Aftershocks driven by afterslip and fluid pressure sweeping through a fault‐fracture mesh

Ross, Zachary E.; Rollins, Chris; Cochran, E. S.; Hauksson, Egill; Avouac, Jean Philippe; Ben‐Zion, Yehuda

doi:10.1002/2017gl074634

Cited by 127 publications

(107 citation statements)

References 49 publications

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“…The time‐magnitude plot for these more distant aftershocks, south of ~42.6°N, is similar to that of a swarm as it has no outstanding principal event (Figure c). The relatively rapid migration rate of 1 km/d is consistent with aftershocks triggered by afterslip (e.g., Hauksson et al, ; Perfettini et al, ; Ross et al, ).…”

Section: Discussionsupporting

confidence: 70%

Afterslip Enhanced Aftershock Activity During the 2017 Earthquake Sequence Near Sulphur Peak, Idaho

Koper

Pankow

Pechmann

et al. 2018

Geophysical Research Letters

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An energetic earthquake sequence occurred during September to October 2017 near Sulphur Peak, Idaho. The normal‐faulting Mw 5.3 mainshock of 2 September 2017 was widely felt in Idaho, Utah, and Wyoming. Over 1,000 aftershocks were located within the first 2 months, 29 of which had magnitudes ≥4.0 ML. High‐accuracy locations derived with data from a temporary seismic array show that the sequence occurred in the upper (<10 km) crust of the Aspen Range, east of the northern section of the range‐bounding, west‐dipping East Bear Lake Fault. Moment tensors for 77 of the largest events show normal and strike‐slip faulting with a summed aftershock moment that is 1.8–2.4 times larger than the mainshock moment. We propose that the unusually high productivity of the 2017 Sulphur Peak sequence can be explained by aseismic afterslip, which triggered a secondary swarm south of the coseismic rupture zone beginning ~1 day after the mainshock.

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Section: Discussionsupporting

confidence: 70%

Afterslip Enhanced Aftershock Activity During the 2017 Earthquake Sequence Near Sulphur Peak, Idaho

Koper

Pankow

Pechmann

et al. 2018

Geophysical Research Letters

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“…This conclusion is supported by the analysis of the spatiotemporal distribution of the seismicity that occurred in the first 30 days (from [1], relocated) or in the first 50 days (INGV catalog, http://cnt.rm.ingv.it/) after the May 20 mainshock (Figure s7). It is possible that the quality of these last localizations does not have the necessary spatial and temporal resolution to detect fluid-driven seismicity; nevertheless, we do not see any clear evidence of fluiddriven hypocenter diffusion both along strike and dip, as observed in previous works (e.g., [49,51]), but only the sudden westward migration due to the occurrence of the May 29 mainshock.…”

Section: Discussionmentioning

confidence: 50%

“…At the May 29 hypocenter location, we also find, in the S2 case ( Figure 9(a)), that the CFF is further increased by about 1 kPa in the first 9 days due to poroelastic rebound. Highpressurized fluids have been indicated as responsible for aseismic and seismic slip in laboratory experiments (e.g., [44]), midscale experiments (e.g., [45,46]), and at fault scale level (e.g., [47][48][49]). In the present model, the estimated pressure variations are relative to hydrostatic conditions.…”

Section: Discussionmentioning

confidence: 99%

Poroelasticity and Fluid Flow Modeling for the 2012 Emilia-Romagna Earthquakes: Hints from GPS and InSAR Data

et al. 2018

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The Emilia-Romagna seismic sequence in May 2012 was characterized by two mainshocks which were close in time and space. Several authors already modeled the geodetic data in terms of the mechanical interaction of the events in the seismic sequence. Liquefaction has been extensively observed, suggesting an important role of fluids in the sequence. In this work, we focus on the poroelastic effects induced by the two mainshocks. In particular, the target of this work is to model the influence of fluids and pore-pressure changes on surface displacements and on the Coulomb failure function (CFF). The fluid flow and poroelastic modeling was performed in a 3D half-space whose elastic and hydraulic parameters are depth dependent, in accordance with the geology of the Emilia-Romagna subsoil. The model provides both the poroelastic displacements and the pore-pressure changes induced coseismically by the two mainshocks at subsequent periods and their evolution over time. Modeling results are then compared with postseismic InSAR and GPS displacement time series: the InSAR data consist of two SBAS series presented in previous works, while the GPS signal was detected adopting a variational Bayesian independent component analysis (vbICA) method. Thanks to the vbICA, we are able to separate the contribution of afterslip and poroelasticity on the horizontal surface displacements recorded by the GPS stations. The poroelastic GPS component is then compared to the modeled displacements and shown to be mainly due to drainage of the shallowest layers. Our results offer an estimation of the poroelastic effect magnitude that is small but not negligible and mostly confined in the near field of the two mainshocks. We also show that accounting for a 3D fault representation with a nonuniform slip distribution and the elastic-hydraulic layering of the half-space has an important role in the simulation results.

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“…Our best‐fitting values of w lie within the range found in previous studies that use rate‐state to model aftershock activity with afterslip (Helmstetter & Shaw, ). We do note that the total additional postseismic stress changes implied by these values of u and w range from 0.5 to 2 times the Ocotillo coseismic stress increment in the respective regions, consistent with findings that the moment associated with postseismic deformation is often a large fraction of or sometimes larger than the coseismic moment (e.g., Barbot et al, ; Floyd et al, ; Langbein et al, ; Pollitz et al, ; Pritchard & Simons, ; Reilinger et al, , and references therein, Ross et al, ).…”

Section: Coupled Coulomb Rate‐state Modeling Resultsmentioning

confidence: 99%

Delayed Seismicity Rate Changes Controlled by Static Stress Transfer

et al. 2017

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On 15 June 2010, a Mw5.7 earthquake occurred near Ocotillo, California, in the Yuha Desert. This event was the largest aftershock of the 4 April 2010 Mw7.2 El Mayor‐Cucapah (EMC) earthquake in this region. The EMC mainshock and subsequent Ocotillo aftershock provide an opportunity to test the Coulomb failure hypothesis (CFS). We explore the spatiotemporal correlation between seismicity rate changes and regions of positive and negative CFS change imparted by the Ocotillo event. Based on simple CFS calculations we divide the Yuha Desert into three subregions, one triggering zone and two stress shadow zones. We find the nominal triggering zone displays immediate triggering, one stress shadowed region experiences immediate quiescence, and the other nominal stress shadow undergoes an immediate rate increase followed by a delayed shutdown. We quantitatively model the spatiotemporal variation of earthquake rates by combining calculations of CFS change with the rate‐state earthquake rate formulation of Dieterich (1994), assuming that each subregion contains a mixture of nucleation sources that experienced a CFS change of differing signs. Our modeling reproduces the observations, including the observed delay in the stress shadow effect in the third region following the Ocotillo aftershock. The delayed shadow effect occurs because of intrinsic differences in the amplitude of the rate response to positive and negative stress changes and the time constants for return to background rates for the two populations. We find that rate‐state models of time‐dependent earthquake rates are in good agreement with the observed rates and thus explain the complex spatiotemporal patterns of seismicity.

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Aftershocks driven by afterslip and fluid pressure sweeping through a fault‐fracture mesh

Cited by 127 publications

References 49 publications

Afterslip Enhanced Aftershock Activity During the 2017 Earthquake Sequence Near Sulphur Peak, Idaho

Afterslip Enhanced Aftershock Activity During the 2017 Earthquake Sequence Near Sulphur Peak, Idaho

Poroelasticity and Fluid Flow Modeling for the 2012 Emilia-Romagna Earthquakes: Hints from GPS and InSAR Data

Delayed Seismicity Rate Changes Controlled by Static Stress Transfer

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