Convolutional neural network for earthquake detection and location

By T. Perol, Michael Gharbi, M. Denolle. Science Advances, 2017

786 citations recorded by the source corpus.

ConvNetQuake is the first neural network for detection and location of earthquakes from seismograms. The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment. Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today’s most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. We leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for earthquake detection and location from a single waveform. We apply our technique to study the induced seismicity in Oklahoma, USA. We detect more than 17 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm is orders of magnitude faster than established methods.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/22cf34a6299e09272e403337d2a47de9f6cf4c56

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Citations in the corpus

Machine learning for data-driven discovery in solid Earth geoscience (2019, methodology): “SeveralrecentstudieshaveappliedDNNswith various architectures for automatic earthquake and seismic event detection ( 16 , 68 , 69 ), phase-picking ( 66 , 70 ), and classification of volcano-seismic events ( 71 ).”
Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking (2020, background): “Perol et al. 30 used a network of 8 convolutional and one fully connected layers to detect and cluster events based on three component waveforms.”
Deep-learning seismology (2022, methodology): “The classification capability of DNNs makes them ideal for processing tasks such as event discrimination ( 6 – 8 ), event detection ( 9 , 10 ), phase picking ( 11 , 12 ), first-polarity determination ( 13 , 14 ), and phase association ( 15 , 16 ) that together form a seismicity monitoring workflow.”
Seismic Signal Denoising and Decomposition Using Deep Neural Networks (2018, methodology): “Deep learning has been shown to be a powerful tool for learning the characteristics of seismic data [47, 48, 49, 50, 51, 52, 53, 54].”
Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “[144] proposed CNNs for detecting earthquakes and locating them from existing earthquake signals recorded by two local stations in Oklahoma.”
Plant Disease Detection and Classification by Deep Learning (2019, background): “However, in the second phase, state-of-the-art algorithms / architectures were developed for many applications including self-driving cars [9–11], healthcare sector [12–14], text recognition [6,15–17], earthquake predictions [18–20], marketing [21], ﬁnance [22,23], and image recognition [24–29].”
P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning (2018, methodology): “CNN architectures could also be used for earthquake detection (Perol et al., 2018).”
Generalized Seismic Phase Detection with Deep Learning (2018, methodology): “In seismology, deep learning has recently been used to detect and roughly locate earthquakes in Oklahoma [20], and to determine P-wave arrival times and ﬁrst-motion polarities [21].”