Convolutional neural network for earthquake detection and location

@article{Perol2017ConvolutionalNN,
  title={Convolutional neural network for earthquake detection and location},
  author={Thibaut Perol and Micha{\"e}l Gharbi and Marine A. Denolle},
  journal={Science Advances},
  year={2017},
  volume={4}
}
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… 
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452 Citations
Highly Influential Citations
29
Background Citations
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Methods Citations
102
Results Citations
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452 Citations

Deep Neural Network of Earthquake Signal Identification using Stridenet

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A novel framework for reliable automatic classification of earthquake magnitudes is proposed, based on a convolutional recurrent neural network in which a new approach for feature extraction using Log-Mel spectrogram representations is applied for seismic signals.

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Location of earthquakes is a primary task in seismology and microseismic monitoring, essential for almost any further analysis. Earthquake hypocenters can be determined by the inversion of arrival…

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A multi-tasking deep learning model – the C onvolutional R ecurrent model for E arthquake I dentification and M agnitude E stimation (CREIME) that detects the first earthquake signal, from background seismic noise, determines the P-arrival time as well as estimates the magnitude using the raw of P-wave data compared to the previous studies.

Small Seismic Events in Oklahoma Detected and Located by Machine Learning–Based Models

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