An algorithmic framework for investigating the temporal relationship of magnetic field pulses and earthquakes applied to California

Abstract An end-to-end algorithm is described wherein field-collected magnetometer time series data were processed and analyzed for potential statistical correlation with pre-seismic activity. The process included windowing the data, extraction of statistically-determined anomalies via a short term average - long term average (STA-LTA) signal processing technique, collating and ranking the anomalous windows as precursory behavior, and testing the results via a Receiver Operating Characteristic (ROC) formulation. The algorithm was employed on a large dataset of over 100 magnetic observatories in California totaling hundreds of thousands of station-days. Using the ROC curve to evaluate its performance, this implementation of the algorithm obtained a 2.20 z-score. This number improved with the preliminary attempt at removing a severe cultural noise source. This work emphasizes an analytic framework more than parametric exploration or optimization, nevertheless there appears to be some suggestion of predictive power in the magnetic field time series.

[1]  Katsumi Hattori,et al.  Investigation of ULF Seismo-Magnetic Phenomena in Kanto, Japan During 2000–2010: Case Studies and Statistical Studies , 2013, Surveys in Geophysics.

[2]  Gary D. Egbert,et al.  Long‐term monitoring of ULF electromagnetic fields at Parkfield, California , 2010 .

[3]  Jiancang Zhuang,et al.  Statistical analysis of ULF seismomagnetic phenomena at Kakioka, Japan, during 2001–2010 , 2014 .

[4]  G. Egbert,et al.  DC trains and Pc3s: Source effects in mid‐latitude geomagnetic transfer functions , 2000 .

[5]  László Szarka,et al.  Geophysical aspects of man-made electromagnetic noise in the earth—A review , 1988 .

[6]  N. Graham,et al.  Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation , 2002 .

[7]  K. Kappler,et al.  A data variance technique for automated despiking of magnetotelluric data with a remote reference , 2011 .

[8]  Peter M. Shearer,et al.  Characterization of global seismograms using an automatic-picking algorithm , 1994, Bulletin of the Seismological Society of America.

[9]  M. Ladd,et al.  Low‐frequency magnetic field measurements near the epicenter of the Ms 7.1 Loma Prieta Earthquake , 1990 .

[10]  M. R. Saradjian,et al.  Electron and ion density variations before strong earthquakes (M>6.0) using DEMETER and GPS data , 2010 .

[11]  T. Bleier,et al.  Long term air ion monitoring in search of pre-earthquake signals , 2019, Journal of Atmospheric and Solar-Terrestrial Physics.

[12]  Michael C. Kelley,et al.  Apparent ionospheric total electron content variations prior to major earthquakes due to electric fields created by tectonic stresses , 2017 .

[13]  T. Bleier,et al.  Cross-validation of independent ultra-low-frequency magnetic recording systems for active fault studies , 2018, Earth, Planets and Space.

[14]  J. Bortnik,et al.  The possible statistical relation of Pc1 pulsations to Earthquake occurrence at low latitudes , 2007 .

[15]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[16]  C. Dunson,et al.  Investigation of ULF magnetic pulsations, air conductivity changes, and infra red signatures associated with the 30 October Alum Rock M5.4 earthquake , 2009 .

[17]  Paul A. Johnson,et al.  Continuous chatter of the Cascadia subduction zone revealed by machine learning , 2018, Nature Geoscience.

[18]  J. Douglas Zechar,et al.  Testing alarm‐based earthquake predictions , 2008 .

[19]  Francisco Martínez-Álvarez,et al.  Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure , 2017, Comput. Geosci..

[20]  Panayiotis A. Varotsos,et al.  Physical properties of the variations of the electric field of the earth preceding earthquakes, I , 1984 .

[21]  D. McPhee,et al.  Imaging the magmatic system of Mono Basin, California, with magnetotellurics in three dimensions , 2015 .

[22]  Katsumi Hattori,et al.  ULF Geomagnetic Changes Associated with Large Earthquakes , 2004 .

[23]  Francisco Martínez-Álvarez,et al.  Detecting precursory patterns to enhance earthquake prediction in Chile , 2015, Comput. Geosci..