Ambient-noise tomography of the wider Vienna Basin region

We present a new 3-D shear-velocity model for the top 30 km of the crust in the wider Vienna Basin region based on surface waves extracted from ambient-noise cross-correlations. We use continuous seismic records of 63 broad-band stations of the AlpArray project to retrieve interstation Green’s functions from ambient-noise cross-correlations in the period range from 5 to 25 s. From these Green’s functions, we measure Rayleigh group traveltimes, utilizing all four components of the cross-correlation tensor, which are associated with Rayleigh waves (ZZ, RR, RZ and ZR), to exploit multiple measurements per station pair. A set of selection criteria is applied to ensure that we use high-quality recordings of fundamental Rayleigh modes. We regionalize the interstation group velocities in a 5 km × 5 km grid with an average path density of ∼20 paths per cell. From the resulting group-velocity maps, we extract local 1-D dispersion curves for each cell and invert all cells independently to retrieve the crustal shear-velocity structure of the study area. The resulting model provides a previously unachieved lateral resolution of seismic velocities in the region of ∼15 km. As major features, we image the Vienna Basin and Little Hungarian Plain as low-velocity anomalies, and the Bohemian Massif with high velocities. The edges of these features are marked with prominent velocity contrasts correlated with faults, such as the Alpine Front and Vienna Basin transfer fault system. The observed structures correlate well with surface geology, gravitational anomalies and the few known crystalline basement depths from boreholes. For depths larger than those reached by boreholes, the new model allows new insight into the complex structure of the Vienna Basin and surrounding areas, including deep low-velocity zones, which we image with previously unachieved detail. This model may be used in the future to interpret the deeper structures and tectonic evolution of the wider Vienna Basin region, evaluate natural resources, model wave propagation and improve earthquake locations, among others

Arrival angles of teleseismic fundamental mode Rayleigh waves across the AlpArray

The dense AlpArray network allows studying seismic wave propagation with high spatial resolution. Here we introduce an array approach to measure arrival angles of teleseismic Rayleigh waves. The approach combines the advantages of phase correlation as in the two-station method with array beamforming to obtain the phase-velocity vector. 20 earthquakes from the first two years of the AlpArray project are selected, and spatial patterns of arrival-angle deviations across the AlpArray are shown in maps, depending on period and earthquake location. The cause of these intriguing spatial patterns is discussed. A simple wave-propagation modelling example using an isolated anomaly and a Gaussian beam solution suggests that much of the complexity can be explained as a result of wave interference after passing a structural anomaly along the wave paths. This indicates that arrival-angle information constitutes useful additional information on the Earth structure, beyond what is currently used in inversions

Southern Queensland Spiral Array

SQSPA is a three arm spiral array. This array was an experimental array designed to enhance weak signals and identify the local character of the wavefield. It consists of 16 trillium compact 120s seismometers and has an array radius of approximately 25 km.Research School of Earth Sciences, ANUAusPass is an initiative supported with funding from AuScope and the Australian National Universit

Southern Queensland Spiral Array

SQSPA is a three arm spiral array. This array was an experimental array designed to enhance weak signals and identify the local character of the wavefield. It consists of 16 trillium compact 120s seismometers and has an array radius of approximately 25 km.Research School of Earth Sciences, ANUAusPass is an initiative supported with funding from AuScope and the Australian National Universit

Spiral-arm seismic arrays

Seismic arrays havemany uses for signal enhancement, from surface-wave characterization of the near surface to teleseismic detection in the context of monitoring nuclear tests. Many variants of the geometrical configuration of stations have been used with the objective of maximizing potential resolution of the incoming wavefronts direction of arrival. Aversatile class of array configurations, with good resolution properties, can be constructed with multiple spiral arms. The array response is comparablewith the same number of full circles, but with far fewer stations and is robust to minor position changes in emplacement. The desirable properties of the spiral-arm arrays are illustrated for a permanent array in the Precambrian Pilbara craton in northwestern Australia and for a temporary array on ancient sediments in southern Queensland, Australia. In each case, the practical array response is very good and matches the theoretical expectations. The spiral-arm configuration allows the deployment of relatively large aperture arrays with a limited number of stations, which is advantageous in a broad range of seismic applications, including near-surface characterization

Direct-seismogram inversion for receiver-side structure with uncertain source-time functions

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

Crustal and uppermost mantle structure beneath the External Dinarides, Croatia, determined from teleseismic receiver functions

Broad-band seismograms of teleseismic events recorded at the Croatian Seismological Network were used to compute radial receiver functions (RFs) for eight locations in the External Dinarides. Waveform modelling was performed by a multistep matching of the theoretical RFs computed for horizontally layered 1-D isotropic models with the averaged observed RFs. Constraints from existing deep seismic sounding profiles, traveltime curves of regional crustal seismic phases and intuitive inferences gained from interactive forward modelling were used to construct initial 1-D models of the Earth. A non-linear inversion was performed in two steps-a grid search followed by the Monte Carlo search for the model parameters. Concurrently, RFs from different azimuths were stacked to obtain trade-off estimates of crustal thickness versus Vp/Vs ratios. The Moho depths were found in the range from around 40 km for Northern Adriatic stations to over 55 km for stations in the central part of the External Dinarides. Comparing our results with recent maps of the Moho topography inferred from seismic and gravimetric data, we find that for some stations the agreement between our results and the existing Moho maps is very good. For the others, we find the Mohorovičić discontinuity to be considerably deeper, indicating some of the thickest crust in Europe. Although it is plausible that such a deep Moho could be a consequence of a complex tectonic setting of the region (e.g. overlapping of two large tectonic units-the Adriatic microplate and the Dinarides), this result will have to be verified in the future studies using various other geophysical techniques

Direct-seismogram inversion for receiver-side structure with uncertain source-time functions

International audienceThis 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