Intraplate seismicity in Canada: a graph theoretic approach to data analysis and interpretation

Vasudevan, K.; Eaton, David W.; Davidsen, Jörn

doi:10.5194/npg-17-513-2010

Cited by 6 publications

(5 citation statements)

References 74 publications

(105 reference statements)

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“…Numerous clustering methods have been applied to microseismic data. Some examples include waveform analysis (Moriya et al, 2005;Skoumal et al, 2015;Tezuka & Niitsuma, 2000), matched-filtering (Caffagni et al, 2016;Eaton & Caffagni, 2015), principal component analysis (PCA) with graph theory (Vasudevan et al, 2010), the k-means algorithm (Eaton et al, 2014), and density-based clustering (Cesca et al, 2013). Unsupervised machine learning techniques have also been applied for automated microseismic data arrival picking (Chen, 2018a) and fast waveform detection in microseismic imaging problems (Chen, 2018b).…”

Section: Research Lettermentioning

confidence: 99%

Quantifying Fracture Networks Inferred From Microseismic Point Clouds by a Gaussian Mixture Model With Physical Constraints

McKean

Priest

Dettmer

et al. 2019

Geophysical Research Letters

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Microseismicity is generated by slip on fractures and faults and can be used to infer natural or anthropogenic deformation processes in the subsurface. Yet identifying patterns and fractures from microseismic point clouds is a major challenge that typically relies on the skill and judgment of practitioners. Clustering has previously been applied to tackle this problem, but with limited success. Here, we introduce a probabilistic clustering method to identify fracture networks, based on a Gaussian mixture model algorithm with physical constraints. This method is applied to a rich microseismic data set recorded during the hydraulic fracturing of eight horizontal wells in western Canada. We show that the method is effective for distinguishing hydraulic‐fracture‐created events from induced seismicity. These fractures follow a log‐normal distribution and reflect the physical mechanisms of the hydraulic fracturing process. We conclude that this method has wide applicability for interpreting natural and anthropogenic processes in the subsurface.

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Section: Research Lettermentioning

confidence: 99%

Quantifying Fracture Networks Inferred From Microseismic Point Clouds by a Gaussian Mixture Model With Physical Constraints

McKean

Priest

Dettmer

et al. 2019

Geophysical Research Letters

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“…The HuBLE‐network contains 38 broadband seismometers (primarily Guralp CMG‐3T, CMG‐ESP). The increased station coverage around Hudson Bay has led to a reduction in magnitude detection threshold in the middle of Hudson Bay from M w = 3.5 to M w = 2.5 (Vasudevan et al 2010). Earthquakes with lower magnitudes are observable close to the stations and are found in the northern part of Hudson Bay.…”

Section: Introductionmentioning

confidence: 99%

Moment tensors, state of stress and their relation to post-glacial rebound in northeastern Canada

Steffen

Eaton

2012

Geophysical Journal International

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SUMMARY In the stable craton of northeastern Canada moderate seismicity as well as glacial isostatic adjustment (GIA) has been observed. We investigate five earthquakes with moment magnitudes between 3.6 and 4.1 that have occurred in northern Hudson Bay since 2007, which may be triggered by GIA. Focal mechanisms of the earthquakes are determined using a waveform‐fitting procedure for surface waves, in which the best double‐couple mechanism is obtained through a grid search over strike, dip, and rake. All events exhibit a thrust‐fault mechanism, in general agreement with mechanisms of previously analysed earthquakes in that area. Stress‐inversion results incorporating our new mechanisms as well as previously published results exhibit significant differences from previously calculated stresses from GIA models. For the first time, maximum horizontal stress direction SHmax is compared to the modelled stress values for northeastern Canada. We find that the maximum horizontal stress direction strikes roughly NNW–SSE, in contrast to previously inferred NE‐SW orientation of SHmax. Our results indicate that the local stress field in northern Hudson Bay deviates from regional stresses resulting from the existence of a roughly E–W oriented zone of faulting, which is not included in existing GIA models.

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“…This defines an element in the earthquake sequence. A continued sequence of events is represented as a directed graph (Vasudevan et al, 2010;Vasudevan and Cavers, 2014b) with the vertices representing the earthquakes (and their attributes) and the arcs the connecting links between neighbors in a sequence. Figures 2a and 3a show the transition matrices for the directed graphs of the two grids, 128 × 128 and 1024 × 1024 grids.…”

Section: Building the Directed Graphmentioning

confidence: 99%

“…Cavers and Vasudevan (2015) have incorporated the spatio-temporal complexity of the earthquake recurrences (Davidsen et al, 2008;Vasudevan et al, 2010) into their Markov chain model.…”

Section: Introductionmentioning

confidence: 99%

Earthquake sequencing: chimera states with Kuramoto model dynamics on directed graphs

Vasudevan

Cavers

Ware

2015

Nonlin. Processes Geophys.

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Abstract. Earthquake sequencing studies allow us to investigate empirical relationships among spatio-temporal parameters describing the complexity of earthquake properties. We have recently studied the relevance of Markov chain models to draw information from global earthquake catalogues. In these studies, we considered directed graphs as graph theoretic representations of the Markov chain model and analyzed their properties. Here, we look at earthquake sequencing itself as a directed graph. In general, earthquakes are occurrences resulting from significant stress interactions among faults. As a result, stress-field fluctuations evolve continuously. We propose that they are akin to the dynamics of the collective behavior of weakly coupled non-linear oscillators. Since mapping of global stress-field fluctuations in real time at all scales is an impossible task, we consider an earthquake zone as a proxy for a collection of weakly coupled oscillators, the dynamics of which would be appropriate for the ubiquitous Kuramoto model. In the present work, we apply the Kuramoto model with phase lag to the nonlinear dynamics on a directed graph of a sequence of earthquakes. For directed graphs with certain properties, the Kuramoto model yields synchronization, and inclusion of nonlocal effects evokes the occurrence of chimera states or the co-existence of synchronous and asynchronous behavior of oscillators. In this paper, we show how we build the directed graphs derived from global seismicity data. Then, we present conditions under which chimera states could occur and, subsequently, point out the role of the Kuramoto model in understanding the evolution of synchronous and asynchronous regions. We surmise that one implication of the emergence of chimera states will lead to investigation of the present and other mathematical models in detail to generate global chimera-state maps similar to global seismicity maps for earthquake forecasting studies.

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Intraplate seismicity in Canada: a graph theoretic approach to data analysis and interpretation

Cited by 6 publications

References 74 publications

Quantifying Fracture Networks Inferred From Microseismic Point Clouds by a Gaussian Mixture Model With Physical Constraints

Quantifying Fracture Networks Inferred From Microseismic Point Clouds by a Gaussian Mixture Model With Physical Constraints

Moment tensors, state of stress and their relation to post-glacial rebound in northeastern Canada

Earthquake sequencing: chimera states with Kuramoto model dynamics on directed graphs

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