| Description |
Few scientific fields have been as dramatically accelerated by war as Earth science. Prior to the twentieth century, Earth science was largely dominated by natural philosophers debating whether the Earth had been formed by a ball of magma that solidified or a ball of liquid that precipitated. This great debate stymied innovation and helped to set Earth science behind other fields like mathematics and physics in a time of great intellectual advancement. Things started to change in 1912 when Alfred Wegener suggested that continents could move. This notion challenged Earth scientists to change the scope of the problems they were trying to solve and incorporate their ideas with Charles Darwin's frame of geologic time that he proposed in 1859. Earth scientists reacted negatively to Wegener's ideas initially, but in the following years, geoscientists began to incorporate precise quantitative techniques to evaluate Wegener's claims and the field began to mature. Sadly, Wegener died an outcast of the scientific community because he failed to give a mechanism that could cause continents to move. Interestingly, much of the evidence for his idea was later discovered as a result of warfare. Wegener's thinking led to the paradigm shift of Earth scientist's accepting plate tectonic theory. Evidence for plate tectonics began to accumulate during World War II as naval control became a priority for countries. Money poured in as navies sought to detect other ships, particularly submarines, using techniques like radar and magnetism. High quality bathymetric data first allowed Henry Hess to hypothesize in 1962 that the seafloor was spreading, driven by mantle convection; the missing link to the plate tectonics problem. His ideas were later supported by electromagnetic surveys that showed banding of magnetic anomalies on the seafloor, a smoking gun for Hess's ideas. iii The Union of Soviet Socialist Republics (USSR) began developing nuclear weapons just as the United States (US) was leading the Manhattan project at the beginning of the Cold War. Initial efforts to better understand the technological progress and capabilities of the USSR's weapons program was found in earthquake science. Seismology first sought to understand earthquakes from an academic perspective, as earthquakes were important to study because large ones could kill hundreds of thousands and change the Earth like few other forces of nature. But it was quickly realized that earthquake location and magnitude estimation techniques could be translated to explosions. The installation of high-fidelity seismographs was useful for detecting and estimating the yield of nuclear explosions in the USSR, but also was leveraged for earthquake analysis. Large earthquakes were found to mostly occur in discrete belts worldwide, which defined the geometry of the plates and made plate tectonics even more robust. Seismology was forever changed when, in 1963, the Limited Test Ban Treaty (LTBT) was ratified by the US and USSR and the United Kingdom. This agreement made testing nuclear weapons illegal in space, underwater, and the atmosphere. The LTBT pushed nuclear weapons testing underground and provided a perfect opportunity for seismologists to contribute to international relations and geopolitics. An important part of the LTBT involved the US funding of Project Vela Uniform, which gave seismology a 3000% increase in federal funding in the course of one year. The funding was to enable scientists to effectively detect, locate, and estimate the yield of nuclear explosions. The sudden influx of resources allowed seismology to accelerate at an unprecedented rate. In 1974, testing was further constrained when the Threshold Test Ban Treaty (TTBT) was passed bilaterally between the US and the USSR. This treaty limited testing to 150 kilotons, which gave seismologists even more reasons to refine their techniques to detect smaller seismic sources. The fall of the USSR led to the Comprehensive Test Ban Treaty (CTBT) being first proposed in 1996. The CTBT is a zero-tolerance treaty, meaning weapon tests of any size are forbidden. This development gave seismologists the ultimate challenge; detect sources as small as conceivable or valuable. It was soon realized that decades of perfecting explosion analysis would need to be re-thought, as discriminating small sources would prove to be systematically different than previous larger explosions. Once a seismic source is detected using a network of seismographs, it needs to be analyzed to determine the type of source that the event is, for example, an explosion or earthquake. It is of the utmost importance to separate illegal nuclear explosions from perfectly natural, tectonic earthquakes. Techniques that accomplish this goal are known as discriminants, as they differentiate between explosions and earthquakes. Historically, discriminants were developed for large explosions, that were easily recorded at teleseismic source-to-receiver distances (>2000 km). Techniques like mb:MS and P/S amplitude ratios proved to be effective, but these worked at teleseismic distances where the wavefield is low frequency and more homogenous. At local distances (<200 km), wavefields are enriched in high frequency and are heterogenous, so the robust methods of the past are less successful. This has created a need for new discriminants to be proposed and work at local distances. Koper et al. (2016) discovered that they could discriminate between deep tectonic earthquakes and shallow mining-induced seismic events in Utah by comparing different magnitudes of the events at local distances. They proposed that ML-MC, or the difference between the local magnitude and the coda duration magnitude, was sensitive to depth. Therefore, by assuming that most earthquakes are relatively deep and all explosions occur near the surface, a jump in logic could be made that the discriminant could also differentiate between those sources. In order to evaluate this idea for application to realworld nuclear monitoring problems, it had to be better understood. First, this discriminant needed to be shown to work in a variety of geographic and geologic locations-it would not be useful if it was a phenomenon only associated with Utah. This was accomplished by Holt et al. (2019), when they showed that ML-MC worked in every place tested, like Italy, Oklahoma, and Yellowstone. The next step was to see if it worked for nuclear weapons analogs that were plentiful: mining explosions. The first chapter of this thesis answers the question of ML-MC being applied to mining explosions (Voyles et al., 2020). We rigorously analyzed thousands of explosions in Utah and showed that in general, ML-MC identified them as shallow sources. Another outcome from this project was an open-source database of high-quality explosions at local distances, which is already being used by the scientific community. This database provides an excellent opportunity to leverage machine learning techniques on the local distance discrimination problem. The second chapter of this thesis applies machine learning techniques to separate explosions from earthquakes and was published in the University of Utah's undergraduate research journal. The paper seeks to reproduce results from Linville et al. (2019) that used complicated, uninformed machine learning models to also discriminate sources at local distances. We use simple, interpretable models that are comparably computationally-inexpensive and obtain similar performance. One of the most important vi steps in establishing ML-MC is to try to reproduce empirical observations, like all prior research discussed has been, using numerical modeling. This allows for complete control over the simulation. In particular, there is a need to understand from a physical standpoint the mechanism as to why ML-MC is sensitive to depth. There are various phenomena that could account for this and there is a need to identify which are most influential. The third chapter of this thesis presents preliminary results concerning modeling and understanding why ML-MC works using high-performance computing, and it will be submitted for publication. By generating numerous models, each with a different combination and weight of the mechanisms that can explain our observations, we can tease out the model that best explains our observations. Final steps to understand ML-MC include continuing modeling, continuing to prepare catalogs of earthquakes and explosions, and test ML-MC on other nuclear explosion analogs like large chemical explosions and even nuclear explosions themselves. |
