Earthquake swarm activity revealed from high-resolution relative hypocenters — clustering of microearthquakes

Abstract Waveform similarity in an earthquake swarm enables precise arrival time differences of P- and S-waves through cross spectral methods to be obtained. A numerical simulation guarantees the time resolution of the order of 1 ms for the time differences. This precise arrival time difference is used to determine relative hypocenters of microearthquakes occurring in a swarm source region with a spatial resolution of the order of 10 m. This precise hypocenter determination (millisecond hypocenter determination) is applied to the data using temporary seismic observation in the Ashio region, Japan, where swarm activities are especially high. Relative hypocenters of 153 events are determined with high spatial resolution. It is revealed that the swarm activities consist of several clusters of events. A cluster is a group of events concentrated within a small area in a short time interval. The cluster activities in one swarm never overlap one another, sharing some part of the swarm source region of about 100 m−1 km in length. One cluster forms a fault-like planar area with a length of about 40–150 m. The event cluster/swarm activity would be a manifestation of the hierarchy structure of earthquake sources.

[1]  J. Pechmann,et al.  Constraints on relative earthquake locations from cross-correlation of waveforms , 1987 .

[2]  Keiiti Aki,et al.  Fault plane with barriers: A versatile earthquake model , 1977 .

[3]  Takashi Miyatake,et al.  Dynamical rupture process on a three‐dimensional fault with non‐uniform frictions and near‐field seismic waves , 1978 .

[4]  R. Matsu’ura.  DETAILED STUDY OF THE EARTHQUAKE SEQUENCE IN 1980 OFF THE EAST COAST OF THE IZU PENINSULA, JAPAN , 1983 .

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

[6]  D. L. Anderson,et al.  Theoretical Basis of Some Empirical Relations in Seismology by Hiroo Kanamori And , 1975 .

[7]  Yosio Nakamura,et al.  A1 moonquakes - Source distribution and mechanism , 1978 .

[8]  A. Dziewoński,et al.  Centroid-moment tensor solutions for April–June, 1984 , 1985 .

[9]  E. Engdahl.  Documentation of earthquake algorithms , 1984 .

[10]  Keiiti Aki,et al.  Asperities, barriers, characteristic earthquakes and strong motion prediction , 1984 .

[11]  D. G. Watts,et al.  Spectral analysis and its applications , 1968 .

[12]  A. Ito,et al.  HIGH RESOLUTION RELATIVE HYPOCENTERS OF SIMILAR EARTHQUAKES BY CROSS-SPECTRAL ANALYSIS METHOD , 1985 .

[13]  W. Spence,et al.  Relative epicenter determination using P-wave arrival-time differences , 1980 .

[14]  M. Yamada,et al.  A Real-Time Detection and Location of Aftershocks of the 1984 Western Nagano Prefecture Earthquake by using Personal Computer , 1985 .

[15]  P. Spudich,et al.  Studies of the seismic coda using an earthquake cluster as a deeply buried seismograph array , 1987 .

[16]  M. Furumoto,et al.  Hierarchy in earthquake size distribution , 1985 .

[17]  D. Andrews.  A stochastic fault model: 2. Time‐dependent case , 1981 .

[18]  Stephen D. Malone,et al.  High precision relative locations of earthquakes at Mount St. Helens, Washington , 1987 .

[19]  A. DOUGLAS,et al.  Joint Epicentre Determination , 1967, Nature.

[20]  J. Koyama.  Earthquake source time-function from coherent and incoherent rupture , 1985 .

[21]  Vertical seismic profiling of Oroville microearthquakes: Velocity spectra and particle motion as a function of depth , 1988 .