Automatic Arrival Time Detection for Earthquakes Based on Stacked Denoising Autoencoder

The accurate detection of P-wave arrival time is imperative for determining the hypocenter location of an earthquake. However, precise detection of onset time becomes more difficult when the signal-to-noise ratio (SNR) of the seismic data is low, such as during microearthquakes. In this letter, a stacked denoising autoencoder (SDAE) is proposed to smooth the background noise. The SDAE acts as a denoising filter for the seismic data. In the proposed algorithm, the SDAE is utilized to reduce background noise such that the onset time becomes more clear and sharp. Afterward, a hard decision with one threshold is used to detect the onset time of the event. The proposed algorithm is evaluated on both synthetic and field seismic data. As a result, the proposed algorithm outperforms the short-time average/long-time average and the Akaike information criterion algorithms. The proposed algorithm accurately picks the onset time of 94.1% for 407 field seismic waveforms with a standard deviation error of 0.10 s. In addition, the results indicate that the proposed algorithm can pick arrival times accurately for weak SNR seismic data with SNR higher than −14 dB.

[1]  M. Leonard,et al.  Multi-component autoregressive techniques for the analysis of seismograms , 1999 .

[2]  Chao Zhang,et al.  Automatic Time Picking for Microseismic Data Based on a Fuzzy C-Means Clustering Algorithm , 2016, IEEE Geoscience and Remote Sensing Letters.

[3]  Jiansi Yang,et al.  Development of an integrated onsite earthquake early warning system and test deployment in Zhaotong, China , 2013, Comput. Geosci..

[4]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[5]  Hongfeng Yang,et al.  Fault-Plane Determination of the 18 April 2008 Mount Carmel, Illinois, Earthquake by Detecting and Relocating Aftershocks , 2009 .

[6]  Tohru Kohda,et al.  Seismic noise study for accurate P-wave arrival detection via MODWT , 2013, Comput. Geosci..

[7]  B. Kennett,et al.  Traveltimes for global earthquake location and phase identification , 1991 .

[8]  Luis Rivera,et al.  A note on the dynamic and static displacements from a point source in multilayered media , 2002 .

[9]  R. V. Allen,et al.  Automatic earthquake recognition and timing from single traces , 1978, Bulletin of the Seismological Society of America.

[10]  Rahul Ramachandran,et al.  Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks , 2018 .

[11]  Luz García,et al.  A Deep Neural Networks Approach to Automatic Recognition Systems for Volcano-Seismic Events , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Rongjiang Wang,et al.  A simple orthonormalization method for stable and efficient computation of Green's functions , 1999, Bulletin of the Seismological Society of America.

[13]  Mohammed Bennamoun,et al.  Deep feature learning for dummies: A simple auto-encoder training method using Particle Swarm Optimisation , 2017, Pattern Recognit. Lett..

[14]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[15]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[16]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[17]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.