Coseismic release of water from mountains: Evidence from the 1999 (Mw = 7.5) Chi-Chi, Taiwan, earthquake

Wang, Chi‐Yuen; Wang, Chung-Ho; Manga, Michael

doi:10.1130/g20753.1

Cited by 161 publications

(204 citation statements)

References 27 publications

(42 reference statements)

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“…(We should keep in mind that the fluctuations of well water levels sometimes observed immediately following nearby earthquakes, provides direct evidence of postseismic pore-pressure transients, e.g. Jónsson et al 2003;Wang et al 2004. ) However, the inability of the logarithmic transient formula to fit the entire time series could also arise because GPS station displacement caused by afterslip is sensitive to spatial variation of the frictional properties of the fault or plate boundary engaged in afterslip.…”

Section: Incorporating Non-steady Displacement Trendsmentioning

confidence: 99%

“…7) are not well suited to this task, because postseismic transients often begin 'in the middle' of a time series, with the fastest accelerations of all just after the earthquake, and no transient acceleration at all immediately before the earthquake. Postseismic deformation is widely thought to be driven by some combination of (1) poroelastic rebound, which is deformation caused by pore fluids flowing in response to the stress perturbations produced by the earthquake (Peltzer et al 1998;Wang 2000;Masterlark and Wang 2002;Jónsson et al 2003;Fialko 2004;Wang et al 2004), (2) afterslip on the fault or plate boundary that generated the earthquake (Smith and Wyss 1968;Bucknam et al 1978;Marone et al 1991;Heki et al 1997;Marone 1998;Perfettini and Avouac 2007;Perfettini et al 2010, Lin et al 2013, and (3) bulk viscoelastic relaxation of material surrounding (in map view) and beneath the fault (e.g. Thatcher and Rundle 1979;Wahr and Wyss 1980;Deng et al 1998;Pollitz et al 2000;Freed 2007;Freed et al 2007;Wang et al 2012).…”

Section: Incorporating Non-steady Displacement Trendsmentioning

confidence: 99%

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Trajectory models and reference frames for crustal motion geodesy

Bevis

Brown

2014

J Geod

190

169

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We sketch the evolution of station trajectory models used in crustal motion geodesy over the last several decades, and describe some recent generalizations of these models that allow geodesists and geophysicists to parameterize accelerating patterns of displacement in general, and postseismic transient deformation in particular. Modern trajectory models are composed of three sub-models that represent secular trends, annual oscillations, and instantaneous jumps in coordinate time series. Traditionally the trend model invoked constant station velocity. This can be generalized by assuming that position is a polynomial function of time. The trajectory model can also be augmented as needed, by including one or more logarithmic transients in order to account for typical multi-year patterns of postseismic transient motion. Many geodetic and geophysical research groups are using general classes of trajectory model to characterize their crustal displacement time series, but few if any of them are using these trajectory models to define and realize the terrestrial reference frames (RFs) in which their time series are expressed. We describe a global GPS reanalysis program in which we use two general classes of trajectory model, tuned on a station by station basis. We define the network trajectory model as the set of station trajectory models encompassing every station in the network. We use the network trajectory model from the each global analysis to assign prior position estimates for the next round of GPS data processing. We allow our daily orbital solutions to relax so as to maintain their consistency with the network polyhedron. After several iterations we produce GPS time series expressed in a RF similar to, but not identical with ITRF2008.M. Bevis (B) · A. Brown Division of Geodetic Sciences, School of Earth Sciences, Ohio State University, Columbus, OH, USA e-mail: mbevis@osu.edu We find that each iteration produces an improvement in the daily repeatability of our global time series and in the predictive power of our trajectory models.

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Section: Incorporating Non-steady Displacement Trendsmentioning

confidence: 99%

Section: Incorporating Non-steady Displacement Trendsmentioning

confidence: 99%

Trajectory models and reference frames for crustal motion geodesy

Bevis

Brown

2014

J Geod

190

169

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“…While earthquake ground shaking triggers near-instantaneous landsliding [Li et al, 2014], some slopes do not fully fail and are weakened [Khattak et al, 2010], resulting in elevated susceptibility of hillslopes to landsliding during postseismic rainfall [Lin et al, 2008] and subsequent seismicity [Parker et al, 2015]. These legacy effects have been broadly attributed to landscape-scale weakening of hillslope substrates resulting from increased brittle (micro)fracturing and joint dilation ("damage") caused by transient hillslope stresses experienced during earthquake ground shaking [Wang et al, 2004]. Our understanding and interpretation of the behavior of brittle landslides in response to seismicity has a firm theoretical basis [Petley et al, 2005].…”

Section: Introductionmentioning

confidence: 99%

The control of earthquake sequences on hillslope stability

Brain

Rosser

Tunstall

2017

Geophysical Research Letters

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Earthquakes trigger landslides in mountainous regions. Recent research suggests that the stability of hillslopes during and after a large earthquake is influenced by legacy effects of previous seismic activity. However, the shear strength and strain response of ductile hillslope materials to sequences of earthquake ground shaking of varying character is poorly constrained, inhibiting our ability to fully explain the nature of earthquake‐triggered landslides. We used geotechnical laboratory testing to simulate earthquake loading of hillslopes and to assess how different sequences of ground shaking influence hillslope stability prior to, during, and following an earthquake mainshock. Ground‐shaking events prior to a mainshock that do not result in high landslide strain accumulation can increase bulk density and interparticle friction. This strengthens a hillslope, reducing landslide displacement during subsequent seismicity. By implication, landscapes in different tectonic settings will likely demonstrate different short‐ and long‐term responses to single earthquakes due to differences in the magnitude, frequency, and sequencing of earthquakes.

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“…Wang et al 2004a). At far-field (many fault lengths) distances from an epicentre, static stresses due to the earthquake are small, so sustained changes in groundwater are thought to be the result of processes that can convert (the larger) transient, dynamic strains into medium-to long-term changes in fluid connectivity and flow.…”

Section: Introductionmentioning

confidence: 99%