Academic Journal
Characterization of seismicity from different glacial bed types: machine learning classification of laboratory stick-slip acoustic emissions
| العنوان: | Characterization of seismicity from different glacial bed types: machine learning classification of laboratory stick-slip acoustic emissions |
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| المؤلفون: | Saltiel, Seth, Groebner, Nathan, Sawi, Theresa, McCarthy, Christine |
| المساهمون: | Directorate for Geosciences, Division of Polar Programs |
| المصدر: | Annals of Glaciology ; volume 65 ; ISSN 0260-3055 1727-5644 |
| بيانات النشر: | Cambridge University Press (CUP) |
| سنة النشر: | 2024 |
| الوصف: | Subglacial seismicity presents the opportunity to monitor inaccessible glacial beds at the epicentral location and time. Glaciers can be underlain by rock or till, a first order control on bed mechanics. Velocity-weakening, necessary for unstable slip, has been shown for each bed type, but is much stronger and evolves over more than an order of magnitude longer distances for till beds. Utilizing a de-stiffened double direct shear apparatus, we found conditions for instability at freezing temperatures and high slip rates for both bed types. During stick–slip stress-drops, we recorded acoustic emissions with piezoelectric transducers frozen into the ice. The two populations of event waveforms appear visually similar and overlap in their statistical features. We implemented a suite of supervised machine learning algorithms to classify the bed type of recorded waveforms and spectra, with prediction accuracy between 65–80%. The Random Forest Classifier is interpretable, showing the importance of initial oscillation peaks and higher frequency energy. Till beds have generally higher friction and resulting stress-drops, with more impulsive first arrivals and more high frequency content compared to rock emissions, but rock beds can produce many till-like events. Seismic signatures could enhance interpretation of bed conditions and mechanics from subglacial seismicity. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1017/aog.2024.11 |
| الاتاحة: | https://doi.org/10.1017/aog.2024.11 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305524000119 |
| Rights: | http://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: | edsbas.70DF00F9 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1017/aog.2024.11 |
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