Aftershock Identification Using Diffusion Maps

Research Article| December 12, 2018 Aftershock Identification Using Diffusion Maps Yuri Bregman; Yuri Bregman aSoreq Nuclear Research Center, 81800 Yavne, Israel, yuri@soreq.gov.il Search for other works by this author on: GSW Google Scholar Neta Rabin Neta Rabin bAfeka ‐ Tel Aviv Academic College of Engineering, Mivtsa Kadesh Street 38, 6910717 Tel Aviv, Israel, netar@afeka.ac.ilneta.rabin@gmail.com Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (2A): 539–545. https://doi.org/10.1785/0220180291 Article history first online: 12 Dec 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Yuri Bregman, Neta Rabin; Aftershock Identification Using Diffusion Maps. Seismological Research Letters 2018;; 90 (2A): 539–545. doi: https://doi.org/10.1785/0220180291 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 aim of this article is to automatically identify repeating seismic events such as an aftershock sequence by utilizing a machine learning technique named diffusion maps. In previous work, the diffusion maps approach was applied for earthquake‐explosion discrimination and for characterizing explosions by their origin quarries. Diffusion maps, which is a nonlinear dimensionality reduction technique, constructs a low‐dimensional geometric representation of the seismograms. The embedding coordinates capture the intrinsic structure of the seismic signals and analysis is done in this low‐dimensional space. As a preprocessing step, the seismograms are converted to images in the time frequency domain. The approach is demonstrated on an aftershock sequence of the February 2004 Dead Sea earthquake with magnitude ML 5.2. In the first stage, the short‐term average/long‐term average (STA/LTA) detector is applied and then the diffusion maps‐based identification is performed. In a second example, a cross‐correlation detector is applied in the first stage and the proposed algorithm serves as a validation tool for the waveform correlation detector. The obtained results were confirmed by an analyst and compared with other methods. The experimental results demonstrate the potential and strength of the diffusion‐maps‐based approach, as the identification process can be carried out with no need of master templates for detecting new aftershocks. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.