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On 7 January 2020, an M-w 6.4 earthquake occurred in the northeastern Caribbean, a few kilometers offshore of the island of Puerto Rico. It was the mainshock of a complex seismic sequence, characterized by a large number of energetic earthquakes illuminating an east-west elongated area along the southwestern coast of Puerto Rico. Deformation fields constrained by Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data indicate that the coseismic movements affected only the western part of the island. To assess the mainshock's source fault parameters, we combined the geodetically derived coseismic deformation with teleseismic waveforms using Bayesian inference. The results indicate a roughly east-west oriented fault, dipping northward and accommodating similar to 1.4 m of transtensional motion. Besides, the determined location and orientation parameters suggest an offshore continuation of the recently mapped North Boqueron Bay-Punta Montalva fault in southwest Puerto Rico. This highlights the existence of unmapped faults with moderate-to-large earthquake potential within the Puerto Rico region.
Caldera unrest can lead to major volcanic eruptions. Analysis of subtle subsidence or inflation at calderas helps understanding of their subsurface volcanic processes and related hazards. Several subsiding calderas have shown similar patterns of ground deformation composed of broad subsidence affecting the entire volcanic edifice and stronger localized subsidence focused inside the caldera. Physical models of internal deformation sources used to explain these observations typically consist of two magma reservoirs at different depths in an elastic half-space. However, such models ignore important subsurface structures, such as ring faults, that may influence the deformation pattern. Here we use both analog subsidence experiments and boundary element modeling to study the three-dimensional geometry and kinematics of caldera subsidence processes, evolving from an initial downsag to a later collapse stage. We propose that broad subsidence is mainly caused by volume decrease within a single magma reservoir, whereas buried ring-fault activity localizes the deformation within the caldera. Omitting ring faulting in physical models of subsiding calderas and using multiple point/sill-like sources instead can result in erroneous estimates of magma reservoir depths and volume changes. (C) 2019 Elsevier B.V. All rights reserved.
Centroid moment tensor (CMT) parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, CMTs are inferred as solutions to inverse problems which are generally underdetermined and require significant assumptions, including assumptions about data noise. Broadly speaking, we consider noise to include both theory and measurement errors, where theory errors are due to assumptions in the inverse problem and measurement errors are caused by the measurement process. While data errors are routinely included in parameter estimation for full CMTs, less attention has been paid to theory errors related to velocity-model uncertainties and how these affect the resulting moment-tensor (MT) uncertainties. Therefore, rigorous uncertainty quantification for CMTs may require theory-error estimation which becomes a problem of specifying noise models. Various noise models have been proposed, and these rely on several assumptions. All approaches quantify theory errors by estimating the covariance matrix of data residuals. However, this estimation can be based on explicit modelling, empirical estimation and/or ignore or include covariances. We quantitatively compare several approaches by presenting parameter and uncertainty estimates in nonlinear full CMT estimation for several simulated data sets and regional field data of the M-1 4.4, 2015 June 13 Fox Creek, Canada, event. While our main focus is at regional distances, the tested approaches are general and implemented for arbitrary source model choice. These include known or unknown centroid locations, full MTs, deviatoric MTs and double-couple MTs. We demonstrate that velocity-model uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. However, not all approaches perform equally well. Including theory errors by estimating non-stationary (non-Toeplitz) error covariance matrices via iterative schemes during Monte Carlo sampling performs best and is computationally most efficient. In general, including velocity-model uncertainties is most important in cases where velocity structure is poorly known.