Seismic Event and Phase Detection Using Time–Frequency Representation and Convolutional Neural Networks

Abstract

Research Article| January 16, 2019 Seismic Event and Phase Detection Using Time–Frequency Representation and Convolutional Neural Networks Ramin M. H. Dokht; Ramin M. H. Dokht aPacific Geoscience Centre, Natural Resources Canada, Geological Survey of Canada, 9860 West Saanich Road, Sidney, British Columbia, Canada V8L 4B2, [e-mail] Search for other works by this author on: GSW Google Scholar Honn Kao; Honn Kao aPacific Geoscience Centre, Natural Resources Canada, Geological Survey of Canada, 9860 West Saanich Road, Sidney, British Columbia, Canada V8L 4B2, [e-mail] Search for other works by this author on: GSW Google Scholar Ryan Visser; Ryan Visser aPacific Geoscience Centre, Natural Resources Canada, Geological Survey of Canada, 9860 West Saanich Road, Sidney, British Columbia, Canada V8L 4B2, [e-mail] Search for other works by this author on: GSW Google Scholar Brindley Smith Brindley Smith aPacific Geoscience Centre, Natural Resources Canada, Geological Survey of Canada, 9860 West Saanich Road, Sidney, British Columbia, Canada V8L 4B2, [e-mail] Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (2A): 481–490. https://doi.org/10.1785/0220180308 Article history first online: 16 Jan 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Ramin M. H. Dokht, Honn Kao, Ryan Visser, Brindley Smith; Seismic Event and Phase Detection Using Time–Frequency Representation and Convolutional Neural Networks. Seismological Research Letters 2019;; 90 (2A): 481–490. doi: https://doi.org/10.1785/0220180308 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The availability of abundant digital seismic records and successful application of deep learning in pattern recognition and classification problems enable us to achieve a reliable earthquake detection framework. To overcome the limitations and challenges of conventional methods, which are mainly due to an incomplete set of template waveforms and low signal‐to‐noise ratio, we design a generalized model to improve discrimination between earthquake and noise recordings using a deep convolutional network (ConvNet). Exclusively based on a dataset of over 4900 earthquakes recorded over a period of 3 yrs in western Canada, a multilayer ConvNet is trained to learn general characteristics of background noise and earthquake signals in the time–frequency domain. In the next step, we train a secondary network using the wavelet transform of the major seismic arrivals to separate P from S waves and estimate their approximate arrival times. The results of validation experiments demonstrate promising performance and achieve an average accuracy of nearly 99% for both networks. To investigate the applicability of our algorithm, we apply the trained model on an independent dataset recently recorded in northeastern British Columbia (NE BC). It is found that deep‐learning‐based methods are superior to traditional techniques in detecting a higher number of seismic events at significantly less computational cost. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.