Multi-scale uncertainty quantification in geostatistical seismic inversion

Geostatistical seismic inversion is commonly used to infer the spatial distribution of the subsurface petro-elastic properties by perturbing the model parameter space through iterative stochastic sequential simulations/co-simulations. The spatial uncertainty of the inferred petro-elastic properties is represented with the updated a posteriori variance from an ensemble of the simulated realizations. Within this setting, the large-scale geological (metaparameters) used to generate the petro-elastic realizations, such as the spatial correlation model and the global a priori distribution of the properties of interest, are assumed to be known and stationary for the entire inversion domain. This assumption leads to underestimation of the uncertainty associated with the inverted models. We propose a practical framework to quantify uncertainty of the large-scale geological parameters in seismic inversion. The framework couples geostatistical seismic inversion with a stochastic adaptive sampling and Bayesian inference of the metaparameters to provide a more accurate and realistic prediction of uncertainty not restricted by heavy assumptions on large-scale geological parameters. The proposed framework is illustrated with both synthetic and real case studies. The results show the ability retrieve more reliable acoustic impedance models with a more adequate uncertainty spread when compared with conventional geostatistical seismic inversion techniques. The proposed approach separately account for geological uncertainty at large-scale (metaparameters) and local scale (trace-by-trace inversion)

Power spectrum multipoles on the curved sky: an application to the 6-degree Field Galaxy Survey

The peculiar velocities of galaxies cause their redshift-space clustering to depend on the angle to the line-of-sight, providing a key test of gravitational physics on cosmological scales. These effects may be described using a multipole expansion of the clustering measurements. Focussing on Fourier-space statistics, we present a new analysis of the effect of the survey window function, and the variation of the line-of-sight across a survey, on the modelling of power spectrum multipoles. We determine the joint covariance of the Fourier-space multipoles in a Gaussian approximation, and indicate how these techniques may be extended to studies of overlapping galaxy populations via multipole cross-power spectra. We apply our methodology to one of the widest-area galaxy redshift surveys currently available, the 6-degree Field Galaxy Survey, deducing a normalized growth rate f*sigma_8(z=0.06) = 0.38 +/- 0.12 in the low-redshift Universe, in agreement with previous analyses of this dataset using different techniques. Our framework should be useful for processing future wide-angle galaxy redshift surveys.Comment: 17 pages, 7 figures, version accepted by MNRA

A hybrid approach for predicting the distribution of vibro-acoustic energy in complex built-up structures

Finding the distribution of vibro-acoustic energy in complex built-up structures in the mid-to-high frequency regime is a difficult task. In particular, structures with large variation of local wavelengths and/or characteristic scales pose a challenge referred to as the mid-frequency problem. Standard numerical methods such as the finite element method (FEM) scale with the local wavelength and quickly become too large even for modern computer architectures. High frequency techniques, such as statistical energy analysis (SEA), often miss important information such as dominant resonance behaviour due to stiff or small scale parts of the structure. Hybrid methods circumvent this problem by coupling FEM/BEM and SEA models in a given built-up structure. In the approach adopted here, the whole system is split into a number of subsystems which are treated by either FEM or SEA depending on the local wavelength. Subsystems with relative long wavelengths are modelled using FEM. Making a diffuse field assumption for the wave fields in the short wave length components, the coupling between subsystems can be reduced to a weighted random field correlation function. The approach presented results in an SEA-like set of linear equations which can be solved for the mean energies in the short wavelength subsystems

Asteroseismology of Solar-Type and Red-Giant Stars

We are entering a golden era for stellar physics driven by satellite and telescope observations of unprecedented quality and scope. New insights on stellar evolution and stellar interiors physics are being made possible by asteroseismology, the study of stars by the observation of natural, resonant oscillations. Asteroseismology is proving to be particularly significant for the study of solar-type and red-giant stars. These stars show rich spectra of solar-like oscillations, which are excited and intrinsically damped by turbulence in the outermost layers of the convective envelopes. In this review we discuss the current state of the field, with a particular emphasis on recent advances provided by the Kepler and CoRoT space missions and the wider significance to astronomy of the results from asteroseismology, such as stellar populations studies and exoplanet studies.Comment: The following paper will appear in the 2013 volume of Annual Reviews of Astronomy and Astrophysics (88 pages, 7 figures; references updated; further corrections to typos during galley-proof review

Phoneme and sentence-level ensembles for speech recognition

We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the phoneme level and one at the utterance level, with a phoneme-level bagging scheme. We control for many parameters and other choices, such as the state inference scheme used. In an unbiased experiment, we clearly show that the gain of boosting methods compared to a single hidden Markov model is in all cases only marginal, while bagging significantly outperforms all other methods. We thus conclude that bagging methods, which have so far been overlooked in favour of boosting, should be examined more closely as a potentially useful ensemble learning technique for speech recognition

A Subband-Based SVM Front-End for Robust ASR

This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using ensemble methods are addressed. The proposed front-end is compared with state-of-the-art ASR front-ends in terms of robustness to additive noise and linear filtering. Experiments performed on the TIMIT phoneme classification task demonstrate the benefits of the proposed subband based SVM front-end: it outperforms the standard cepstral front-end in the presence of noise and linear filtering for signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed front-end with a conventional front-end such as MFCC yields further improvements over the individual front ends across the full range of noise levels