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Remona Gurrieri
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These past couple of weeks have mostly been me going through more and more data using the Matched Filter Technique (MFT). I do not want to jinx myself, but I would like to think that I have finally figured out most of the nuances of the processes and my multitude of codes. To help myself, I have created a self-help document that is a step-by-step guide that details what works, the commands I need, what files I need and what did not work in the past. Creating this document has honestly helped a tremendous amount. It saves me the trouble of trying to struggle through the confusing lines of code each time that I try to run it.
Another frustrating issue that I ran across was related to self-detection. At the simplest level, what my code does is take a template and compare it to continuous data to see how similar it is to the template. It does this through computing the cross-correlation. Self-detection arises when the template recognizes itself in the continuous data. Therefore, wherever the template lies in the continuous data, a cross-correlation value of 1 is seen. Bewilderingly, when I first ran my code, I would get no self-detection. This was a red flag that something was not running correctly and was frustrating to see that after hours of running a code, it did not work properly. After a very long day, the issue was found: the time window for the data on the day of the template was incomplete. When editing my data, I had accidentally left a few stations in the continuous data that had very different time windows. These data had windows that were half the time of the rest of the data, and the template arrivals did not happen to be within this time window. My code, when looking at continuous data for each day, will only run on the smallest common time window. Hence, the code was not even looking at the time window in which the template and a lot of data were located. Once I fixed this issue, I finally started to get self-detections.
Another issue with the MFT I have come across is deciding which events to use as templates. The first criteria is that the template must come from the same spatial region as the area of study. To find these events, I use a catalog of known events that have occurred in the region. Because I am applying a bandpass filter to my data, distance from the station is also an issue. To ensure that events we are seeing are local (high frequency) events, we only want to use events in the catalog that relatively close to the stations. Ideally, events less than 100 kilometers away from the stations would be used. However, none of the events listed in the catalog for the northeast region of China fit this criteria, so I am using events that are as big and close as possible.
I am currently looking at events in the first region I will be looking at, northeastern China. Below is a map of the region. The black triangles are the seismic stations in the 1A array. The circles represent events that are from the catalog of known earthquakes in the region. The red circles represent events that occurred before the 2008 Wenchuan mainshock, and the blue circles represent events that occurred after the Wenchuan mainshock. Because of the scale of the map, it may be difficult to get a geographical sense of the region. For reference, the stations end right at the border between China and North Korea. I produced this map in GMT.
China is amazing! We have been to the Forbidden City, the Great Wall, Behai Park, the Olympic Park, the Hutong, and other places here in Beijing. The graduate students at the Chinese Academy of Sciences are extremely welcoming and have been more than willing to show us around the city. One of them took us to see Jurassic World 2 at a movie theater here (the movie was in English with Chinese subtitles). Beijing is such an ancient city steeped with culture and history. I love the architecture of so many of the buildings here. It is also interesting to see the differences between what Americans and the Chinese consider historical. America is relatively young and any building built in the 1700's or 1800's seems very old to us. However, here, they will tell us that something built in the 1600's is young.
Another interesting and useful tool that Dr. Peng has prepared me with is the ability to read SAC files into MATLAB. I also am learning how to generate spectrograms using these waveforms. Additionally, I have learned that I can directly load data into MATLAB without having to download it into my computer first. This is useful for loading single waveforms, but loading a lot of data directly into MATLAB takes a long time and I have discovered that with the large amounts of data with which I am working, it is actually easier to download my files directly to my computer first.
I am super lucky to have another intern, Harrison, working on the same project as I am. On that Saturday, even though we had different flights to Atlanta, we met up at the Atlanta airport to grab a quick bite to eat. Then we took an Uber to the apartment at which we were staying. Our Uber driver was extremely friendly and definitely gave off a good first impression of Atlanta.
On Monday, we came into work to meet our mentor, Dr. Peng, for the first time. Dr. Peng is an incredible mentor, and I can tell how extremely knowledgeable about his field he is. It has been a week and I have already learned a tremendous amount from him. I am super excited for the rest of the summer!
This first week has mainly been learning to use the different tools that I will be needing for the rest of the summer. We have been learning how to use Matlab, UNIX, SAC, and GMT. GMT seems to be a very powerful tool for mapping. Dr. Peng has many great tools available on his website that we have used to learn how to navigate several different programs. I really found them well written, but Dr. Peng was still more than happy to answer any questions that we had.
A daunting sorceress offers Mirka some help with her goal, but first Mirka is obliged to do her Sixth Day chores. The mix of magic and Jewish life is lively and fresh. Maybe in her next adventure, Mirka can start cutting down on the overpopulation of angst- ridden adolescent werewolves and vampires. Ages 10 and up.
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