ALBERT

Description: The Piton de la Fournaise basaltic volcano, on La Réunion Island in the western Indian Ocean, is one of the most active volcanoes in the world. This volcano is classically considered as the surface expression of an upwelling mantle plume and its activity is continuously monitored, providing detailed information on its superficial dynamics and on the edifice structure. Deeper crustal and upper mantle structure under La Réunion Island is surprisingly poorly constrained, motivating this study. We used receiver function techniques to determine a shear wave velocity profile through the crust and uppermost mantle beneath La Réunion, but also at other seismic stations located on the hotspot track, to investigate the plume and lithosphere interaction and its evolution through time. Receiver functions (RFs) were computed at permanent broad-band seismic stations from the GEOSCOPE network (on La Réunion and Rodrigues), at IRIS stations MRIV and DGAR installed on Mauritius and Diego Garcia islands, and at the GEOFON stations KAAM and HMDM on the Maldives. We performed non-linear inversions of RFs through modelling of P -to- S conversions at various crustal and upper mantle interfaces. Joint inversion of RF and surface wave dispersion data suggests a much deeper Mohorovičić discontinuity (Moho) beneath Mauritius (~21 km) compared to La Réunion (~12 km). A magmatic underplated body may be present under La Réunion as a thin layer (≤3 km thick), as suggested by a previous seismic refraction study, and as a much thicker layer beneath other stations located on the hotspot track, suggesting that underplating is an important process resulting from the plume–lithosphere interaction. We find evidence for a strikingly low velocity layer starting at about 33 km depth beneath La Réunion that we interpret as a zone of partial melt beneath the active volcano. We finally observe low velocities below 70 km beneath La Réunion and below 50 km beneath Mauritius that could represent the base of the oceanic lithosphere.

Description: Azimuthal anisotropy is a powerful tool to reveal information about both the present structure and past evolution of the mantle. Anisotropic images of the upper mantle are usually obtained by analysing various types of seismic observables, such as surface wave dispersion curves or waveforms, SKS splitting data, or receiver functions. These different data types sample different volumes of the earth, they are sensitive to different length scales, and hence are associated with different levels of uncertainties. They are traditionally interpreted separately, and often result in incompatible models. We present a Bayesian inversion approach to jointly invert these different data types. Seismograms for SKS and P phases are directly inverted using a cross-convolution approach, thus avoiding intermediate processing steps, such as numerical deconvolution or computation of splitting parameters. Probabilistic 1-D profiles are obtained with a transdimensional Markov chain Monte Carlo scheme, in which the number of layers, as well as the presence or absence of anisotropy in each layer, are treated as unknown parameters. In this way, seismic anisotropy is only introduced if required by the data. The algorithm is used to resolve both isotropic and anisotropic layering down to a depth of 350 km beneath two seismic stations in North America in two different tectonic settings: the stable Canadian shield (station FFC) and the tectonically active southern Basin and Range Province (station TA-214A). In both cases, the lithosphere–asthenosphere boundary is clearly visible, and marked by a change in direction of the fast axis of anisotropy. Our study confirms that azimuthal anisotropy is a powerful tool for detecting layering in the upper mantle.

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: We apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian posterior model probability density function of 2-D seafloor resistivity as constrained by marine controlled source electromagnetic data. This density function of earth models conveys information on which parts of the model space are illuminated by the data. Whereas conventional gradient-based inversion approaches require subjective regularization choices to stabilize this highly non-linear and non-unique inverse problem and provide only a single solution with no model uncertainty information, the method we use entirely avoids model regularization. The result of our approach is an ensemble of models that can be visualized and queried to provide meaningful information about the sensitivity of the data to the subsurface, and the level of resolution of model parameters. We represent models in 2-D using a Voronoi cell parametrization. To make the 2-D problem practical, we use a source–receiver common midpoint approximation with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where the number of resistivity cells within a 2-D depth section is variable, as are their positions and geometries. Two synthetic studies demonstrate the algorithm's use in the appraisal of a thin, segmented, resistive reservoir which makes for a challenging exploration target. As a demonstration example, we apply our method to survey data collected over the Scarborough gas field on the Northwest Australian shelf.

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: We present a novel 3-D pre-stack Kirchhoff depth migration (PKDM) method for teleseismic receiver functions. The proposed algorithm considers the effects of diffraction, scattering and traveltime alteration caused by 3-D volumetric heterogeneities. It is therefore particularly useful for imaging complex 3-D structures such as dipping discontinuities, which is hard to accomplish with traditional methods. The scheme is based on the acoustic wave migration principle, where at each time step of the receiver function, the energy is migrated back to the ensemble of potential conversion points in the image, given a smooth 3-D reference model. Traveltimes for P and S waves are computed with an efficient eikonal solver, the fast marching method. We also consider elastic scattering patterns, where the amplitude of converted S waves depends on the angle between the incident P wave and the scattered S wave. Synthetic experiments demonstrate the validity of the method for a variety of dipping angle discontinuities. Comparison with the widely used common conversion point (CCP) stacking method reveals that our migration shows considerable improvement. For example, the effect of multiple reflections that usually produce apparent discontinuities is avoided. The proposed approach is practical, computationally efficient, and is therefore a potentially powerful alternative to standard CCP methods for imaging large-scale continental structure under dense networks.

Description: Ambient seismic noise tomography has proven to be a valuable tool for imaging 3D crustal shear velocity using surface waves; however, conventional two-stage inversion schemes are severely limited in their ability to properly quantify solution uncertainty and account for inhomogeneous data coverage. In response to these challenges, we developed a two-stage hierarchical, transdimensional, Bayesian scheme for inverting surface wave dispersion information for a 3D shear velocity structure and apply it to ambient seismic noise data recorded in Tasmania, southeast Australia. The key advantages of our Bayesian approach are that the number and distribution of model parameters are implicitly controlled by the data and that the standard deviation of the data noise is treated as an unknown in the inversion. Furthermore, the use of Bayesian inference — which combines prior model information and observed data to quantify the a posteriori probability distribution — means that model uncertainty information can be correctly propagated from the dispersion curves to the phase velocity maps and finally onward to the 1D shear models that are combined to form a composite 3D image. We successfully applied the new method to ambient noise dispersion data (1–12-s period) from Tasmania. The results revealed an east-dipping anomalously low shear velocity zone that extends to at least a 15-km depth and can be related to the accretion of oceanic crust onto the eastern margin of Proterozoic Tasmania during the mid-Paleozoic.

Description: Receiver functions are a powerful tool to isolate and interpret receiver-side structure effects in teleseismic seismic records. They are easily constructed by deconvolving one component of a seismogram by another. Deconvolution is the inverse of convolution, and hence can be mathematically viewed as an inverse problem. It is a numerically unstable procedure that needs to be stabilized (i.e. regularized). This points to a recurring problem in geophysical imaging: there is a trade-off between variance and resolution, where the user needs to arbitrarily define a level of compromise. Here we propose a novel misfit function for inversion of converted phases that avoids deconvolution. In this way, the choice of regularization parameters (e.g. water level, width of a low pass filter) is avoided, and statistics of data errors can be correctly accounted for. We use this misfit measure to construct a likelihood probability function and carry out a transdimensional Bayesian inversion for shear wave structure. After illustrating the method with a synthetic test, a real data application is shown where teleseismic signals recorded at HYB station (Hyderabad, India) and surface wave dispersion measurements are jointly inverted to provide a probabilistic 1-D seismic model beneath the station. The results help address the debate on the thickness of the lithosphere in this region. We show that the sharp negative velocity jump at 110 km that was previously interpreted as the lithosphere–asthenosphere boundary (LAB) is actually a mid-lithospheric discontinuity. The actual LAB is seen deeper as a milder gradient between 150 and 200 km.