Next Article in Journal
Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images
Next Article in Special Issue
Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data
Previous Article in Journal
Validation of Thigh Angle Estimation Using Inertial Measurement Unit Data against Optical Motion Capture Systems
Previous Article in Special Issue
Automatic Building Extraction from Google Earth Images under Complex Backgrounds Based on Deep Instance Segmentation Network
Open AccessArticle

Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog

1
Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA
2
Air Force Technical Applications Center, Patrick AFB, FL 32925, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 597; https://doi.org/10.3390/s19030597
Received: 27 November 2018 / Revised: 22 January 2019 / Accepted: 28 January 2019 / Published: 31 January 2019
(This article belongs to the Special Issue Deep Learning Remote Sensing Data)
The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep temporal convolutional neural network is employed to learn the complex non-linear filters required to transform the single-trace waveforms into corresponding idealized characteristic functions. The training data consists of all arrivals in the International Seismological Centre Database for seven seismic arrays over a five year window from 1 January 2010 to 1 January 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 January 2015 to 1 January 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the generalization and transportability of the technique to new stations. Detection performance against this test set is outstanding, yielding significant improvements in recall over existing techniques. Fixing a type-I error rate of 0.001, the algorithm achieves an overall recall (true positive rate) of 56% against the 141,095 array-beam arrivals in the test set, yielding 78,802 correct detections. This is more than twice the 37,572 detections made by an STA/LTA detector over the same period, and represents a 35% improvement over the 58,515 detections made by a state-of-the-art kurtosis-based detector. Furthermore, DeepPick provides at least a 4 dB improvement in detector sensitivity across the board, and is more computationally efficient, with run-times an order of magnitude faster than either of the other techniques tested. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network. View Full-Text
Keywords: geophysical signal processing; pattern recognition; temporal convolutional neural networks; seismology; deep learning; nuclear treaty monitoring geophysical signal processing; pattern recognition; temporal convolutional neural networks; seismology; deep learning; nuclear treaty monitoring
Show Figures

Figure 1

MDPI and ACS Style

Dickey, J.; Borghetti, B.; Junek, W. Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog. Sensors 2019, 19, 597.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop