Continuous Kurtosis-Based Migration for Seismic Event Detection and Location, with Application to Piton de la Fournaise Volcano, La Reunion

Langet, Nadège; Maggi, Alessia; Michelini, Alberto; Brenguier, Florent

doi:10.1785/0120130107

Cited by 82 publications

(54 citation statements)

References 34 publications

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“…For sources for which the P-wave arrivals are not sufficiently similar, we use kurtosis with its positive derivative (Langet et al, 2014) to obtain time series that are peaked at the P-wave onsets. These peaks are highly similar for the different events and thus lead to accurate time differences after cross-correlation.…”

Section: Fig 8 Estimation Of Average P-and S-wave Head-wave Velocitmentioning

confidence: 99%

Relocation of clustered earthquakes in the Groningen gas field

Jagt

Ruigrok

Paulssen

2017

Netherlands Journal of Geosciences

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Previous locations of earthquakes induced by depletion of the Groningen gas field were not accurate enough to infer which faults in the reservoir are reactivated. A multiplet analysis is performed to identify clusters of earthquakes that have similar waveforms, representing repeating rupture on the same or nearby faults. The multiplet analysis is based on the cross-correlation of seismograms to assess the degree of similarity. Using data of a single station, six earthquake clusters within the limits of the Groningen field were identified for the period 2010 to mid-2014. Four of these clusters were suitable for a relocation method that is based on the difference in travel time between the P-and the S-wave. Events within a cluster can be relocated relative to a master event with improved accuracy by cross-correlating first arrivals. By choosing master events located with a new dense seismic network, the relocated events likely not only have better relative, but also improved absolute locations. For a few clusters with sufficient signal-to-noise detections, we show that the relocation method is successful in assigning clusters to specific faults at the reservoir level. Overall, about 90% of the events did not show clustering, despite choosing low correlation thresholds of 0.5 and 0.6. This suggests that different faults and/or fault segments with likely varying source mechanisms are active in reservoir sub-regions of a few square kilometres.

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Section: Fig 8 Estimation Of Average P-and S-wave Head-wave Velocitmentioning

confidence: 99%

Relocation of clustered earthquakes in the Groningen gas field

Jagt

Ruigrok

Paulssen

2017

Netherlands Journal of Geosciences

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“…; Langet et al . ). To address this issue, we applied the successive transformations proposed by Baillard et al .…”

Section: Methodsmentioning

confidence: 97%

“…The very beginning of this increase matches well the human-operator picking (Baillard et al 2014). The onset time of the considered phase can be determined using the maximum CF k derivative (Lois et al 2013;Langet et al 2014). To address this issue, we applied the successive transformations proposed by Baillard et al (2014) to CF k followed by smoothing; in a similar fashion as in MNW method, we can also apply low pass filtering to CF k by using the same function and the smoothing parameters.…”

Section: Higher Order Statistics Methodsmentioning

confidence: 99%

Development of an adaptive multi‐method algorithm for automatic picking of first arrival times: application to near surface seismic data

Khalaf

Camerlynck

Florsch

et al. 2018

Near Surface Geophysics

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Accurate picking of first‐arrival times is important in many seismic studies, particularly in seismic tomography and reservoirs or aquifers monitoring. Many techniques have been developed, mainly for seismological purposes, in order to pick first arrivals automatically or semi‐automatically. However, these techniques do not reach the accuracy required in shallow seismics due to the complexity of near‐surface structures and low signal‐to‐noise ratio. We propose here a new adaptive algorithm to automatically pick first arrival in near‐surface seismic data by combining three picking methods: multi‐nested windows, higher order statistics, and Akaike information criterion. They benefit from combining different properties of the signal in order to highlight first arrivals and finally to provide an efficient and robust automatic picking. This strategy mimics the human first‐break picking, where a global trend is first defined at the beginning of the picking procedure. The exact first breaks are then sought in the vicinity of each point suggested by this trend. Three successive phases are combined in a multistage algorithm, each of them characterizing a specific signal property. Within each phase, the potential picks and their error range are automatically assessed and sequentially used as prior constraints in the following phase picking. Since having realistic estimates of the error in picked traveltimes is crucial for seismic tomography, our adaptive algorithm automatically provides picked arrival times with their associated uncertainties. We demonstrate the accuracy and robustness of the implemented algorithm using synthetic, pseudo‐synthetic and real datasets that pose challenges to classical automatic pickers. A comparison of both manual and adaptive picking procedures demonstrates that our new scheme provides more reliable results even under different noisy conditions. All parameters of our multi‐method algorithm are self‐adaptive, thanks to the sequential integration of each sub‐algorithm results in the workflow. Hence, it is nearly a parameter‐free algorithm, which is straightforward to implement and demands low computational resources.

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“…Besides the imaging operator, the PWS methods also differ in preprocessing of the input waveforms and the event detection and location detection criteria. There are various CFs adopted to improve the SNR and compensate the side effects of source radiation patterns; for example, the input data are converted to envelope (Gharti et al, ; Liao et al, ; Zeng et al, ); semblance (Furumoto et al, ; W. Zhang & Zhang, ; Staněk et al, ; C. Zhang et al, ); short‐term average to long‐term average ratio (STA/LTA; Drew et al, ; Grigoli et al, ; L. Li et al, ); kurtosis (Langet et al, ; Poiata et al, ); multichannel coherency (Shi, Angus, et al, ). …”

Section: Methodologiesmentioning

confidence: 99%

“…The largest intersection spacing between stations is about 40 km. Four different CFs, that is, envelope (Kao & Shan, ), STA/LTA (Drew et al, ), kurtosis (Langet et al, ), and Pearson correlation coefficients (Shi, Angus, et al, ), are compared using the same example of volcanic‐tectonic event. Figure b shows the migration profiles of the four different migration methods; the catalog location of the example event is displayed as the red star in Figure a.…”

Section: Applicationsmentioning

confidence: 99%

Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

Tan

Schwarz

et al. 2020

Reviews of Geophysics

132

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Source locations provide fundamental information on earthquakes and lay the foundation for seismic monitoring at all scales. Seismic source location as a classical inverse problem has experienced significant methodological progress during the past century. Unlike the conventional traveltime‐based location methods that mainly utilize kinematic information, a new category of waveform‐based methods, including partial waveform stacking, time reverse imaging, wavefront tomography, and full waveform inversion, adapted from migration or stacking techniques in exploration seismology has emerged. Waveform‐based methods have shown promising results in characterizing weak seismic events at multiple scales, especially for abundant microearthquakes induced by hydraulic fracturing in unconventional and geothermal reservoirs or foreshock and aftershock activity potentially preceding tectonic earthquakes. This review presents a comprehensive summary of the current status of waveform‐based location methods, through elaboration of the methodological principles, categorization, and connections, as well as illustration of the applications to natural and induced/triggered seismicity, ranging from laboratory acoustic emission to field hydraulic fracturing‐induced seismicity, regional tectonic, and volcanic earthquakes. Taking into account recent developments in instrumentation and the increasing availability of more powerful computational resources, we highlight recent accomplishments and prevailing challenges of different waveform‐based location methods and what they promise to offer in the near future.

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Continuous Kurtosis-Based Migration for Seismic Event Detection and Location, with Application to Piton de la Fournaise Volcano, La Reunion

Cited by 82 publications

References 34 publications

Relocation of clustered earthquakes in the Groningen gas field

Relocation of clustered earthquakes in the Groningen gas field

Development of an adaptive multi‐method algorithm for automatic picking of first arrival times: application to near surface seismic data

Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

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