Accurate and fast magnitude determination for large, shallow earthquakes is of key importance for post-seismic response and tsumami alert purposes. When no local real-time data are available, which is today the case for most subduction earthquakes, the first information comes from teleseismic body waves. Standard body-wave methods give accurate magnitudes for earthquakes up to Mw= 7–7.5. For larger earthquakes, the analysis is more complex, because of the non-validity of the point-source approximation and of the interaction between direct and surface-reflected phases. The latter effect acts as a strong high-pass filter, which complicates the magnitude determination. We here propose an automated deconvolutive approach, which does not impose any simplifying assumptions about the rupture process, thus being well adapted to large earthquakes. We first determine the source duration based on the length of the high frequency (1–3 Hz) signal content. The deconvolution of synthetic double-couple point source signals—depending on the four earthquake parameters strike, dip, rake and depth—from the windowed real data body-wave signals (including P, PcP, PP, SH and ScS waves) gives the apparent source time function (STF). We search the optimal combination of these four parameters that respects the physical features of any STF: causality, positivity and stability of the seismic moment at all stations. Once this combination is retrieved, the integration of the STFs gives directly the moment magnitude. We apply this new approach, referred as the SCARDEC method, to most of the major subduction earthquakes in the period 1990–2010. Magnitude differences between the Global Centroid Moment Tensor (CMT) and the SCARDEC method may reach 0.2, but values are found consistent if we take into account that the Global CMT solutions for large, shallow earthquakes suffer from a known trade-off between dip and seismic moment. We show by modelling long-period surface waves of these events that the source parameters retrieved using the SCARDEC method explain the observed surface waves as well as the Global CMT parameters, thus confirming the existing trade-off. For some well-instrumented earthquakes, our results are also supported by independent studies based on local geodetic or strong motion data. This study is mainly focused on moment determination. However, the SCARDEC method also informs us about the focal mechanism and source depth, and can be a starting point to study systematically the complexity of the STF

International audienceWe propose a class of spherical wavelet bases for the analysis of geophysical models and for the tomographic inversion of global seismic data. Its multiresolution character allows for modelling with an effective spatial resolution that varies with position within the Earth. Our procedure is numerically efficient and can be implemented with parallel computing. We discuss two possible types of discrete wavelet transforms in the angular dimension of the cubed sphere. We describe benefits and drawbacks of these constructions and apply them to analyse the information in two published seismic wave speed models of the mantle, using the statistics of wavelet coefficients across scales. The localization and sparsity properties of wavelet bases allow finding a sparse solution to inverse problems by iterative minimization of a combination of the ℓ2 norm of the data residuals and the ℓ1 norm of the model wavelet coefficients. By validation with realistic synthetic experiments we illustrate the likely gains from our new approach in future inversions of finite-frequency seismic dat

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[1] We present a realistic application of an inversion scheme for global seismic tomography that uses as prior information the sparsity of a solution, defined as having few nonzero coefficients under the action of a linear transformation. In this paper, the sparsifying transform is a wavelet transform. We use an accelerated iterative soft-thresholding algorithm for a regularization strategy, which produces sparse models in the wavelet domain. The approach and scheme we present may be of use for preserving sharp edges in a tomographic reconstruction and minimizing the number of features in the solution warranted by the data. The method is tested on a data set of time delays for finite-frequency tomography using the USArray network, the first application in global seismic tomography to real data. The approach presented should also be suitable for other imaging problems. From a comparison with a more traditional inversion using damping and smoothing constraints, we show that (1) we generally retrieve similar features, (2) fewer nonzero coefficients under a properly chosen representation (such as wavelets) are needed to explain the data at the same level of root-mean-square misfit, (3) the model is sparse or compressible in the wavelet domain, and (4) we do not need to construct a heterogeneous mesh to capture the available resolution.

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International audienceSeismic moment and the corresponding moment magnitude Mw are classically obtained from the spectrum of far-field body waves. Near-field records are generally not used for that purpose, particularly in the case of large earthquakes because different types of wave arrive simultaneously, preventing the definition of a simple relation between the seismic moment and the spectrum. We developed an original method to determine Mw from the displacement spectra of near-field records. The spectral amplitude at low frequency obtained from the real records is compared to that of synthetic records computed using kinematic rupture models scaled with Mw. Synthetic records are computed and averaged for various fault orientations and for epicentral distances ranging from 1 to 100 km. The initial portion of the spectrum affected by baseline shift in the acceleration records is automatically identified and removed by high-pass filtering using a cutoff frequency adapted to each station. The synthetic spectral values as a function of moment magnitude, epicentral distance, and filtering are computed only once and stored in tables. The spectral amplitudes of the real records are simply interpolated in the tables of synthetic data, allowing a fast determination of Mw. The method has been validated using 22 shallow earthquakes (depth<50 km) with magnitude ranging from 3.9 to 7.7. We show that a window of 80 sec of signal after the earthquake origin time provides robust values of Mw for the whole magnitude range considered here. Shorter time windows may be used but with Mw underestimated for large events. The method is well suited for near real-time fast determination of Mw