Continuous Kurtosis‐Based Migration for Seismic Event Detection and Location, with Application to Piton de la Fournaise Volcano, La Réunion

Abstract We present an automatic earthquake detection and location technique based on migration of continuous waveform data. Data are preprocessed using a kurtosis estimator in order to enhance the first arrival information, then migrated onto a predefined search grid using precalculated P ‐wave travel times, and finally stacked. Local maxima in the resulting 4D space–time grid indicate the locations and origin times of seismic events. We applied our technique to earthquake swarms occurring on Piton de la Fournaise volcano, La Reunion, France. We located 5000 events from 12 different swarms that occurred between 2009 and 2011. Our automated locations are consistent with those performed using manual picks and indicate that the seismicity concentrates around sea level. Multiplet analysis of the detected events and subsequent double‐difference relocation produce sharper images of the earthquake swarms. Our code, Waveloc, is released in open source. Online Material: Figures of seismicity distributions from Waveloc, synthetic test, and stack amplitude values versus magnitudes.

[1]  V. Oye,et al.  Automated microearthquake location using envelope stacking and robust global optimization , 2010 .

[2]  E. Tessmer,et al.  Reverse modelling for seismic event characterization , 2005 .

[3]  P. Podvin,et al.  Finite difference computation of traveltimes in very contrasted velocity models: a massively parallel approach and its associated tools , 1991 .

[4]  Thomas Meier,et al.  Automated determination of P-phase arrival times at regional and local distances using higher order statistics , 2010 .

[5]  W. H. Lee,et al.  A method of estimating magnitude of local earthquakes from signal duration , 1972 .

[6]  Winfried Hanka,et al.  Real-time earthquake monitoring for tsunami warning in the Indian Ocean and beyond , 2010 .

[7]  F. Scherbaum,et al.  Acoustic imaging of earthquake sources from the Chalfant Valley, 1986, aftershock series , 1994 .

[8]  R. V. Allen,et al.  Automatic phase pickers: Their present use and future prospects , 1982 .

[9]  F. Waldhauser,et al.  A Double-Difference Earthquake Location Algorithm: Method and Application to the Northern Hayward Fault, California , 2000 .

[11]  William L. Ellsworth,et al.  Monitoring velocity variations in the crust using earthquake doublets: An application to the Calaveras Fault, California , 1984 .

[12]  George A. McMechan,et al.  Imaging of earthquake sources in Long Valley Caldera, California, 1983 , 1985 .

[13]  A. G. Lindh,et al.  Robust regional phase association , 1997 .

[14]  Aline Peltier,et al.  First Results from the UnderVolc High Resolution Seismic and GPS Network Deployed on Piton de la Fournaise Volcano , 2012 .

[15]  Felix Waldhauser,et al.  Waveform Cross-Correlation-Based Differential Travel-Time Measurements at the Northern California Seismic Network , 2005 .

[16]  Mathias Fink,et al.  Time‐reversal imaging of seismic sources and application to the great Sumatra earthquake , 2006 .

[17]  Christopher John Young,et al.  A comparison of select trigger algorithms for automated global seismic phase and event detection , 1998, Bulletin of the Seismological Society of America.

[18]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[19]  H. Kao,et al.  Delineating complex spatiotemporal distribution of earthquake aftershocks: an improved Source-Scanning Algorithm , 2012 .

[20]  Robert Granat,et al.  Real-time Earthquake Location Using Kirchhoff Reconstruction , 2005 .

[21]  A. Lomax,et al.  The effect of velocity structure errors on double‐difference earthquake location , 2004 .

[22]  J. Antoni.  Fast computation of the kurtogram for the detection of transient faults , 2007 .

[23]  W. K. Lee,et al.  Principles and applications of microearthquake networks , 1981 .

[24]  Göran Ekström,et al.  Global Detection and Location of Seismic Sources by Using Surface Waves , 2006 .

[25]  H. Kao,et al.  The Source‐Scanning Algorithm: mapping the distribution of seismic sources in time and space , 2004 .

[26]  J. Got,et al.  P-wave velocity structure of Piton de la Fournaise volcano deduced from seismic data recorded between 1996 and 1999 , 2009 .

[27]  W. H. Lee,et al.  HYPO71 (revised; a computer program for determining hypocenter, magnitude, and first motion pattern of local earthquakes , 1972 .

[28]  Stavros M. Panas,et al.  PAI-S/K: A robust automatic seismic P phase arrival identification scheme , 2002, IEEE Trans. Geosci. Remote. Sens..

[29]  W. B. Joyner,et al.  The ML scale in central California , 1984 .

[30]  Peter M. Shearer,et al.  Global seismic event detection using a matched filter on long‐period seismograms , 1994 .

[31]  Carl Tape,et al.  An automated time-window selection algorithm for seismic tomography , 2009 .

[32]  Alberto Michelini,et al.  Automatic picking of P and S phases using a neural tree , 2006 .

[33]  Christopher John Young,et al.  An automated local and regional seismic event detection and location system using waveform correlation , 1999 .

[34]  Agathe Schmid.  Quelle prédictibilité pour les éruptions volcaniques ? De l'échelle mondiale au Piton de la Fournaise , 2011 .