network, and this is done during training when the error from each training run is used to adjust the link strengths.

Simplified neural network

This is a very simple (too simple to be useful) example of a neural network. A set of values are passed to the input layer (in green) and an output is generated by the output layer (in purple). What happens in between is determined by the connection strengths, which are the result of training.

Neural networks are deceptively simple in concept but are very powerful and can end up spotting trends that are not clear to humans, or that are too complex or nuanced to implement easily as a designed algorithm.  However the number of nodes needed to achieve anything useful is normally large, so figuring out what is actually happening inside the network is not practical.  For this reason you can never be quite sure that the network, given new or unexpected data, won’t do something crazy!

Again, this is not the only way of implementing machine learning, but it gives an idea of the way this sort of system works – trained rather than designed.

In general, machine learning approaches take more processing power than designed algorithms, so in most cases a pipeline is used.  First, a quicker, more predictable, designed algorithm is used to select areas of interest (potential craters), and then a machine learning approach is used to sort the real craters from the noise.

Hough Transform, on an HRSC image of Mars

Hough Transform, on an LROC image of the Moon (TOP) The result of running an algorithm, based on the Hough Transform, on an HRSC image of Mars. The smooth terrain and crisp crater rims produce fairly good results, although there are still a few errors, some of which are glaringly obvious to a human. (Image: modified from HRSC/ESA). (BOTTOM) The result of running the same algorithm, on a tile from the Moon Zoo database. The degraded rims and noisy background confuse the algorithm which finds lots of craters, but almost all are in the wrong place! (Image: modified from NASA/GSFC/ASU)

The images at left are an example of the kind of output that can be achieved by automated crater recognition (this one is based on Hough Transforms), and the problems with it.  This is far from the best algorithm available, and other researchers have developed much more accurate programs, but they all suffer to a greater or lesser degree from image noise.  The first image shows how accurate an algorithm can be in a clear image with little noise.  It misses many smaller craters and there are a few false positives (which are somewhat surprising to a human eye), but in general it finds the rims of the most obvious craters very accurately.  The second image features degraded crater rims, a lumpy surface and sub-optimal illumination.  The result is that the same algorithm does very badly at spotting craters.  This is not surprising; even a human would have to look harder at the second image, but the algorithm performs so badly that it is arguably not worth using, and this is the sort of image where humans really are the only (reliable) show in town at the moment.You might think that automated crater counting would be a direct competitor to crowd sourcing efforts like Moon Zoo, and in some cases you would be correct, but it can also be used as a complementary technique.  This could be done by using moon Zoo crater identifications as the areas of interest and then running an algorithm to find the exact location of the crater rim, or using an algorithm to spot Moon Zoo data which has been entered by mistake, or by users who didn’t understand the task.

Most interestingly, in my view, is the idea that algorithms are just another type of user.  Some algorithms are not great at spotting craters, but some human users are a bit variable too.  Admittedly, the best humans are far better than the best algorithms, but the best algorithms are probably better than the worst humans, so they fall within the quality spectrum that Moon Zoo (and the rest of the Zooniverse) already deals with.  They probably won’t be much good at spotting unusual objects and they certainly won’t be much fun on the forums, but perhaps we might one day be working with algorithms as our (less able) peers.

By Anthony Milbourne (Birkbeck College)

New Look for Moon Zoo

Moon Zoo launched more than 18 months ago and we’ve been meaning to make some changes to the site. Later today our refreshed site will go live! You’ll notice that we’ve had the decorators in – the website looks a little different and a new tutorial (see below). On the back end, we’ve added new images and retired some old ones.

Tutorial

We have created a new interactive tutorial for Moon Zoo. This tutorial guides you through the basic interface of Moon Zoo and teaches you how to avoid some of the common pitfalls that we’ve seen since the project began. Even if you’ve classified on Moon Zoo before you might need to take the tutorial just once to get back on track – you should find it’s no problem. The tutorial has lists of known craters against which your markings are compared. If you’re too far off the mark with your crater drawing, we’ll ask you to try again. There’s nothing to worry about, just our way of ensuring maximum results from the site by bringing everybody up to speed.

Images

We are retiring the Moon images we used at launch in 2010. But we have some great, new ones that allow us to study secondary craters and volcanic regions of the Moon. These will help us study interesting features and crater types, building on the work that has already been done by Moon Zoo volunteers.

So Moon Zoo: Phase 2 has begun – and will go live today – take a look!

Moon Zoo Science Conference Abstract Now Available

Full Moon - Credit, Stuart Robbins

Hello Zooites! This is a quick post to let you know that I’m headed to the Planetary Crater Consortium conference in Flagstaff, AZ, in about three weeks and I’ll be presenting a paper there about Moon Zoo and what we’ve learned so far. The abstract is two pages and it’s not too technical. If you’re interested in reading it, you can download the ~140 KB PDF here!