Young Geologists' Field Day

Hello hello,

Lab work this summer has been lots of fun, it’s also been flying by! I feel like I’ve only just arrived but here it is, the last week to get some work done. Just a few days ago Monica and I had the opportunity to go and collect samples locally from the Vermillion river. While not as exciting as a trip to China, it was definitely a fun experience. Before we could go, Monica and I had to round up the supplies from the many different rooms Geology holds within Carnegie and Severance.  Those supplies included several sieves and buckets, a fair few sample bags, a large spoon to collect the sample from the river and a handful of spoons to move the sediment from the sieve into the bags (super scientific, I know!) Some people who are more critical of their terrain may look at the photos and say that we were not in a river as much as we were in a drainage ditch on the side of the road; I would say to those people, you’re right, but where’s the fun in that? We can nitpick the topographical features of the beautiful state of Ohio all we want but a day that has field work in it is a good day.

Monica here, with an update on the sediment we collected. Unlike Marcus I had a bit of a rougher time, as I was the one sacrificed to ride in the trunk with the sieves and freshly-collected samples. After much labor and help/interference from Amanda’s children we managed to collect the samples we needed from the river to be used further on our leaching adventures. They were collected as part of a project that I’ve been working on to determine the right ratio of Acid to Sample for leaching. Unfortunately the previous sample I leached was not collected recently enough so the fallout radio nucleotide Be-7 dissipated. By quickly leaching this sample we should be able to get the data were missing out on. They are currently in the oven, evaporating off the ditch-water and getting ready to be stripped in acid.

To make the field photos more enjoyable, we included some Gerber-baby level cuteness toddlers (Amanda’s children), who accompanied us into the field. Photo credit to Amanda’s wonderful au pair, Jenny. Enjoy! 

Staying STRONG in the lab

Hello!
Monica here, celebrating my second week as a STRONG Scholar working in the geomorphology lab this July. I'm an incoming first-year and I hail from Shorewood Wisconsin (just north of Milwaukee). Currently I am considering doing Oberlin's 3-2 Engineering program with the ultimate goal of becoming an environmental engineer. I also am interested in Hispanic Studies and Politics. In my free time I enjoy folding origami lotuses, swimming competitively, and creating scavenger hunts.
Thus far at Oberlin, I have been working on some odds and ends in my research. From day one I started running calculations to test how changing the accuracy of parameters affects the efficiency (how active a sample is) in a program called Angle. The goal here was to discover how specific the parameters need to be to stay within the margin of error. On a larger scale, it helps us judge how much information we are required to know about the sample in question to receive accurate results when preforming calculations. The three parameters included sample composition, source height, and density.
I used a bunch of different methods of simplifying the composition percentages from nine elements into as few as one. Overall, I found that simplifying the data didn't have a very large impact. I was definitely surprised by that but at the same time I was relieved. Knowing that composition data isn't as important makes the process of running these calculations easier for others in the future.
Here's a graph showing five of the different methods I used with black lines above and below the x-axis marking the margin of error that thou shalt not cross:

I also studied a few different source heights and these turned out to be quite a bit more influential.

I found a similar pattern when it came to density as well.

Based on the graphs I made, it was apparent that these values needed to be somewhat correct to get accurate calculations. Because of that, my next step of lab work was to analyze 13 of the leachate samples from one of the labs previous expeditions to China. This presented numerous challenges, as leachate is the outer coating of sediment, separated using acid. The samples had a unique acidy smell and presented some difficulties in measurement. It was a double challenge to be using calipers for the first time on leachates that were fractions of millimeters thick. Not to mention, when the ordeal was over I had to pray that I washed everything thoroughly enough so it wouldn't get corroded. Despite my struggles in getting all the values to agree with one another, I was finally able to get results accurate enough to graph.
Ultimately I am really excited because my work will contribute to Amanda's research on a new way to more accurately measure Lead 210 and hopefully create a better system for quantifying this indicator that can be used to measure erosion.
Next in the process, Marcus and I will be leaching more samples to be examined later on and *fingers crossed* the hood will remain intact as the HCl evaporates. We also are all reading a series of Parsons and Foster papers (and their critiques) around the validity of using Lead 210 as an indicator of erosion which are laden with witty scientific dissing.
I'm hoping my next two weeks this summer of research will be just as enjoyable as the past two and I want to thank both Marcus and Amanda for facilitating this fantastic experience and helping me get adjusted. Geomorphology rocks!

Please someone tell me how to write generic code for specifc tasks

Heyo,  Joe here, coming off of a great semester of working with Marcus to coerce our data to play nice with Python.  Our goal was to unite the geographic data we had for the area upstream of each point with the isotope data from Harbin, our hard working germanium detector, into a format that could be sensibly manipulated with Python for graphing and statistical purposes. The problem was twofold, figuring out a way to sensibly store and access data, and how to take that data and use it to make graphs that were understandable and looked nice. I dove into writing a whole tangle of functions to pull out the data of interest and Marcus became good friends with the matplotlib documentation, his only ally in the noble fight against the matplotlib library.

It must’ve been just about two years ago now that I first started to truly get my hands dirty with both Python and ArcPy, ArcGIS’s Python library.  I started with a simple goal, create unique watershed files for each point in a shapefile full of sample collection locations.  Through a combination of the ArcPy documentation, stackoverflow answers, and a dear friend of mine with far more Python experience than myself, I was able to create such a script.  It was tailored to my specific project, but I tried my best to make it something that could be reused for other projects.  Looking back now, I would do it all totally differently, but ya live and learn!

When I began work on my next script, which extracted spatial information for each watershed, I became consumed with finding my way around ArcGIS’s ‘table joins’ which is perhaps the most obtuse way to unite two sets of data.  I won't go into detail, but I accomplished my goal, learning a lot about how ArcGIS stores data in the process, and thus began the quest that still consumes me this day, which is to avoid using ArcGIS at all costs, offloading as much work as possible to Python.

In the fall, I declared, to no one in particular, my intent to secede from ArcGIS, and began work on a Python project to manage my data, which would only dirty its feet by dipping into ArcGIS as needed for certain spatial analyses, then whisking the results out of the clutches of whatever heinous file that Arc would create, and into the sanctuary of my Python datatype.  Progress was slow, mainly because I kept on trying to start over!  My code worked fine, but I was never satisfied with how it was structured, I wanted this to be something that people doing similar, but distinct, work could use.  I struggled with how to avoid design decisions specific to my project, which was hard to do when I was also trying to use it at the same time to do my actual project. Eventually looming deadlines (apparently you need “results” when you “present” at a “conference”) forced me to move forward, so I ended the semester with a datatype to store the data about my samples, some functions to grab that data, and some functions to graph it.

Now, from what you read above when you saw “functions to graph” you may have thought to yourself, “oh, this must be where matplotlib comes into play” and you would be right, if I had an ounce of sense in me.  For a reason I am unable to explain, I’m not sure if it was ignorance about the existence of graphing specific libraries like matplotlib, hubris, or just naive fondness for LaTeX, I decided to write functions to generate the markup to generate plots using the PGFPlots package for Latex.  This meant that instead of calling functions like plt.plot(), I was writing long format strings to generate a file in the LaTeX markup language.  The results were rather pleasing, but when I came back to the project in February with Marcus I thought a more straightforward approach would be appropriate.  When we found out about matplotlib, I thought, “Now here is the answer to all of our problems!  All we have to do is hook up the Python code that stores the data to matplotlib and out will come beautiful graphs”.  Sure, I thought we might have to do some tweaking to get graphs up to our very refined standards, but how hard could it be.  For that answer, see Marcus’s post.

The template...

...and the result!

So, as Marcus went off to figure out just how to make matplotlib give us graphs that could be read with ease, I went off to figure out how I could pull the data we wanted out of the jumble of samples we were working with.  Our dataset was a collection of 83 soil samples from three different field seasons.  For each sampling location we determined the area upstream of it, and calculated various geographic parameters.  

Now that I had gotten all this data, it was time to get organized logically.  This took some time, but boy was it worth it.  Once I knew that all the data would be have the same way, I wrote a series of functions (way too many functions, probably, but once you learn Lisp, there’s no going back) to return lists of the data we actually wanted to plot.  If each sample has activity and error values for 3 different isotopes, a thousand different geographic parameters, a location, links to files, lists of other samples that this sample is related, it’s not quite plug-and-play.  But it got done, and meant that if some of the values for our samples changed (as they often do) or if new samples got added or old samples got removed, as long as they conformed to the standards, we didn’t have to do a darned thing!  Just take the list of sample objects, plug it into the function that pulls out the data you want to graph, and then shoot the result of that into Marcus’s graphing code.  Badadbing badaboom!  

    Things should be smoother from here on out...until I finally figure out that perfect structure and write the definitive program for managing soil samples, computing watersheds, doing some spatial analyses, and plotting and tabulating the data.  Someday it will happen, and we will be better for it. 

It's been a great 2+ years working for OGRe, but I wouldn't be surprised if I come back in one form or another, even if it's just to preach about why we should be scripting more and clicking less.

Lab Work (Spring 2016)

Hi all, 

Marcus here, and ready to share with you what i've accomplished in the while working in the lab this Spring. This project was quite an undertaking from the very beginning; I had just conquered the mighty task of “Hello World” in java when I found out that I’d be paired with Joe to prevent more Excel made graphs from entering publication. We were given the option to either work in R, a completely unfamiliar language to both Joe and I, or to see if there were any ways to get Python to co-operate. Enter matplotlib (MPL).

This library was so combative that we had to make use of a separate application called Jupyter, which already had MPL integrated in it, to start working. Determining a starting location was a task in itself. Our first graph made extensive use of an oh-too-kind stackoverflow users code which gave us a 7x7 grid of information. That was a lot to take in, so we started looking into how get more specific with what we were presenting. The next graphs we created were specifically targeting lead, in-channel vs overbank and resample vs original, including variants that had data that accounted for negative values and those that didn’t. The nature of working with lead values is that they all had crazy error-bars, which got to be distracting visually. Suffice to say, there were a lot of moving pieces that didn’t want to work together at first.

This is when we started changing up our approach, Joe had delegated me to be more in charge of creating the graphs themselves while she continued working on her already built code that was able to grab and pair related information sets. My job required me to understand what data I was being given and how to use that in the graphs I would be creating, so naturally I needed to understand, at least some parts of, the code Joe was already working on. Now my to-do list included: learning how Jupyter worked, learning how to use MatPlotLib, understanding how Joes code was structured. Maybe it would’ve been easier to work in R. Jokes aside, it was a somewhat daunting task, so I figured it’d be best to start by seeing how Joes code worked, as she was much more accessible than the authors of the other two applications.  

It was a really interesting experience getting to see how Joe went about setting up her code to retrieve data and return meaningful results. The structure she had set-up worked in a way that was fairly straightforward, so figuring out how to add to and build off of what was there already wasn’t too difficult. It was a nice introductory period before we got our hands dirty with MatPlotLib. As I mentioned earlier, we really started off making plots that looked nothing like what our final graphs became. It was definitely a learning experiencing as we figured out what worked best and how to get information dense graphs that were still intelligible. Stackover-flow and the many documentation web pages quickly became purple links in my google searches as to why this wasn’t working or how to change this seemingly obvious part of the graph. Changing from regular plots to scatterplots, playing with subplots and legends and even changing the font size, color and shapes all seemed to have specific intricacies that want to provide un-intuitive results for seemingly small modifications. Ultimately though, Joe and I were able to work through these bugs as they popped up and create some good figures in the process.

This semester was a great introduction to working in a lab and I'm exciting to see what the next several years will bring!

STEM Night

On Friday 6 May, the Geomorphology Group joined other geology students to participate in STEM night, an outreach event for 3rd-5th graders. We had a great time. Below are pictures of OC students interacting with elementary school students. They are playing with the stream table (with Marcus and Adrian), exploring mineral properties (with Andrea), and looking at fossils (with Alex). Sydney and Andrew aren't pictured.

Winter Term and Beyond!

Hi everybody! I hope you all had a wonderful Winter Term and are ready for a semester of research!

As stated in my previous blog post, I spent the Winter Term working on the SEM in order to determine whether it could be used to achieve quantitative elemental information. By comparing my data from the SEM to the data sent over from the College of Wooster by Meagan, who analyzed the same samples using an XRF device, I was able to determine which factors on the SEM to set in order to achieve the closest results.  In short, I determined which factors (such as magnification of the sample and how much time is spent analyzing the sample) are the best for achieving as close results as possible compared to the results achieved by Meagan using her XRF device. I spent the last week of Winter Term learning how accurate and precise I could get my data and also spent some time learning how to incorporate new standards into the SEM for future analysis of other samples.

            The upcoming semester will be spent continuing this research. I will determine a protocol to use the samples sent from Wooster as standards for analyzing ground up sediment by using fractions of the samples sent over by Meagan to try to further replicate the results by using different sample preparation techniques, such as dipping SEM tape in sediment, making grain mounts from the powdered versions of the samples, and even making dirt thin sections (hopefully with the new thin section machine we got in January!). In the future, I also hope to compare my results to samples that were analyzed separately at the University of Vermont using a portable XRF device. I will also eventually be using other mineral standards with the same protocols on the SEM.

            Adrian and I will be working together on a research project for the upcoming semester. I spent Winter Term preparing for this by reading through various papers related to radionuclides and their effects on erosion and the interpretation of particle size effects on the adsorption of certain isotopes, such as ¹³⁷Cs and ²¹⁰Pb.

            I will talk further about my results and my future plans at the lab meeting this coming week. I look forward to hearing about what other people did over Winter Term!

-Chloe

Winter Term 2016: Marcus' Post

Hello everybody! The bulk of my first Winter Term at Oberlin was spent doing research in China with a crew of others who all seemed to be geologists at heart. This trip was eventful from the get-go, from dealing with crazy TSA lines and unfortunate weather delays, to overcoming the local cuisine, there was so much more of everything than I had anticipated.

We spent the first two days in Chengdu while waiting for my delayed luggage to arrive in China. This free time allowed us to have a little bit of the touristy experience, so Maisy and Liam took Al, Zanna and I to see pandas at the Chengdu Panda Research Base. After spending a few hours there, we all crammed into one taxi and made our way back to the university to explore for a bit. This was followed by listening to a talk about the relationship between health and air quality within China at Sichuan University. Not surprisingly, there was a correlation between increased pollution and health hazards, but what was interesting was that transportation output didn’t seem to be the biggest factor. The first day was really enjoyable as we were able to see firsthand things that were distinctive and relevant to China, like pandas and unfortunately pollution.

As for the actual field work, we sampled from a variety of sites, each unique and holding their own entry hazards. With the first couple it was mainly figuring out a way to navigate through the garbage. The first in particular wasn’t too bad as it was at the base of a construction site, while the next few really involved a lot of maneuvering around decent sized cobbles. Getting down was only half the battle as finding a good spot along the river that had sandy material to sieve, rather than mud, wasn’t easy. Although having so many people willingly to get their hands and feet wet really made the process easier.

Speaking of tasks that weren’t easy, Amanda really had the worst of it with some of the driving she had to do. Both she and the rental car deserve at least a few medals for going through what seemed like worse and worse road conditions every day. As we were heading up a mountain one night a blanket of fog completely engulfed the car, reducing visibility to maybe 5 feet at most. There were also the countless wēi qiáos (dangerous bridges) that were both crossed and thankfully just passed. Tunnels that stretched on for kilometers, without and without lights, and of course other drivers. As tense as some of that driving was, it really did provide for some of the best views you could get while traveling. There were several times where it felt incredibly surreal to be looking out and see such impressive terrain right outside the window. Coming back home to the mid-west where the land is all flat, I definitely miss having such amazing landscapes before me on long car rides.

Getting to try all sorts of different food was also a fun time. As someone who isn’t particularly skilled in using chop-sticks it was a task just getting it from the serving plate to my bowl, but by the end I had improved considerably. Although I am an adventurous eater it seems I’ve got a fairly weak stomach, and even trying to stick with the less spicy stuff, authentic Chinese food got the best of me. Nonetheless this trip was an amazing adventure to go on, missing luggage and stomach bugs and all. As Zanna mentioned, exploring China for its geologic information provides an experience that is completely unlike one you’d get as a tourist or someone on business. Getting to spend just over two weeks in great weather doing field work for the first time really made this a fantastic way to start the New Year, and im excited to see the data that will come out of it. I’m so grateful that I was able to be a part of this trip, and really hope to go on more in the coming years.

Marcus

Left to right: Amanda, Maisy, Liam, Marcus, Zanna, Al, Wong Yu Mei