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Title: A comparison of earthquake backprojection imaging methods for dense local arrays

Backprojection imaging has recently become a practical method for local earthquake detection and location due to the deployment of densely sampled, continuously recorded, local seismograph arrays. While backprojection sometimes utilizes the full seismic waveform, the waveforms are often pre-processed and simplified to overcome imaging challenges. Real data issues include aliased station spacing, inadequate array aperture, inaccurate velocity model, low signal-to-noise ratio, large noise bursts and varying waveform polarity. We compare the performance of backprojection with four previously used data pre-processing methods: raw waveform, envelope, short-term averaging/long-term averaging and kurtosis. Our primary goal is to detect and locate events smaller than noise by stacking prior to detection to improve the signal-to-noise ratio. The objective is to identify an optimized strategy for automated imaging that is robust in the presence of real-data issues, has the lowest signal-to-noise thresholds for detection and for location, has the best spatial resolution of the source images, preserves magnitude, and considers computational cost. Imaging method performance is assessed using a real aftershock data set recorded by the dense AIDA array following the 2011 Virginia earthquake. Our comparisons show that raw-waveform backprojection provides the best spatial resolution, preserves magnitude and boosts signal to detect events smaller than noise,more » but is most sensitive to velocity error, polarity error and noise bursts. On the other hand, the other methods avoid polarity error and reduce sensitivity to velocity error, but sacrifice spatial resolution and cannot effectively reduce noise by stacking. Of these, only kurtosis is insensitive to large noise bursts while being as efficient as the raw-waveform method to lower the detection threshold; however, it does not preserve the magnitude information. For automatic detection and location of events in a large data set, we therefore recommend backprojecting kurtosis waveforms, followed by a second pass on the detected events using noise-filtered raw waveforms to achieve the best of all criteria.« less
Authors:
 [1] ;  [2] ;  [3] ;  [2] ;  [2] ;  [2] ;  [4] ;  [5] ;  [6]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
  3. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); CGG, Houston, TX (United States)
  4. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Oregon State Univ., Corvallis, OR (United States)
  5. Cornell Univ., Ithaca, NY (United States)
  6. Cornell Univ., Ithaca, NY (United States); Baylor Univ., Waco, TX (United States)
Publication Date:
Report Number(s):
SAND-2017-11407J
Journal ID: ISSN 0956-540X; 666482
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Geophysical Journal International
Additional Journal Information:
Journal Volume: 212; Journal Issue: 3; Journal ID: ISSN 0956-540X
Publisher:
Oxford University Press
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Body waves; Earthquake source observations; Seismicity and tectonics; Dynamics and mechanics of faulting
OSTI Identifier:
1465799

Beskardes, G. D., Hole, J. A., Wang, K., Michaelides, M., Wu, Q., Chapman, M. C., Davenport, K. K., Brown, L. D., and Quiros, D. A.. A comparison of earthquake backprojection imaging methods for dense local arrays. United States: N. p., Web. doi:10.1093/gji/ggx520.
Beskardes, G. D., Hole, J. A., Wang, K., Michaelides, M., Wu, Q., Chapman, M. C., Davenport, K. K., Brown, L. D., & Quiros, D. A.. A comparison of earthquake backprojection imaging methods for dense local arrays. United States. doi:10.1093/gji/ggx520.
Beskardes, G. D., Hole, J. A., Wang, K., Michaelides, M., Wu, Q., Chapman, M. C., Davenport, K. K., Brown, L. D., and Quiros, D. A.. 2017. "A comparison of earthquake backprojection imaging methods for dense local arrays". United States. doi:10.1093/gji/ggx520. https://www.osti.gov/servlets/purl/1465799.
@article{osti_1465799,
title = {A comparison of earthquake backprojection imaging methods for dense local arrays},
author = {Beskardes, G. D. and Hole, J. A. and Wang, K. and Michaelides, M. and Wu, Q. and Chapman, M. C. and Davenport, K. K. and Brown, L. D. and Quiros, D. A.},
abstractNote = {Backprojection imaging has recently become a practical method for local earthquake detection and location due to the deployment of densely sampled, continuously recorded, local seismograph arrays. While backprojection sometimes utilizes the full seismic waveform, the waveforms are often pre-processed and simplified to overcome imaging challenges. Real data issues include aliased station spacing, inadequate array aperture, inaccurate velocity model, low signal-to-noise ratio, large noise bursts and varying waveform polarity. We compare the performance of backprojection with four previously used data pre-processing methods: raw waveform, envelope, short-term averaging/long-term averaging and kurtosis. Our primary goal is to detect and locate events smaller than noise by stacking prior to detection to improve the signal-to-noise ratio. The objective is to identify an optimized strategy for automated imaging that is robust in the presence of real-data issues, has the lowest signal-to-noise thresholds for detection and for location, has the best spatial resolution of the source images, preserves magnitude, and considers computational cost. Imaging method performance is assessed using a real aftershock data set recorded by the dense AIDA array following the 2011 Virginia earthquake. Our comparisons show that raw-waveform backprojection provides the best spatial resolution, preserves magnitude and boosts signal to detect events smaller than noise, but is most sensitive to velocity error, polarity error and noise bursts. On the other hand, the other methods avoid polarity error and reduce sensitivity to velocity error, but sacrifice spatial resolution and cannot effectively reduce noise by stacking. Of these, only kurtosis is insensitive to large noise bursts while being as efficient as the raw-waveform method to lower the detection threshold; however, it does not preserve the magnitude information. For automatic detection and location of events in a large data set, we therefore recommend backprojecting kurtosis waveforms, followed by a second pass on the detected events using noise-filtered raw waveforms to achieve the best of all criteria.},
doi = {10.1093/gji/ggx520},
journal = {Geophysical Journal International},
number = 3,
volume = 212,
place = {United States},
year = {2017},
month = {12}
}