EQcorrscan: Repeating and Near‐Repeating Earthquake Detection and Analysis in Python

Chamberlain, C. J.; Hopp, Chet; Boese, C. M.; Warren‐Smith, Emily; Chambers, Derrick; Chu, Steve; Michailos, Konstantinos; Townend, John

doi:10.1785/0220170151

Cited by 132 publications

(142 citation statements)

References 23 publications

Supporting

Mentioning

142

Contrasting

Order By: Relevance

“…This method is best used to generate a preliminary set of detections which could be used as real templates in a subsequent matched‐filter run to generate a final catalog. Projects like EQcorrscan (Chamberlain et al, ) and FastMatchedFilter (Beaucé et al, ), which leverage parallel architectures, enable the large grids required to cover source regions to be constructed and used to detect earthquakes faster than real time.…”

Section: Discussionmentioning

confidence: 99%

“…Templates are correlated with continuous seismic data using the EQcorrscan package (Chamberlain et al, ). Data are filtered using a fourth‐order Butterworth bandpass filter and resampled in the frequency domain.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, we remove events within 20 s of another higher‐correlating detection. Using EQcorrscan (Chamberlain et al, ) allows us to run the correlations in less than 15 min using 40 CPU threads. In other words, it would be possible using moderate computing resources to run large template sets faster than real time.…”

Section: Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Real Earthquakes Using Artificial Earthquakes: On the Use of Synthetic Waveforms in Matched‐Filter Earthquake Detection

Chamberlain

Townend

2018

Geophysical Research Letters

View full text Add to dashboard Cite

Matched‐filters are an increasingly popular tool for earthquake detection, but their reliance on a priori knowledge of the targets of interest limits their application to regions with previously documented seismicity. We explore an extension to the matched‐filter method to detect earthquakes and low‐frequency earthquakes on local to regional scales. We show that it is possible to increase the number of detections compared with standard energy‐based methods, with low false‐detection rates, using suites of synthetic waveforms as templates. We apply this to a microearthquake swarm and an aftershock sequence, and to detect low‐frequency earthquakes. We also explore the sensitivity of detections to the synthetic source's location and focal mechanism. Source‐receiver geometry has a first‐order control on how sensitive matched‐filter detectors are to variations in source location and focal mechanism, and this likely applies to detections made using both synthetic and real templates.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Real Earthquakes Using Artificial Earthquakes: On the Use of Synthetic Waveforms in Matched‐Filter Earthquake Detection

Chamberlain

Townend

2018

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…The following equation, which we use for this study, describes normalized cross-correlation in the time domain (Chamberlain et al, 2017) The following equation, which we use for this study, describes normalized cross-correlation in the time domain (Chamberlain et al, 2017)…”

Section: Matched-filter Detectionmentioning

confidence: 99%

“…The analysis reported here made use of the ObsPy seismic processing toolbox (Team, 2016), and the matched-filter detection was conducted using the EQcorrscan package (Chamberlain et al, 2017;Chamberlain & Hopp, 2016) which can be freely downloaded and installed via PyPI or Anaconda on all major platforms. 2 Trust and Mercury NZ Limited) for the funding to conduct this research and for allowing us access to the data and permission to publish our findings.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Seismic Response to Injection Well Stimulation in a High‐Temperature, High‐Permeability Reservoir

Hopp

Sewell

Mroczek

et al. 2019

Geochem Geophys Geosyst

Self Cite

View full text Add to dashboard Cite

Fluid injection into the Earth's crust can induce seismic events that cause damage to local infrastructure but also offer valuable insight into seismogenesis. The factors that influence the magnitude, location, and number of induced events remain poorly understood but include injection flow rate and pressure as well as reservoir temperature and permeability. The relationship between injection parameters and injection‐induced seismicity in high‐temperature, high‐permeability reservoirs has not been extensively studied. Here we focus on the Ngatamariki geothermal field in the central Taupō Volcanic Zone, New Zealand, where three stimulation/injection tests have occurred since 2012. We present a catalog of seismicity from 2012 to 2015 created using a matched‐filter detection technique. We analyze the stress state in the reservoir during the injection tests from first motion‐derived focal mechanisms, yielding an average direction of maximum horizontal compressive stress (SHmax) consistent with the regional NE‐SW trend. However, there is significant variation in the direction of maximum compressive stress (σ1), which may reflect geological differences between wells. We use the ratio of injection flow rate to overpressure, referred to as injectivity index, as a proxy for near‐well permeability and compare changes in injectivity index to spatiotemporal characteristics of seismicity accompanying each test. Observed increases in injectivity index are generally poorly correlated with seismicity, suggesting that the locations of microearthquakes are not coincident with the zone of stimulation (i.e., increased permeability). Our findings augment a growing body of work suggesting that aseismic opening or slip, rather than seismic shear, is the active process driving well stimulation in many environments.

show abstract

Tidal Triggering of Microseismicity at the Equatorial Mid-Atlantic Ridge, Inferred from OBS Network

Leptokaropoulos¹,

Harmon²,

Hicks³

et al. 2021

Preprint

View full text Add to dashboard Cite

EQcorrscan: Repeating and Near‐Repeating Earthquake Detection and Analysis in Python

Cited by 132 publications

References 23 publications

Detecting Real Earthquakes Using Artificial Earthquakes: On the Use of Synthetic Waveforms in Matched‐Filter Earthquake Detection

Detecting Real Earthquakes Using Artificial Earthquakes: On the Use of Synthetic Waveforms in Matched‐Filter Earthquake Detection

Seismic Response to Injection Well Stimulation in a High‐Temperature, High‐Permeability Reservoir

Tidal Triggering of Microseismicity at the Equatorial Mid-Atlantic Ridge, Inferred from OBS Network

Contact Info

Product

Resources

About