ALBERT

Description: Secondary microseism sources are pressure fluctuations close to the ocean surface. They generate acoustic P waves that propagate in water down to the ocean bottom where they are partly reflected and partly transmitted into the crust to continue their propagation through the Earth. We present the theory for computing the displacement power spectral density of secondary microseism P waves recorded by receivers in the far field. In the frequency domain, the P -wave displacement can be modeled as the product of (1) the pressure source, (2) the source site effect that accounts for the constructive interference of multiply reflected P waves in the ocean, (3) the propagation from the ocean bottom to the stations and (4) the receiver site effect. Secondary microseism P waves have weak amplitudes, but they can be investigated by beamforming analysis. We validate our approach by analysing the seismic signals generated by typhoon Ioke (2006) and recorded by the Southern California Seismic Network. Backprojecting the beam onto the ocean surface enables to follow the source motion. The observed beam centroid is in the vicinity of the pressure source derived from the ocean wave model WAVEWATCH III R . The pressure source is then used for modeling the beam and a good agreement is obtained between measured and modeled beam amplitude variation over time. This modeling approach can be used to invert P -wave noise data and retrieve the source intensity and lateral extent.

Description: An elastic wavefield propagating in an inhomogeneous elastic medium is only sensitive in an effective way to inhomogeneities much smaller than its minimum wavelength. The corresponding effective medium, or homogenized medium, can be computed thanks to the non-periodic homogenization technique. In the seismic imaging context, limiting ourselves to layered media, we numerically show that a tomographic elastic model which results of the inversion of limited frequency band seismic data is an homogenized model. Moreover, we show that this homogenized model is the same as the model that can be computed with the non-periodic homogenization technique. We first introduce the notion of residual homogenization, which is computing the effective properties of the difference between a reference model and a ‘real’ model. This is necessary because most imaging technique parametrizations use a reference model that often contains small scales, such as elastic discontinuities. We then use a full-waveform inversion method to numerically show that the result of the inversion is indeed the homogenized residual model. The full-waveform inversion method used here has been specifically developed for that purpose. It is based on the iterative Gauss–Newton least-square non-linear optimization technique, using full normal mode coupling to compute the partial Hessian and gradient. The parametrization has been designed according to the residual homogenized parameters allowing to build a real multiscale inversion with progressive frequency band enrichment along the Gauss–Newton iterations.

Description: We present a new upper-mantle tomographic model derived solely from hum seismic data. Phase correlograms between station pairs are computed to extract phase-coherent signals. Correlograms are then stacked using the time–frequency phase-weighted stack method to build-up empirical Green's functions. Group velocities and uncertainties are measured in the wide period band of 30–250 s, following a resampling approach. Less data are required to extract reliable group velocities at short periods than at long periods, and 2 yr of data are necessary to measure reliable group velocities for the entire period band. Group velocities are first regionalized and then inverted versus depth using a simulated annealing method in which the number and shape of splines that describes the S -wave velocity model are variable. The new S -wave velocity tomographic model is well correlated with models derived from earthquakes in most areas, although in India, the Dharwar craton is shallower than in other published models.

Description: 〈span〉〈div〉ABSTRACT〈/div〉Interstation correlation is the basic operation in seismic noise and coda‐wave interferometry for signal extraction in imaging and monitoring applications. Conventional cross‐correlations evaluate the similarity between two signals along lag time, and they are efficiently computed by the fast Fourier transform (FFT), valuable to manage the large data volumes that ambient noise applications demand. The phase cross‐correlation (PCC) method contributes to increase convergence, a key issue in seismic ambient noise imaging and monitoring; however, it is much more computationally demanding. PCC evaluates similarity by subtracting the modulus of the sum and difference of the instantaneous phase of two signals. We introduce solutions to dramatically reduce the high‐computational cost of PCC. We show that PCC can be rewritten as a complex cross‐correlation and computed by the FFT when the moduli are raised to the power of 2, and we demonstrate PCC can improve waveform coherence and increase convergence compared with the default processing flow of 1‐bit amplitude normalization and standard cross‐correlation. Moreover, we develop a graphics processing unit implementation to accelerate computations when using powers other than 2 and particularly when using the power of 1. Finally, we extract Rayleigh‐ and body‐wave signals from many years of data from seismic stations distributed worldwide using PCC without a significant increase in computational cost compared with conventional cross‐correlation.〈/span〉

Description: 〈span〉〈div〉SUMMARY〈/div〉We use seismic noise cross-correlations to obtain a 3-D tomography model of 〈span〉SV〈/span〉-wave velocities beneath the western Indian Ocean, in the depth range of the oceanic crust and uppermost mantle. The study area covers 2000 × 2000 km〈sup〉2〈/sup〉 between Madagascar and the three spreading ridges of the Indian Ocean, centred on the volcanic hotspot of La Réunion. We use seismograms from 38 ocean bottom seismometers (OBSs) deployed by the RHUM-RUM project and 10 island stations on La Réunion, Madagascar, Mauritius, Rodrigues, and Tromelin. Phase cross-correlations are calculated for 1119 OBS-to-OBS, land-to-OBS, and land-to-land station pairs, and a phase-weighted stacking algorithm yields robust group velocity measurements in the period range of 3–50 s. We demonstrate that OBS correlations across large interstation distances of 〉2000 km are of sufficiently high quality for large-scale tomography of ocean basins. Many OBSs yielded similarly good group velocity measurements as land stations. Besides Rayleigh waves, the noise correlations contain a low-velocity wave type propagating at 0.8–1.5 km s〈sup〉−1〈/sup〉 over distances exceeding 1000 km, presumably Scholte waves travelling through seafloor sediments. The 100 highest-quality group velocity curves are selected for tomographic inversion at crustal and lithospheric depths. The inversion is executed jointly with a data set of longer-period, Rayleigh-wave phase and group velocity measurements from earthquakes, which had previously yielded a 3-D model of Indian Ocean lithosphere and asthenosphere. Robust resolution tests and plausible structural findings in the upper 30 km validate the use of noise-derived OBS correlations for adding crustal structure to earthquake-derived tomography of the oceanic mantle. Relative to crustal reference model CRUST1.0, our new shear-velocity model tends to enhance both slow and fast anomalies. It reveals slow anomalies at 20 km depth beneath La Réunion, Mauritius, Rodrigues Ridge, Madagascar Rise, and beneath the Central Indian spreading ridge. These structures can clearly be associated with increased crustal thickness and/or volcanic activity. Locally thickened crust beneath La Réunion and Mauritius is probably related to magmatic underplating by the hotspot. In addition, these islands are characterized by a thickened lithosphere that may reflect the depleted, dehydrated mantle regions from which the crustal melts where sourced. Our tomography model is available as electronic supplement.〈/span〉

Description: 〈span〉〈div〉Summary〈/div〉Primary microseism is the less studied seismic background vibration of the Earth. Evidence points to sources caused by ocean gravity waves coupling with the seafloor topography. As a result, these sources should be in water depth smaller than the wavelength of ocean waves. Using a state-of-the-art ocean wave model, we carry out the first global-scale seismic modeling of the vertical-component power spectral density of primary microseisms. Our modeling allows us to infer that the observed weak seasonality of primary microseisms in the southern hemisphere corresponds to a weak local seasonality of the sources. Moreover, a systematic analysis of the source regions that mostly contribute to each station reveals that stations on both the East and West sides of the North Atlantic Ocean are sensitive to frequency-dependent source regions. At low frequency (i.e., 0.05 Hz), the dominant source regions can be located thousands of kilometers away from the stations. This observation suggests that identifying the source regions of primary microseisms as the closest coasts can be misleading.〈/span〉

Description: 〈span〉〈div〉Summary〈/div〉Secondary microseismic sources emit seismic waves over long time spans. Reoccurring signals with similar slowness and frequency therefore arrive at seismic arrays. Blind source separation techniques can be used to identify and isolate such reoccurring signals from other signals and from diffuse seismic noise. Along these lines, we use non-negative matrix factorization as blind source separation technique to decompose continuous seismic array records. We model the recorded energy as a mixture of a few components with static slowness-frequency and time dependent amplitudes. Components and amplitudes are fitted to optimally explain the recorded seismic energy over time. These components represent secondary microseismic signals with quasi-static slowness-frequency vector and fluctuating amplitude. Each fitted component reveals the geographical origin (through the slowness-frequency vector) and time evolution of an active secondary microseism with high precision because it is separated from other signals and diffuse seismic noise. Furthermore, relative travel times can be automatically extracted for the signals that correspond to a specific component that can potentially be used in tomographic studies. We show two examples of seismic signals that were extracted with this technique, one focusing on P-waves from the typhoons Goni and Atsani, and another showing secondary microseism PKP signals from typhoon Glenda.〈/span〉

Description: Ocean waves activity is a major source of microvibrations that travel through the solid Earth, known as microseismic noise and recorded worldwide by broadband seismometers. Analysis of microseismic noise in continuous seismic records can be used to investigate noise sources in the oceans such as storms, and their variations in space and time, making possible the regional and global-scale monitoring of the wave climate. In order to complete the knowledge of the Atlantic and Pacific oceans microseismic noise sources, we analyse 1 yr of continuous data recorded by permanent seismic stations located in the Indian Ocean basin. We primarily focus on secondary microseisms (SM) that are dominated by Rayleigh waves between 6 and 11 s of period. Continuous polarization analyses in this frequency band at 15 individual seismic stations allow us to quantify the number of polarized signal corresponding to Rayleigh waves, and to retrieve their backazimuths ( BAZ ) in the time–frequency domain. We observe clear seasonal variations in the number of polarized signals and in their frequencies, but not in their BAZ that consistently point towards the Southern part of the basin throughout the year. This property is very peculiar to the Indian Ocean that is closed on its Northern side, and therefore not affected by large ocean storms during Northern Hemisphere winters. We show that the noise amplitude seasonal variations and the backazimuth directions are consistent with the source areas computed from ocean wave models.