Picking versus stacking in a modern microearthquake location: Comparison of results from a surface passive seismic monitoring array in Oklahoma

Abstract

We present location results for a group of ∼200 microearthquakes that occurred in 2012 in a region of Oklahoma hosting ongoing exploration activities. Using a local passive surface seismic monitoring network of 15 broadband stations, we applied two modern location techniques that use fundamentally different approaches. The first is a pick-based double-difference relocation method with waveform crosscorrelation. Multipleevent location techniques such as these are generally regarded as the best approach for obtaining high-precision locations from pick data. The second approach is an automated waveform migration stacking method. These types of methods are becoming increasingly common due to increasing network station density and computer power. The results from the two methods show excellent agreement and provide similar results for the interpreter. Both methods reveal spatial and temporal patterns in the locations that are not visible in results obtained using a more traditional pick-based approach. We performed two statistical uncertainty tests to assess the effects of data quality and quantity on the two methods. We show that the uncertainties for both methods are comparable, but that the stack-based locations are less sensitive to station geometry, likely due to the different treatment of outliers and the beneficial inclusion of noisier data. Finally, we discuss the favorable conditions in which to apply each method and argue that for small aperture surface arrays where accurate velocity information exists, such as in this study, the stack-based method is preferable due to the higher degree of automation. Under these conditions, stack-based methods better allow for rapid and precise determination of microearthquake locations, facilitating improved interpretations of seismogenic processes.

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

“…For example, the imaging resolution or distribution can also provide preliminary knowledge about the location uncertainty (Anikiev et al, ; Kao & Shan, ), and the Bootstrap or Jackknife method can help to extract some statistical information of the location results from waveform‐based methods (Grigoli et al, ; L. Li et al, ). According to several existing studies for PWS, the estimated location uncertainty ranges from tens of meters to about 200 m, which are comparable to the corresponding prevailing wavelengths (Grigoli et al, ; Pesicek et al, ; L. Li et al, ).…”

“…One potential solution is adding perturbations to the theoretical traveltime table. Given the short development course of waveform‐based methods and the difference of location uncertainty estimation between the two types of methods, more systematic benchmarking studies of seismic source location algorithms are in demand, though several recent comparisons of microseismic waveform‐based and traveltime‐based location algorithms can be found in recent literature (e.g., Pesicek et al, ; Trojanowski & Eisner, ; L. Li et al, ; Wuestefeld et al, ; Grigoli, Scarabello, et al, ). Note that full waveform‐based methods have more sources of uncertainty from, for example, density, S wave velocity, and attenuation models.…”

“…Waveform‐based location methods involve waveform storage and transmission, which raises new challenges for real‐time application, especially for large monitoring arrays and large target regions (Pesicek et al, ; Xue et al, ; Li et al, ). For example, it took tens of minutes to several hours for PWS when hundreds of receivers and millions of grid points were considered (Li, Xie, et al, ), let alone TRI and FWI, which are more computational demanding (see Table ).…”

“…Traveltime‐based location methods require phase picking and only make use of the traveltime information. They are also known as picking‐ or ray‐based methods (Z. Li & van der Baan, ; Pesicek et al, ). Many modifications have been introduced to improve the performance of the traveltime‐based methods, most of which lie in the construction of misfit function and inversion strategies, such as joint hypocenter location method (e.g., Douglas, ; Pujol, ), relative location method (e.g., Fitch, ; Grechka et al, ; Spence, ), plain grid search (e.g., Eisner et al, ), master‐station or station‐pair double difference method (e.g., Font et al, ; Zhang et al, ; Zhou, ), joint location and velocity inversion (e.g., Block et al, ; Jansky et al, ; Z. Zhang et al, ; Diekmann et al, ), double difference relocation method (Waldhauser & Ellsworth, ; H. Zhang & Thurber, ; Hauksson & Shearer, ; Waldhauser & Schaff, ; Kwiatek et al, ), and cluster‐based relocation methods (G. Lin et al, ; P. Shearer et al, ; Matoza et al, ; Trugman & Shearer, ).…”

“…They are also known as picking-or ray-based methods (Z. Li & van der Baan, 2016;Pesicek et al, 2014). Many modifications have been introduced to improve the performance of the traveltime-based methods, most of which lie in the construction of misfit function and inversion strategies, such as joint hypocenter location method (e.g., Douglas, 1967;Pujol, 1992), relative location method (e.g., Fitch, 1975;Grechka et al, 2015;Spence, 1980), plain grid search (e.g., , masterstation or station-pair double difference method (e.g., Font et al, 2004;Zhang et al, 2010;Zhou, 1994), joint location and velocity inversion (e.g., Block et al, 1994;Jansky et al, 2010;Z.…”