I've had a whirlwind of two days to start my internship here at Indiana University!
Here's my progress so far! Look at those pretty colors.
Now I should probably tell you why they are significant, and why I have decided to share them with you. For starters, I've actually figured out my project now, and that is exciting in itself. I will be doing P-wave tomography and creating a new synthetic model that will hopefully help define adjustment factors for existing tomography models of the Illinois Basin region. This will improve the accuracy of models in the region, and hopefully help to explain some of the interesting questions that have arisen from previous OIINK Data analysis.
To accomplish this, my project is broken into three distinct phases. First, I have to pick P-wave arrival times for the new raw data that is coming in from the OIINK array. Secondly, I have to prepare this data for the tomographic inversion. And thirdly, I have to run the tomographic inversion and compare this new synthetic model to the data and existing models. These three steps will consume most of my summer when I am not out in the field helping to maintain the array. Today, I officially started on phase 1. I am working on a new name for phase 1 because everything in science needs a cool name or acronym. I'll get back to you on my naming progress. Don't let me forget. To help pick the arrival times I have been using a program called "dbxcor", which my mentor developed. I am able to load waveform data from all the stations, stack the data, and throw out bad waveforms all at once in dbxcor. While this process is fairly simple, throwing out noisy data and stacking the data correctly isn't as easy as it sounds.
After the data is stacked, it is crucial to check the residuals to see if you lined up the waveforms correctly. To do this, I used a GMT shell script written by my mentor that plotted the residuals with a gradient color map as well as showing the locations of the stations. It was relatively obvious if a station was out of sync, usually indicated by a steep color change or a circle around a particular station. If you look at the picture above, the red circle around station FVM is a perfect example of an error. To fix this, you need to go back in and adjust your P-wave arrival time for that particular station, or remove the station from the stack. This isn't as easy as it looks, as finding the station was challenging. You might be asking yourself at this point, "But Bradley, the station's are labeled? How was that hard?" Well, to answer that question, the stations were not originally labeled. They are labeled because I told them to be labeled. Or my GMT shell script told them to. Without the station labels, the process of identifying the station was ridiculously tedious. And thus, with the help of Dr. Hersh Gilbert, the Purdue advisor who is currently in town, I worked on adding the functionality into the script. GMT is notoriously nasty to script, and this proved no different. Even with Dr. Gilbert's help, it took us over an hour to add the simple functionality of printing the station locations on the map. It was complicated because we not only had to work within GMT syntax, but had to use database and logic commands which I hadn't used before in shell scripts. Finally finishing the addition proved incredibly satisfying, as I knew the feature would prove quite useful in increasing my efficiency in the future. Having successfully scripted GMT was a nice bonus as well.
This feature also allowed us to quickly identify station FVM as a consistent misbehaver. It was showing up with a similar red circle on repeated events, and seeing the same station encircled was a serious red flag. Knowing things like this are critical to ensuring that your analysis isn't pulling from faulty stations. The last thing you want is to make an interpretation based on incorrect data. This provided a good learning experience for how to identify anomalous data, and why it's important to understand what errors should look like.
The work described above in addition to spending yesterday learning about the broader context of the OIINK experiment comprises the bulk of the past two days. I am excited to continue processing the data over the next couple of days. Josh and I also get to go out into the field next week to service stations and collect data from our non-cellular stations which should be a nice change of pace from processing data in front of a computer. I'm having so much fun working on a real data set with huge unanswered questions and look forward to exploring the new questions that present themselves in the next couple of weeks.
Until the next P-wave arrival time,
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