ALBERT

Description: This paper presents direct-seismogram inversion (DSI) for receiver-side structure which treats the source signal incident from below (the effective source–time function—STF) as a vector of unknown parameters in a Bayesian framework. As a result, the DSI method developed here does not require deconvolution by observed seismogram components as typically applied in receiver-function inversion and avoids the problematic issue of choosing subjective tuning parameters in this deconvolution. This results in more meaningful inversion results and uncertainty estimation compared to classic receiver-function inversion. A rigorous derivation is presented of the likelihood function required for unbiased inversion results. The STF is efficiently inferred by a maximum-likelihood closed-form expression that does not require deconvolution by noisy waveforms. Rather, deconvolution is only by predicted impulse responses for the unknown environment (considered to be a 1-D, horizontally stratified medium). For a given realization of the parameter vector which describes the medium below the station, data predictions are computed as the convolution of the impulse response and the maximum-likelihood source estimate for that medium. Therefore, the assumption of a Gaussian pulse with specified parameters, typical for the prediction of receiver functions, is not required. Directly inverting seismogram components has important consequences for the noise on the data. Since the signal processing does not require filtering and deconvolution, data errors are less correlated and more straightforward to model than those for receiver functions. This results in better inversion results (parameter values and uncertainties), since assumptions made in the derivation of the likelihood function are more likely to be met by the inversion process. The DSI method is demonstrated for simulated waveforms and then applied to data for station Hyderabad on the Indian craton. The measured data are inverted with both the new DSI and traditional receiver-function inversion. All inversions are carried out for a trans-dimensional model that treats the number of layers in the model as unknown. Results for DSI are consistent with previous studies for the same location. The DSI has clear advantages in trans-dimensional inversion. Uncertainty estimates appear more realistic (larger) in both model complexity (number of layers) and in terms of seismic velocity profiles. Receiver-function inversion results in more complex profiles (highly-layered structure) and suggests unreasonably small uncertainties. This effect is likely also significant when the parametrization is considered to be fixed but exacerbated for the trans-dimensional model: If hierarchical errors are poorly estimated, trans-dimensional models overestimate the structure which produces unfavourable results for the receiver-function inversion.

Description: In order to characterize the subsurface structure of the Jakarta Basin, Indonesia, a dense portable seismic broad-band network was operated by The Australian National University (ANU) and the Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) between October 2013 and February 2014. Overall 96 locations were sampled through successive deployments of 52 seismic broad-band sensors at different parts of the city. Oceanic and anthropogenic noises were recorded as well as regional and teleseismic earthquakes. We apply regularized deconvolution to the recorded ambient noise of the vertical components of available station pairs, and over 3000 Green's functions were retrieved in total. Waveforms from interstation deconvolutions show clear arrivals of Rayleigh fundamental and higher order modes. The traveltimes that were extracted from group velocity filtering of fundamental mode Rayleigh wave arrivals, are used in a 2-stage Transdimensional Bayesian method to map shear wave structure of subsurface. The images of S wave speed show very low velocities and a thick basin covering most of the city with depths up to 1.5 km. These low seismic velocities and the thick basin beneath the city potentially cause seismic amplification during a subduction megathrust or other large earthquake close to the city of Jakarta.

Description: Delineating the crustal structure of central Java is crucial for understanding its complex tectonic setting. However, seismic imaging of the strong heterogeneity typical of such a tectonically active region can be challenging, particularly in the upper crust where velocity contrasts are strongest and steep body wave ray paths provide poor resolution. To overcome these difficulties, we apply the technique of ambient noise tomography (ANT) to data collected during the Merapi Amphibious Experiment (MERAMEX), which covered central Java with a temporary deployment of over 120 seismometers during 2004 May–October. More than 5000 Rayleigh wave Green's functions were extracted by cross-correlating the noise simultaneously recorded at available station pairs. We applied a fully non-linear 2-D Bayesian probabilistic inversion technique to the retrieved traveltimes. Features in the derived tomographic images correlate well with previous studies, and some shallow structures that were not evident in previous studies are clearly imaged with ANT. The Kendeng Basin and several active volcanoes appear with very low group velocities, and anomalies with relatively high velocities can be interpreted in terms of crustal sutures and/or surface geological features.

Description: This paper develops a probabilistic Bayesian approach to the problem of inferring the spatiotemporal evolution of earthquake rupture on a fault surface from seismic data with rigorous uncertainty estimation. To date, uncertainties of rupture parameters are poorly understood, and the effect of choices such as fault discretization on uncertainties has not been studied. We show that model choice is fundamentally linked to uncertainty estimation and can have profound effects on results. The approach developed here is based on a trans-dimensional self-parametrization of the fault, avoids regularization constraints and provides rigorous uncertainty estimation that accounts for model-selection ambiguity associated with the fault discretization. In particular, the fault is parametrized using self-adapting irregular grids which intrinsically match the local resolving power of the data and provide parsimonious solutions requiring few parameters to capture complex rupture characteristics. Rupture causality is ensured by parametrizing rupture-onset time by a rupture-velocity field and obtaining first rupture times from the eikonal equation. The Bayesian sampling of the parameter space is implemented on a computer cluster with a highly efficient parallel tempering algorithm. The inversion is applied to simulated and observed W-phase waveforms from the 2010 Maule (Chile) earthquake. Simulation results show that our approach avoids both over- and underparametrization to ensure unbiased inversion results with uncertainty estimates that are consistent with data information. The simulation results also show the ability of W-phase data to resolve the spatial variability of slip magnitude and rake angles. In addition, sensitivity to spatially dependent rupture velocities exists for kinematic slip models. Application to the observed data indicates that residual errors are highly correlated and likely dominated by theory error, necessitating the iterative estimation of a non-stationary data covariance matrix. The moment magnitude for the Maule earthquake is estimated to be ~8.9, with slip concentrated in two zones updip of and north and south of the hypocentre, respectively. While this aspect of the slip distribution is similar to previous studies, we show that the slip maximum in the southern zone is poorly resolved compared to the northern zone. Both slip maxima are higher than reported in previous studies, which we speculate may be due to the lack of bias caused by the regularization used in other studies.

Description: In recent studies, the Time Reverse Imaging (TRI) method has been implemented in tsunami science to estimate tsunami source models. A TRI algorithm called Green's Function Based Time Reverse Imaging (GFTRI) was previously developed to reconstruct the source model by using source inversion as a guide to appropriately scale time-reversed images of the tsunami source. In this article, we consider a more complete approach to source inversion using reversed time images of the tsunami source by considering cross correlations among Green's functions. As a consequence, the performance of the method improves significantly. We tested this new algorithm using data from the 2011 Japan tsunami. Our results show that the method is capable of extracting more details of the source and providing excellent waveform fits at all stations, including those not used in the source imaging. We have also studied the sensitivity analysis of reversed time tsunami source inversion and found that the method is less sensitive to the number of stations, once a minimum number of stations is utilized. Moreover, this new approach is able to estimate the tsunami source with reasonable accuracy using data available soon after an earthquake, which indicates that it has potential to be used in both near- and far-field tsunami forecasting. ©2017. American Geophysical Union. All Rights Reserved.

Description: A dataset comprising some 1200 weak- and strong-motion records from 84 earthquakes is compiled to develop a regional ground-motion model for southeastern Australia (SEA). Events were recorded from 1993 to 2004 and range in size from moment magnitude 2.0〈 or =M〈 or =4.7. The decay of vertical-component Fourier spectral amplitudes is modeled by trilinear geometrical spreading. The decay of low- frequency spectral amplitudes can be approximated by the coefficient of R (super -1.3) (where R is hypocentral distance) within 90 km of the seismic source. From approximately 90 to 160 km, we observe a transition zone in which the seismic coda are affected by postcritical reflections from midcrustal and Moho discontinuities. In this hypocentral distance range, geometrical spreading is approximately R (super +0.1) . Beyond 160 km, low-frequency seismic energy attenuates rapidly with source-receiver distance, having a geometrical spreading coefficient of R (super -1.6) . The associated regional seismic-quality factor can be expressed by the polynomial: log Q(f) = 3.66-1.44 log f+0.768 (log f) (super 2) +0.058 (log f) (super 3) for frequencies 0.78〈 or =f〈 or =19.9 Hz. Fourier spectral amplitudes, corrected for geometrical spreading and anelastic attenuation, are regressed with M to obtain quadratic source scaling coefficients. Modeled vertical-component displacement spectra fit the observed data well. Amplitude residuals are, on average, relatively small and do not vary with hypocentral distance. Predicted source spectra (i.e., at R = 1 km) are consistent with eastern North American (ENA) models at low frequencies (f less than approximately 2 Hz) indicating that moment magnitudes calculated for SEA earthquakes are consistent with moment magnitude scales used in ENA over the observed magnitude range. The models presented represent the first spectral ground-motion prediction equations developed for the southeastern Australian region. This work provides a useful framework for the development of regional ground-motion relations for earthquake hazard and risk assessment in SEA.