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<div class="x_ContentPasted0" style=""><b><span style="font-family:Calibri,Helvetica,sans-serif; font-size:14pt">Onions, Grapes, and Tiny Quakes: New Dimensions in Deep Learning Seismology</span><br>
</b></div>
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<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size:12pt"><b>Speaker:
</b>Tim Clements</span></font></div>
<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size:12pt"> Earthquake Science Center</span></font></div>
<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size:12pt"> USGS</span></font></div>
<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size:12pt"><b>Location:
</b>Hybrid (online and in-person at 1711 Illinois St, Golden, CO, Rm 204)</span></font></div>
<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size:12pt"> *Non-USGS visitors welcome! Please bring a photo ID and arrive early to allow time to go through building security*</span></font></div>
<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size:12pt"><b>Date/Time:
</b>Tuesday, May 2, 2023 10-11AM MDT</span></font></div>
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<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size:12pt"><b>Abstract:</b> <span class="x_ContentPasted1" style="font-size: 12pt; background: rgb(255, 255, 255);">I will present recent and ongoing work exploring
representations of the seismic wavefield – building from classical labels, such as “P-wave”, “S-wave”, or “noise”, to vector space representations of the wavefield at a single station and across an entire seismic network to encoding the seismic wavefield as
a stream of integers. The key insight to my approach is learning higher-dimensional representations of seismic waves by predicting future shaking or waveforms from current waveforms. I train a suite of deep learning models, ranging from tiny models with a
few thousand parameters to generative pre-trained transformer models with billions of parameters, for tasks such as earthquake detection, earthquake early warning (EEW), and earthquake waveform synthesis.
</span></span></font></div>
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<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size:16px"><b>Bio: </b><span class="x_ContentPasted3" style="font-size: 12pt; background: rgb(255, 255, 255);">Tim Clements is a Mendenhall Fellow at the USGS Earthquake
Science Center in Moffett Field, CA. He focuses on applying natural language processing, deep geometric learning, and tiny machine learning to a variety of earthquake-related problems. He received a PhD in Earth and Planetary Science from Harvard University
in 2021 and a BS in Engineering from Cornell University in 2015. He has deployed seismometers in Ethiopia, Chile, Argentina, Massachusetts, Oklahoma, California, and Washington state. At ESC he is prototyping low-cost MEMS accelerometers for EEW, designing
new algorithms for EEW, and extracting a seismic grammar from large earthquake datasets.
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<div style=""><font face="Calibri, sans-serif" style="color: rgb(32, 31, 30);"><span style="font-size: 12pt; color: rgb(102, 102, 102);">For questions, comments, or speaker suggestions, please contact the </span></font><b style="font-family:inherit; font-size:12pt; font-style:inherit; font-variant-ligatures:inherit; font-variant-caps:inherit"><span style="font-weight:400"><span style="font-size: 12pt; font-family: Calibri, sans-serif; color: rgb(102, 102, 102);">GHSC
Seminar Committee: Andrea Llenos <allenos@usgs.gov>, Benjamin Murphy <bmurphy@usgs.gov>, Lauren Schaefer <lschaefer@usgs.gov></span></span></b></div>
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