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A Data-Driven Framework for Automated Detection of Aircraft-Generated Signals in Seismic Array Data Using Machine Learning

Journal Article · · Seismological Research Letters
DOI:https://doi.org/10.1785/0220210198· OSTI ID:1981026
 [1];  [1];  [1];  [1];  [2];  [3]
  1. Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, Texas, U.S.A.
  2. Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego, California, U.S.A.
  3. Department of Earth Sciences and Southern California Earthquake Center, University of Southern California, Los Angeles, California, U.S.A.
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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

References (23)

Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network conference February 2017
Basic data features and results from a spatially dense seismic array on the San Jacinto fault zone journal May 2015
Machine learning for data-driven discovery in solid Earth geoscience journal March 2019
Train Traffic as a Powerful Noise Source for Monitoring Active Faults With Seismic Interferometry journal August 2019
Deep learning for electroencephalogram (EEG) classification tasks: a review journal April 2019
Turboprop and rotary-wing aircraft flight parameter estimation using both narrow-band and broadband passive acoustic signal-processing methods journal October 2000
ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network journal January 2019
Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning journal August 2020
Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles journal August 2019
Machine Learning in Seismology: Turning Data into Insights journal November 2018
Deep Learning Models Augment Analyst Decisions for Event Discrimination journal April 2019
Characteristics of Airplanes and Helicopters Recorded by a Dense Seismic Array Near Anza California journal June 2018
Air-traffic events recorded by a dense seismic array near Anza California dataset January 2021
Analysis of Seismic Signals Generated by Vehicle Traffic with Application to Derivation of Subsurface Q-Values journal February 2021
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI journal January 2019
Discrimination of Seismic Signals from Earthquakes and Tectonic Tremor by Applying a Convolutional Neural Network to Running Spectral Images journal January 2019
A Survey on Transfer Learning journal October 2010
Exploring Approaches for Large Data in Seismology: User and Data Repository Perspectives journal January 2021
Probing Slow Earthquakes With Deep Learning journal February 2020
ImageNet Large Scale Visual Recognition Challenge journal April 2015
Efficient Emotion Recognition from Speech Using Deep Learning on Spectrograms conference August 2017
Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System journal June 2019
Deep Learning for Geophysics: Current and Future Trends journal July 2021

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