A Data-Driven Framework for Automated Detection of Aircraft-Generated Signals in Seismic Array Data Using Machine Learning
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- Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, Texas, U.S.A.
- Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego, California, U.S.A.
- Department of Earth Sciences and Southern California Earthquake Center, University of Southern California, Los Angeles, California, U.S.A.
Abstract Ground motions associated with aircraft overflights can cover a significant portion of the seismic data collected by shallowly emplaced seismometers, such as new nodal and Distributed Acoustic Sensing systems. This article describes the first published framework for automated detection of aircraft on single channel and multichannel seismic data. The seismic data are converted to spectrograms in a sliding time window and classified as aircraft or nonaircraft in each window using a deep convolutional neural network trained with analyst-labeled data. A majority voting scheme is used to convert the output from the sequence of sliding time windows onto a decision time sequence for each channel and to combine the binary classifications on the decision time sequences across multiple channels. Precision, recall, and F-score are used to quantify the detection performance of the algorithm on nodal data using fourfold time-series cross validation. By applying our framework to data from the Sage Brush Flats nodal array in Southern California, we provide a benchmark performance and demonstrate the advantage of using an array of sensors.
- Research Organization:
- Univ. of Southern California, Los Angeles, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- SC0016520
- OSTI ID:
- 1981026
- Journal Information:
- Seismological Research Letters, Vol. 93, Issue 1; ISSN 0895-0695
- Publisher:
- Seismological Society of America
- Country of Publication:
- United States
- Language:
- English
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