# Looking for students

Hey UW students:

Are you looking for an undergrad research project, either for capstone credit (for UW Tacoma Environmental Science or Studies students) or for experience? We’re looking for new lab members! Here are a few ways you can get involved:

1. We’re finishing up some work using magnetic properties to look at sediment transport in mud from the Bengal Fan. We need someone who’s interested in doing some electron microscopy, and someone else who wants to hone their lab skills by separating sediment into size fractions (sand, silt, and clay) and analyzing magnetic properties. Both projects involve fun with big magnets, getting muddy in the lab, and going to the Geological Society of America conference in October.  (The image from this post is a SEM element map from former student Aaron Burr’s capstone project; red is iron, blue is calcium, and green is silicon.)
2. Anybody interested in using magnetism to answer local environmental questions? Starting in late August, I’ll be looking for some students to determine magnetite content in some soil cores for a groundwater hydrology study.
3. We’re also hoping to start some experimental projects and fieldwork this year aimed at learning how transport through natural (river and dry grassland) and built environments might change the size distribution of magnetic particles in sediment. These projects are going to have some outreach and citizen-science components.

Contact me for further details: paselkin at uw dot edu.

# A New Way to Look at Changes in Earth’s Magnetic Field Intensity?

I noticed this article in EOS recently (thanks to Jon Mound and Nick Swanson-Hysell on Twitter for the heads up), and thought I’d comment. Although I’m framing these as caveats, please don’t take the comments to be an attack on anyone, either the article’s author or the authors of the study it describes. I’m just trying to outline my thought process and the kinds of questions a paleomagnetist like me might have when I look at coverage of something in the popular press (which EOS is, sort of…).

Caveat 1: Be careful of the source. My first impulse when I see an article on paleomagnetism in EOS or another responsible publication is to look for the original study. As someone who works in this field, I want to know the details: how was the data analysis done? What dataset was used, exactly? How does it fit into the context of work that’s come before? (I can usually figure this out by myself, but maybe I’ve missed something.) Were there any checks to make sure the result is plausible versus being an illusion of how the data were processed or a bias in the dataset? The problem here is that the results reported in EOS were described in a talk, not a peer-reviewed paper. The talk was by Kirschner and co-authors at the European Geosciences Union (EGU) conference a few months ago. EGU is kind of analogous to the American Geophysical Union conference here. The abstract from the talk, which is all I have to go on, is here. There’s actually a lot in it that didn’t make it into the EOS article, but not the details I’m looking for. (Also, there were some other neat talks in that session at EGU that I wish I’d heard!)

This isn’t to say that science journalists should never write about talks – of course they should. Conferences are where we share current work in progress. But as a reader, when you see that a story is based on a talk, know that there are some questions about the work that might not be answered – or answerable – yet.

Caveat 2: The Data.  Estimates of Earth’s magnetic field intensity are much harder to deal with than standard paleomagnetic data. This is in part because the intensity of a rock’s magnetization has a complicated relationship with the  intensity of the magnetic field in which the rock was magnetized. You can, for example, collect samples from two basalts that cooled at the same time, and so were magnetized in the same field. The magnetizations that you measure from your two basalt samples might be vastly different for a number of reasons. For one thing, one basalt might have more magnetite in it than the other. (Titanium-bearing) magnetite is the mineral that is mostly responsible for the magnetic record in basalt. Alternatively, differently sized or shaped or aligned titano-magnetite particles may have led one of the basalts to record the magnetic field more efficiently than the other. Alternatively, one of the basalts may have had its magnetic record wiped clean (by being reheated, for example, or chemically changed), or may have been remagnetized by a lightning strike, or may just have lost part of its magnetic record by sitting around in changing magnetic fields for a long time (we call that “viscous decay”). Over the years, various techniques have been developed to screen for these effects, and in some cases to adjust for them. But techniques do matter, and sometimes applying the wrong techniques or not applying the proper adjustments may bias estimates of ancient magnetic fields.

This is relevant because in the EOS article, the study is described as using “all available data”. The PINT database contains published results from hundreds of studies that attempt to estimate Earth’s ancient magnetic field intensity. This includes some from studies in the 1950s that don’t check for many of the problems that we know exist, some results that use different kinds of screening techniques, different ways to estimate amounts of magnetic material or the efficiency of magnetization, and different corrections for the weird effects I described. So it’s sometimes difficult to compare data from one study – one data point in the PINT database – to another. The usual approach to comparing paleointensity estimates through time is to come up with a set of criteria (based on the type of intensity estimate, or on the number of checks for a sample’s “ideal” behavior, or on agreement between different types of estimates) and look at data that meet those criteria. In fact, that appears to be what the authors of the study in question did in addition to the analysis of the whole dataset (From the abstract: “Spectral analysis of all palaeointensity data and a quality-filtered dataset obtained from the palaeointensity database…”).

Now, even if you decided to compare only the same kind of estimates of magnetic field intensity through time, there would still be some issues with the data. For example, different rock types may require different checks or adjustments, or may respond differently to the same estimation technique… and those rocks may be more or less common through different parts of the geological time scale. Intensity estimates from one particularly time-constrained rock type, basaltic glass, are really only available for the past 180 million years. Basaltic glass records magnetic fields differently from basalt (mainly because the magnetic particles are much smaller, and glass cooled more rapidly than basalt), and different adjustments for cooling rate (if you agree with them, which not everyone does) may need to be applied. Geology matters!

Caveat 3: The analysis. OK, so even if the data are filtered so that they are all reliable and comparable in terms of technique, estimates of past magnetic field intensity are unevenly spaced in time. Not all rocks are appropriate to use in intensity experiments. The older the rocks are, the fewer usable ones there are that have withstood the ravages of weathering, metamorphism, reheating, lightning strikes, and tectonism. This makes for a dataset that unevenly samples the magnetic field through time. One of the assumptions of digital signal processing- which the authors of the study in question do – is that the data are (at least close to) evenly spaced in time. There are ways to get around this requirement, say by fitting a smooth curve to the data and re-sampling it – but those require some assumptions about the data and the underlying process. Because this study was reported in a talk, I’m not sure what those assumptions were. Nonetheless, any smoothing, fitting, or filtering done to the data could strongly influence the cyclic behavior that the authors describe. I’m not trying to imply that the study’s authors are signal processing newbies – they may very well know more than I do about it. I’m just highlighting a question that I’d ask if I were trying to evaluate the study.

By the way, more than the signal processing aspects, I’m interested in the authors’ work on the change in the distribution of intensity estimates through time. This isn’t mentioned in the EOS article, but is in the abstract – I’m not sure why. Here’s my summary: When we talk about a “distribution”, we’re imagining that the intensity estimates are like students’ grades in a class: a random bunch of numbers to an outsider. If you take all of those grades together, they fall within some range of a middle value, with some grades are more frequent than others. If you taught the class again the same way, but with a different set of students, the pattern of grades would probably look somewhat similar even though the specifics would be different. In statistical terms, the underlying pattern of numbers (grades or intensity estimates) is a probability distribution. The authors, in their abstract, note that they see a change in the probability distribution of intensity estimates around 1.3 billion years ago, ” coincident with the time that geologic and palaeogeographic evidence suggests the onset of quasiperiodic assembly and fragmentation of supercontinents.” It’s also within the time frame that recent work (if you believe it) has suggested that the inner core began to grow. So is the cause of the change external (tectonic-related), internal (inner-core-driven), or neither? I’m not sure, but it adds another intriguing possibility into the mix! (I guess that’s Caveat 4: The Interpretation.) The approach that the study’s authors take to look at changes in probability distributions isn’t described in the abstract, but it might work better than the signal processing approach with data that are unevenly scattered through time.

Caveat 5: Is it a New Result? The idea that tectonics influence Earth’s magnetic field has been popular at least since the late 1990s, when geodynamo simulations suggested that different patterns of heat flux at the core-mantle boundary – due to patterns of cold, subducting lithospheric slabs – could influence magnetic reversals (see Glatzmaier et al., Nature, 1999). Since then, there have been a number of studies looking for tectonic-related cycles in intensity data (see here and here for example; both studies’ authors are quoted in the EOS article). So the research problem isn’t new, but I’m not sure that anyone has succeeded at the signal processing approach.

I’m sure other people have other ideas or concerns about the study or the way it was reported in EOS. The things I’d like to know about the Kirschner et al. study represent my own perspective as someone who used to do this stuff. Maybe you have a different set of questions? Please comment below!

# Geology as Quilts

Sometimes you make the darndest connections on Twitter. Like a few weeks ago, when Nadine Gabriel tweeted this:

Here is a tweet from a geologist halfway around the world about an art exhibit less than an hour from me. That’s a fun connection. But also: how often do you get to see a geology-themed art exhibit? I had to go.

I had the chance to go to that exhibit (The Contact: Quilts of the Sierra Nevada by Ann Johnston) today, the day before it closed. The Bellevue Arts Museum was mostly empty, and I went alone, so I got to take my time and look closely at the fabric art, which spanned the entire third floor of the museum. The exhibit benefited from close inspection: there’s even more geology in the works on display than I’d originally thought. Plus, downstairs was an exhibit of new works by emerging glass artists that had some interesting petrologic parallels.

What struck me was the degree to which an understanding of the geology informed the artwork. These quilts weren’t simply illustrations of geology: they were a way to deeply understand a landscape, both through analysis and creation. Apparently, Johnston’s family own the rights to a mining claim in the Eastern Sierra Nevada – it’s delineated with a thin thread on this quilted geologic map. I can imagine that , having grown up with this claim in the family, someone who is both an artist and a geographer (as Johnson is) would want to explore it from both perspectives.

A lot of my own work deals with fabric in the geologic sense: the arrangement of mineral crystals in a rock. In this sense, fabric is a three-dimensional thing: something that pervades a rock but may change from one part of an outcrop to another or even across one hand sample. Fabric is also something that, most of the time, you need to look closely at to be able to interpret. I was impressed by the detail and three-dimensionality of the (textile) fabric in this exhibit. In most of the pieces, the stitching added a layer of information beyond the fabric’s dye and reflectivity – in the same way as a rock’s fabric gives a geologist information beyond the rock’s composition.

For more images, click the gallery below.

# Coming Autumn 2017: Earth Materials!

Are you curious about how volcanoes work, what’s inside a mountain belt, and what would happen if the oceans dried up?

Earth Materials (T GEOS 347, SLN 22043) explores the rocks and minerals that make up our planet: how they form, what they mean, where they’re found, and how we analyze them. We will investigate all parts of the rock cycle, through our focus will mostly be on igneous and metamorphic rocks, the processes that make them, and the minerals in them.

Earth Materials is a prerequisite for many graduate programs in geoscience, as well as a required course for a WA professional geologist’s license. It counts as a geoscience lab course (“List G”) for the Geoscience Option in the Environmental Science BS curriculum.

Things you will get to do in Earth Materials:

• 3-D print crystal models
• Examine thin sections – paper-thin slices of rock – in a polarized light microscope
• Make your own thin sections
• Wow your friends by being able to identify hundreds of minerals and rocks
• Use an electron microscope and an x-ray diffractometer
• Walk on Earth’s mantle and ocean crust (field trip!)
• Distinguish between types of asbestos
• Tell a countertop salesperson which slabs are really granite
• Expand your knowledge of geology by connecting it with physics and chemistry

Earth Materials has T GEOS/TESC 117 (Physical Geology), TESC 151/ T CHEM 152 (Chem II), and T MATH 110 (Intro Stats) as prerequisites. Contact me if you are enrolled in Chem II or Stats and want to take the course.

Here is a tentative course schedule:

The class meets Tu/Th 12:50-2:55 in SCI 209, and F 1:30-4:00 for lab. Please register ASAP so that we can make sure that the class fills!

# Grad School 2: I want to go to grad school, so what should I do?

Suppose you've been mulling over graduate school, and you've decided that it's for you. You have a good reason - maybe you like research, or maybe you want to teach, or maybe your plan to save the world (or maybe just a secure career with some hope of advancement) involves having an MS or a PhD - and you are OK with the commitment. Now: how do you actually do it?

# Grad School: A Primer

I’ve had a few students discuss grad school with me lately, so I thought I’d offer my thoughts via the blog and open it up for comments. This is the first of a series of posts where I’m going to try to address some of the concerns that our students might have, specifically when applying to geoscience or oceanography programs. I’m going to start at the root of the problem: do you really want to (or need to) go to grad school? Please leave some comments if I’ve missed anything, or if I’ve got something wrong!

You need to decide whether grad school is right for you. So far none of my students have been lukewarm about grad school plans after graduating: either they want to go, or they don’t. Either is OK with me. I don’t want to see students deciding to go to grad school because it seems like “what you do” after college. If you have a plan, and go in with open eyes about what you want after your grad degree, you’ll be much happier. Unfortunately, many of my students want to go to grad school, but can’t do it right after college. Sometimes that means they never go. I’m going to address that in a separate post, because it’s kind of a big deal.

But figuring out whether grad school is right for you might be tough, particularly if you’re not familiar with what you can do with a geoscience degree. Are you interested in getting out into the field? An undergraduate degree, with field experience, might be OK for field technician jobs, such as those with the USGS. Experience does count, and it is possible to advance toward a career with a combination of a BS (or maybe a BA) and on-the-job experience. Developing some specific technical skills as an undergrad – in the context of your capstone project or in your classes – will help you get the foot in the door as a college graduate.

Are you interested in working as a consultant or at a state or federal agency? An MS, in those kinds of positions, shows that you are able to work independently and to take the lead on projects, making you more employable. You may additionally need a Professional Geologist’s (PG) certification – a subject for a later post. Are you interested in working in or managing a research lab? An MS or PhD is usually required for managerial-level and skilled lab positions (for example, operating an electron microscope or a paleomagnetic lab). MS-level positions are typically higher-paying than BS-level positions.

Do you want to teach? Elementary through high school education requires an education degree after your Bachelors. Several of my students have gone on to K-12 education, and it makes me incredibly happy to see UW Tacoma graduates teaching in the Tacoma Public Schools (particularly in science). Teaching science at the K-12 level requires a science degree and a teaching credential. If you want to teach in a 2-year college, you’ll need at least an MS; 4-year colleges typically require a PhD for tenure-track (more secure) positions, and may require it for non-tenure-track (often more precarious) positions. If you want to teach college, try getting teaching experience as a graduate student. Also be aware that any full-time college faculty job involves more than teaching.

I intended this post to lay out the foundation for a series on grad school. Keep an eye on this space for posts focused on the courses you need to take as an undergrad, how to apply to grad schools (including timelines!), how grad school classes are different from undergraduate classes, and a list of helpful resources. In the meantime, you can answer these questions in the comments below:

• If you’ve been to grad school, what do you wish you knew beforehand?
• If not, what are you most concerned or curious about regarding grad school?

Image: Lock and key, from Arthur Mee and Holland Thompson, eds. The Book of Knowledge (New York, NY: The Grolier Society, 1912). https://etc.usf.edu/clipart/4500/4514/lock_1.htm Honestly, I can’t find a good grad school image, so this metaphor will have to do.

# Undergrad Research Symposium Abstracts: Coming Up!

Presenting at the Undergraduate Research Symposium in Seattle (the “URS”) is a great opportunity to show off your work, and to get useful feedback from a broader range of perspectives than you’d get in the UW Tacoma program alone. It’s a good chance to network, too, if you are interested in a job or grad school in Seattle. The first step in participating in the URS is to write and submit an abstract.

By the time you are ready to present at the URS, you’ll have had to write an abstract in TESC 310, and maybe even in 410 and some other courses, so the idea of an abstract is probably not a new one. But the specifics of URS abstracts need a little bit of explaining. Fortunately, the Undergraduate Research Program has a good website about abstracts, and runs workshops on abstract writing (including one that has been recorded in case they don’t have one at UW Tacoma). Here are a couple of things to keep in mind:

• Abstracts have to be 300 words or less. That’s SHORT!
• Abstracts should be written for a general audience. Don’t assume the audience knows the context you’re talking about: try to focus on the big picture. Also avoid jargon (if you have to use a technical term, such as “magnetic anisotropy”, use it when you describe your methods).
• One nice way to indicate the sentence where you’re reporting results is to use a phrase like “Here we show that…” (you don’t need to use those words exactly).
• We usually talk about an “hourglass” structure to an abstract. If you’re really ambitious, consider your abstract as a story. Science communicator Randy Olson boils it down to the “And/But/Therefore” framework. Could you describe your work in this format?
• The sooner you have your abstract done, the better. The URS staff send back abstracts that are poorly written or not for a general audience. You’d have to rewrite it if you do. I will read your abstract before it’s accepted, too, and if the facts aren’t right or the interpretation isn’t justified, I’ll make you rewrite it. So: better to get that done in the draft stage!

Good luck! And let me know if you have any problems.

# Moving the blog!

I’m moving my blog content to my faculty website for a few reasons. First of all, Science 304 is no longer just my lab. I now share it with Dan Shugar, of the WaterSHED Lab. Second, it will be easier for me to manage the WordPress software if I’m just taking care of one site instead of two. I’m hoping that this will light a fire under me to write those posts I’ve been talking about for months…

# Lab Fun

Lest you think all we do in my lab is mess around with magnets, I’m posting a few tweets with photos of today’s lab barbecue! Bonnie and I have an annual summer party for students, alums, and associates in our labs. Unfortunately, my camera is broken, so I have to rely on photos taken by Bonnie and her student Megan, at the links below. Geoduck! Grilled oysters with bacon! Chocolate Olympia oysters, ammonites, and trilobites! A good time was had by all.

# Magnetic Susceptibility

My students and I are preparing to go up to Bellingham on Thursday to do some work in the paleomagnetic lab up there, so we spent today’s lab meeting getting everyone acquainted with the data they are going to collect. I started explaining something in the lab meeting that I thought could use a demonstration. So here it is.

Rocks might have lots of different magnetic particles in them. They might contain magnetite, an iron oxide that forms in igneous and metamorphic rocks as well as in soils; they might contain titanomagnetite, a common  consitituent of oceanic basalt; they might contain maghemite that formed as magnetite was oxidized — rusted — by weathering, or they might contain maghemite that formed in soils; they might contain hematite or goethite, indicating soil formation in dryer or wetter environments… there are even rock-forming minerals like pyroxenes and micas that are magnetic to a certain extent. In addition to forming in different environmental conditions, all of these minerals have particular quirks in their record of Earth’s magnetic field. So we need to be able to tell the kinds of magnetic minerals apart.

One way we differentiate between magnetic minerals is by their response to weak magnetic fields. So I tried a little experiment. I put a bunch of different materials inside a wire coil. I could send a current through the coil, producing a (weak) magnetic field at the coil’s center. I also set up a magnetometer to measure the magnetic field just outside the coil.

Why might the field inside the coil be different from what I measure with the magnetometer? This is a secret that is not addresed in the physics textbook we use in Physics II: there are two different ways of describing magnetic fields. We call the magnetic field that the magnetometer $\vec{B}$, and we measure it in Tesla. But there are two kinds of things that produce that magnetic field. One is the current through the coil, and the other is whatever is inside the coil (or outside it but close by). So we say that there is an applied magnetic field (applied by the coil to whatever is inside it) as well as a little bit of extra magnetic field due to whatever we put inside the coil. In physics terms, we call the applied magnetic field $\mu_0\vec{H}$, and the bit of extra field from whatever is in the coil $\mu_0\vec{M}$. Both of these are measured in A/m. We say that:

$\vec{B} = \mu_0(\vec{H}+\vec{M})$

There are two ways you can get a little bit of extra field from putting stuff inside the coil. A large number of Earth materials become magnetic when you put them in a magnetic field, but then revert to what most people would call “non-magnetic” when the magnetic field is turned off. For example, if you put an iron-bearing garnet crystal inside an area with zero magnetic field, it wouldn’t attract a compass needle. But as soon as you turn the magnetic field on, the compass needle begins to deflect – ever so slightly – toward the garnet. We call that garnet paramagnetic. Other minerals, like quartz, are diamagnetic: put them in a magnetic field, and the compass needle deflects away from the mineral. For both paramagnetic and diamagnetic materials, the effect on the compass disappears when you shut off the magnetic field you’ve applied. We call this an induced magnetization.

Some materials also have a remanent magnetization – a magnetization that remains after the $\mu_0\vec{H}$ field is switched off. Magnetite is a good example of this. Besides behaving like an induced magnet, magnetite also has induced magnetic behavior.

So: I took pieces of a bunch of different materials – steel, teflon, hematite, and various other minerals – and put them in the middle of the coil to see what would happen to the magnetic field as I increased and decreased the current. I tried the mica in two different directions (with the edge pointed toward the magnetometer, and with the flat face 45° from the magnetometer) to see if there was an effect.

Here is a plot illustrating the response of these materials to the magnetic fields produced in the coil:

The first thing you might notice is that all materials, more or less, make a linear trend on this plot. So the total magnetic field is proportional to the applied field. The biggest effect is in the bar magnets: they are ferromagnetic (the line of points does not intersect the origin, meaning that there is some magnetic field that remains when you turn off the $\mu_0\vec{H}$ field). The rest of the materials have a different slope, varying between low (Teflon) and high-ish (empty sample holder, keys, nail).

The ratio between applied magnetic field and a material’s (induced) magnetization is called magnetic susceptibility. It is given the symbols k, κ, or χ. If you were to measure magnetic susceptibility carefully, you could identify differences between these minerals – perhaps even between the different orientations of the mica. To do that, you need to have a good idea about what the response of your magnetometer would be if your coil were empty. That’s your model for how your measurement device works. It’s just a linear equation here: $y = m x$ (using the variables we have here, $B = \chi_0\mu_0H$). You can then subtract your prediction based on the empty coil model from all of your $\vec{B}$ magnetic field measurements to see whether the stuff you put in the coil is adding to $\vec{B}$ (ferromagnetic, paramagnetic) or decreasing it.

Here is what you get when you subtract out the empty sample holder’s response:

On these graphs, a positive slope indicates a material that behaves as a paramagnet; a negative slope indicates a diamagnet. Most of these materials behave like a combination of the two – not a particularly steep positive slope (except for the bar magnets) and a variety of negative slopes. The Teflon rods have the steepest negative slope because they contain the most diamagnetic material. Because ferromagnetic materials retain a $\vec{M}$ when the applied magnetic field is reduced to zero, their behavior shows up as a vertical offset of the whole graph, as seen in the bar magnets and in the hematite, below:

Here is the R code for the graphs:

…and the data file.