Detection of weak seismic signals in noisy environments from unfiltered, continuous passive seismic recordings

Research output: Contribution to journalSpecial issue

Abstract

Robust event detection of low signal-to-noise ratio (SNR) events, such as those characterized as induced or triggered seismicity, remains a challenge. The reason is the relatively small magnitude of the events (usually less than 2 or 3 in Richter scale) and the fact that regional permanent seismic networks can only record the strongest events of a microseismic sequence. Monitoring using temporary installed short-period arrays can fill the gap of missed seismicity but the challenge of detecting weak events in long, continuous records is still present. Further, for low SNR recordings, commonly applied detection algorithms generally require pre-filtering of the data based on a priori knowledge of the background noise. Such knowledge is often not available.
We present the NpD (Non-parametric Detection) algorithm, an automated algorithm which detects potential events without the requirement for pre-filtering. Events are detected by calculating the energy contained within small individual time segments of a recording and comparing it to the energy contained within a longer surrounding time window. If the excess energy exceeds a given threshold criterion, which is determined dynamically based on the background noise for that window, then an event is detected. For each time window, to characterize background noise the algorithm uses non-parametric statistics to describe the upper bound of the spectral amplitude. Our approach does not require an assumption of normality within the recordings and hence it is applicable to all datasets.
We compare our NpD algorithm with the commonly commercially applied STA/LTA algorithm and another highly efficient algorithm based on Power Spectral Density using a challenging microseismic dataset with poor SNR. For event detection, the NpD algorithm significantly outperforms the STA/LTA and PSD algorithms tested, maximizing the number of detected events whilst minimizing the number of false positives.
LanguageEnglish
Pages2993-3004
Number of pages12
JournalBulletin of the Seismological Society of America
Volume108
Issue number5
Early online date8 May 2018
DOIs
Publication statusPublished - 1 Nov 2018

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recording
background noise
signal-to-noise ratio
Signal to noise ratio
signal to noise ratios
seismicity
energy
detection
normality
Power spectral density
Statistics
statistics
Monitoring
requirements
thresholds
monitoring

Keywords

  • seismic events
  • signal-to-noise ratio
  • Richter scale
  • microseismic monitoring

Cite this

@article{a0b9f4c3402b4398ace8106124672435,
title = "Detection of weak seismic signals in noisy environments from unfiltered, continuous passive seismic recordings",
abstract = "Robust event detection of low signal-to-noise ratio (SNR) events, such as those characterized as induced or triggered seismicity, remains a challenge. The reason is the relatively small magnitude of the events (usually less than 2 or 3 in Richter scale) and the fact that regional permanent seismic networks can only record the strongest events of a microseismic sequence. Monitoring using temporary installed short-period arrays can fill the gap of missed seismicity but the challenge of detecting weak events in long, continuous records is still present. Further, for low SNR recordings, commonly applied detection algorithms generally require pre-filtering of the data based on a priori knowledge of the background noise. Such knowledge is often not available. We present the NpD (Non-parametric Detection) algorithm, an automated algorithm which detects potential events without the requirement for pre-filtering. Events are detected by calculating the energy contained within small individual time segments of a recording and comparing it to the energy contained within a longer surrounding time window. If the excess energy exceeds a given threshold criterion, which is determined dynamically based on the background noise for that window, then an event is detected. For each time window, to characterize background noise the algorithm uses non-parametric statistics to describe the upper bound of the spectral amplitude. Our approach does not require an assumption of normality within the recordings and hence it is applicable to all datasets.We compare our NpD algorithm with the commonly commercially applied STA/LTA algorithm and another highly efficient algorithm based on Power Spectral Density using a challenging microseismic dataset with poor SNR. For event detection, the NpD algorithm significantly outperforms the STA/LTA and PSD algorithms tested, maximizing the number of detected events whilst minimizing the number of false positives.",
keywords = "seismic events, signal-to-noise ratio, Richter scale, microseismic monitoring",
author = "M. Kinali and S. Pytharouli and Lunn, {R. J.} and Shipton, {Z. K.} and M. Stillings and R. Lord and S. Thompson",
note = "Manuscript includes supplementary information.",
year = "2018",
month = "11",
day = "1",
doi = "10.1785/0120170358",
language = "English",
volume = "108",
pages = "2993--3004",
journal = "Bulletin of the Seismological Society of America",
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}

TY - JOUR

T1 - Detection of weak seismic signals in noisy environments from unfiltered, continuous passive seismic recordings

AU - Kinali, M.

AU - Pytharouli, S.

AU - Lunn, R. J.

AU - Shipton, Z. K.

AU - Stillings, M.

AU - Lord, R.

AU - Thompson, S.

N1 - Manuscript includes supplementary information.

PY - 2018/11/1

Y1 - 2018/11/1

N2 - Robust event detection of low signal-to-noise ratio (SNR) events, such as those characterized as induced or triggered seismicity, remains a challenge. The reason is the relatively small magnitude of the events (usually less than 2 or 3 in Richter scale) and the fact that regional permanent seismic networks can only record the strongest events of a microseismic sequence. Monitoring using temporary installed short-period arrays can fill the gap of missed seismicity but the challenge of detecting weak events in long, continuous records is still present. Further, for low SNR recordings, commonly applied detection algorithms generally require pre-filtering of the data based on a priori knowledge of the background noise. Such knowledge is often not available. We present the NpD (Non-parametric Detection) algorithm, an automated algorithm which detects potential events without the requirement for pre-filtering. Events are detected by calculating the energy contained within small individual time segments of a recording and comparing it to the energy contained within a longer surrounding time window. If the excess energy exceeds a given threshold criterion, which is determined dynamically based on the background noise for that window, then an event is detected. For each time window, to characterize background noise the algorithm uses non-parametric statistics to describe the upper bound of the spectral amplitude. Our approach does not require an assumption of normality within the recordings and hence it is applicable to all datasets.We compare our NpD algorithm with the commonly commercially applied STA/LTA algorithm and another highly efficient algorithm based on Power Spectral Density using a challenging microseismic dataset with poor SNR. For event detection, the NpD algorithm significantly outperforms the STA/LTA and PSD algorithms tested, maximizing the number of detected events whilst minimizing the number of false positives.

AB - Robust event detection of low signal-to-noise ratio (SNR) events, such as those characterized as induced or triggered seismicity, remains a challenge. The reason is the relatively small magnitude of the events (usually less than 2 or 3 in Richter scale) and the fact that regional permanent seismic networks can only record the strongest events of a microseismic sequence. Monitoring using temporary installed short-period arrays can fill the gap of missed seismicity but the challenge of detecting weak events in long, continuous records is still present. Further, for low SNR recordings, commonly applied detection algorithms generally require pre-filtering of the data based on a priori knowledge of the background noise. Such knowledge is often not available. We present the NpD (Non-parametric Detection) algorithm, an automated algorithm which detects potential events without the requirement for pre-filtering. Events are detected by calculating the energy contained within small individual time segments of a recording and comparing it to the energy contained within a longer surrounding time window. If the excess energy exceeds a given threshold criterion, which is determined dynamically based on the background noise for that window, then an event is detected. For each time window, to characterize background noise the algorithm uses non-parametric statistics to describe the upper bound of the spectral amplitude. Our approach does not require an assumption of normality within the recordings and hence it is applicable to all datasets.We compare our NpD algorithm with the commonly commercially applied STA/LTA algorithm and another highly efficient algorithm based on Power Spectral Density using a challenging microseismic dataset with poor SNR. For event detection, the NpD algorithm significantly outperforms the STA/LTA and PSD algorithms tested, maximizing the number of detected events whilst minimizing the number of false positives.

KW - seismic events

KW - signal-to-noise ratio

KW - Richter scale

KW - microseismic monitoring

UR - https://pubs.geoscienceworld.org/bssa

U2 - 10.1785/0120170358

DO - 10.1785/0120170358

M3 - Special issue

VL - 108

SP - 2993

EP - 3004

JO - Bulletin of the Seismological Society of America

T2 - Bulletin of the Seismological Society of America

JF - Bulletin of the Seismological Society of America

SN - 0037-1106

IS - 5

ER -