Seismic network detection probability assessment using waveforms and accounting to event association logic

Vladimir Pinsky; Avi Shapira

doi:10.1007/s10950-016-9583-7

Seismic network detection probability assessment using waveforms and accounting to event association logic

Vladimir Pinsky, Avi Shapira

Research output: Contribution to journal › Article › peer-review

Abstract

The geographical area where a seismic event of magnitude M ≥ M_t is detected by a seismic station network, for a defined probability is derived from a station probability of detection estimated as a function of epicentral distance. The latter is determined from both the bulletin data and the waveforms recorded by the station during the occurrence of the event with and without band-pass filtering. For simulating the real detection process, the waveforms are processed using the conventional Carl Johnson detection and association algorithm. The attempt is presented to account for the association time criterion in addition to the conventional approach adopted by the known PMC method.

Original language	English
Pages (from-to)	69-82
Number of pages	14
Journal	Journal of Seismology
Volume	21
Issue number	1
DOIs	https://doi.org/10.1007/s10950-016-9583-7
State	Published - 1 Jan 2017
Externally published	Yes

Bibliographical note

Funding Information:
This research was supported by the Israel Ministry of Infrastructures and Israel Ministry of Absorption and Immigration through the KAMEA Science Foundation. We thank our colleagues from the Seismological division of the Geophysical Institute of Israel for discussions of the research, particularly Mr. Andrey Polozov and Mrs. Veronic Avirav, who provided expertise on the ISN detection algorithm and Mr. Dov Zakosky for administrative support of the work. We are grateful to the anonymous reviewers for valuable comments that greatly improved the manuscript.

Publisher Copyright:
© 2016, Springer Science+Business Media Dordrecht.

Keywords

Carl Johnson detection and association algorithm
Detection probability of individual station
Epicentral distance
Magnitude
PMC method
Probability of completeness magnitude
Probability of seismic event detection
Seismic bulletin, waveforms
Seismic network detectability

ASJC Scopus subject areas

Geophysics
Geochemistry and Petrology

Access to Document

10.1007/s10950-016-9583-7

Cite this

@article{5cc8cada2cd744cb837ddf98d91c85ad,

title = "Seismic network detection probability assessment using waveforms and accounting to event association logic",

abstract = "The geographical area where a seismic event of magnitude M ≥ Mt is detected by a seismic station network, for a defined probability is derived from a station probability of detection estimated as a function of epicentral distance. The latter is determined from both the bulletin data and the waveforms recorded by the station during the occurrence of the event with and without band-pass filtering. For simulating the real detection process, the waveforms are processed using the conventional Carl Johnson detection and association algorithm. The attempt is presented to account for the association time criterion in addition to the conventional approach adopted by the known PMC method.",

keywords = "Carl Johnson detection and association algorithm, Detection probability of individual station, Epicentral distance, Magnitude, PMC method, Probability of completeness magnitude, Probability of seismic event detection, Seismic bulletin, waveforms, Seismic network detectability",

author = "Vladimir Pinsky and Avi Shapira",

note = "Funding Information: This research was supported by the Israel Ministry of Infrastructures and Israel Ministry of Absorption and Immigration through the KAMEA Science Foundation. We thank our colleagues from the Seismological division of the Geophysical Institute of Israel for discussions of the research, particularly Mr. Andrey Polozov and Mrs. Veronic Avirav, who provided expertise on the ISN detection algorithm and Mr. Dov Zakosky for administrative support of the work. We are grateful to the anonymous reviewers for valuable comments that greatly improved the manuscript. Publisher Copyright: {\textcopyright} 2016, Springer Science+Business Media Dordrecht.",

year = "2017",

month = jan,

day = "1",

doi = "10.1007/s10950-016-9583-7",

language = "English",

volume = "21",

pages = "69--82",

journal = "Journal of Seismology",

issn = "1383-4649",

publisher = "Springer Netherlands",

number = "1",

}

TY - JOUR

T1 - Seismic network detection probability assessment using waveforms and accounting to event association logic

AU - Pinsky, Vladimir

AU - Shapira, Avi

N1 - Funding Information: This research was supported by the Israel Ministry of Infrastructures and Israel Ministry of Absorption and Immigration through the KAMEA Science Foundation. We thank our colleagues from the Seismological division of the Geophysical Institute of Israel for discussions of the research, particularly Mr. Andrey Polozov and Mrs. Veronic Avirav, who provided expertise on the ISN detection algorithm and Mr. Dov Zakosky for administrative support of the work. We are grateful to the anonymous reviewers for valuable comments that greatly improved the manuscript. Publisher Copyright: © 2016, Springer Science+Business Media Dordrecht.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - The geographical area where a seismic event of magnitude M ≥ Mt is detected by a seismic station network, for a defined probability is derived from a station probability of detection estimated as a function of epicentral distance. The latter is determined from both the bulletin data and the waveforms recorded by the station during the occurrence of the event with and without band-pass filtering. For simulating the real detection process, the waveforms are processed using the conventional Carl Johnson detection and association algorithm. The attempt is presented to account for the association time criterion in addition to the conventional approach adopted by the known PMC method.

AB - The geographical area where a seismic event of magnitude M ≥ Mt is detected by a seismic station network, for a defined probability is derived from a station probability of detection estimated as a function of epicentral distance. The latter is determined from both the bulletin data and the waveforms recorded by the station during the occurrence of the event with and without band-pass filtering. For simulating the real detection process, the waveforms are processed using the conventional Carl Johnson detection and association algorithm. The attempt is presented to account for the association time criterion in addition to the conventional approach adopted by the known PMC method.

KW - Carl Johnson detection and association algorithm

KW - Detection probability of individual station

KW - Epicentral distance

KW - Magnitude

KW - PMC method

KW - Probability of completeness magnitude

KW - Probability of seismic event detection

KW - Seismic bulletin, waveforms

KW - Seismic network detectability

UR - http://www.scopus.com/inward/record.url?scp=84968665472&partnerID=8YFLogxK

U2 - 10.1007/s10950-016-9583-7

DO - 10.1007/s10950-016-9583-7

M3 - Article

AN - SCOPUS:84968665472

SN - 1383-4649

VL - 21

SP - 69

EP - 82

JO - Journal of Seismology

JF - Journal of Seismology

IS - 1

ER -

Seismic network detection probability assessment using waveforms and accounting to event association logic

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this