Éditeur(s) : Amer Geophysical Union
Résumé : Seismic noise is an indirect source of information on ocean waves. Using a model of noise generation and propagation, seismic stations can be separated into those that are mostly sensitive to local sea states, and those that integrate sources from a large oceanic area. The model also provides a classification of noise-generating sea states into three classes. The analysis of Central California seismic noise data, well correlated with local waves, reveals that class I events dominate in summer, caused by a single wind-sea system, and for which ocean wave spectral levels are proportional to seismic spectral levels to an exponent b similar or equal to 0.9. In winter, noise is dominated by class II generation, for which coastal reflection is important, with a wave spectral density roughly proportional to the seismic spectral density to an exponent b similar or equal to 0.7. Sporadic events of class III probably produce some of the strongest noise events in Central California and need to be properly screened. These events are caused by opposed wave systems that are usually the wind-sea and a swell. This noise classification can be used to improve on the correlation between measured and estimated wave heights(up to r = 0.93 for daily averages). For other locations, where remote oceanic sources are recorded, a significant wave height estimated from the seismic noise compares well with area-averaged satellite data or wave model results(r > 0.85 for daily averages). These analyses pave the way for quantitative uses of seismic records, including the reconstruction of past wave climates, and the calibration of wave hindcasts.
Journal Of Geophysical Research-oceans (0148-0227) (Amer Geophysical Union), 2012-05 , Vol. 117 , N. C05002 , P. 19 pp.

Droits : 2012 AGU
http://archimer.ifremer.fr/doc/00083/19443/17051.pdf
DOI:10.1029/2011JC007449
http://archimer.ifremer.fr/doc/00083/19443/

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with image processing. This study takes place in a segmentation process based on texture analysis. We use the multifractal ap-proach to characterize the textures. More precisely we study a particular multi-fractal spectrum called the large deviation spectrum. We consider two statistical methods to numerically compute this spectrum. The resulting spectrum, com-puted by both methods over an image, is a one dimension spectrum. In the scope of this article, we extend these methods in order to obtain a two dimen-sions spectrum which could be assimilated to an image. This 2D spectrum al-lows a local characterization of the image singularities while a 1D spectrum is a global characterization. Moreover, the computation of the spectrum requires the use of a measure. We introduce here a pre processing based on the gradient to improve the measure. We show results on both synthetic and real world images. Finally, we remark that the resulting 2D spectrum is close to the resulting im-age of an edge detection process while edge detection using one dimension spectrum requires post processing methods. This statement will be used for fu-ture works.
SITIS Proceedings
Hammamet, Tunisia

hal-00602273
https://hal.archives-ouvertes.fr/hal-00602273

Éditeur(s) : HAL CCSD Intech
Résumé : International audience
The massive integration of sustainable energies into electrical grids (non-interconnectedor connected) is a major problem due to their stochastic character revealed by strongfluctuations at all scales. In this paper, the scaling behaviour or power law correlationsand the nature of scaling behaviour of sustainable resource data such as flow velocity,atmospheric wind speed, solar global solar radiation and sustainable energy such as,wind power output, are highlighted. For the first time, Fourier power spectral densitiesare estimated for each dataset. We show that the power spectrum densities obtained areclose to the 5/3 Kolmogorov spectrum. Furthermore, the multifractal and intermittentproperties of sustainable resource and energy data have been revealed by the concavityof the scaling exponent function. The proposed analysis frame allows a full descriptionof fluctuations of processes considered. A good knowledge of the dynamic of fluctua-tions is crucial to management of the integration of sustainable energies into a grid.
Sustainable Energy - Technological Issues, Applications and Case Studies

hal-01528087
https://hal.archives-ouvertes.fr/hal-01528087
DOI : 10.5772/106341

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
In order to reveal the structural properties at small scale within a geological unit apparently homogeneous at large scale, we explore the stochastic components of petrophysical measurements recorded in a borehole drilled through the Cretaceous siliclastic units of the South-eastern Basin of France. First, we analyse the instantaneous fluctuations of gamma ray log measurements searching for scaling laws in its power spectrum. Power spectra indicate a scaling behaviour with average exponent beta a values mostly falling in the range 1
ISSN: 0920-4105

hal-00496425
https://hal.archives-ouvertes.fr/hal-00496425
DOI : 10.1016/j.petrol.2009.11.001

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
In this paper, we have presented a spectral and a multifractal analysis performed on 412 time series of wind speed data each of duration of 350 s and sampled at 20 Hz. The average spectrum for the wind speed displays a scaling behavior, in the inertial range, over two decades, with b ¼ 1:68 close to the Kolmogorov value À 5/3. A multifractal analysis has been motivated by the presence of scaling invariance in data set. Then we have considered their scaling properties in the framework of fully developed turbulence and multifractal cascades. The results obtained for wind speed confirm that the exponent scaling function z V ðqÞ is nonlinear and concave. This exponent characterizes the scaling functions in the inertial range indicating that the wind speed is intermittent and multifractal. Moreover the theoretical quadratic relation for lognormal multifractals is well fitted. We investigate the consequence for wind energy production: we generate stochastic simulations of a multifractal random walk, and using a power curve derived from experimental data, we generate the associated power time series. We show that, due to the saturation of the power curve for large speed values, when the input time series (turbulent wind speed) is multifractal, the output can be almost monofractal.
ISSN: 0167-6105

hal-01527886
https://hal.archives-ouvertes.fr/hal-01527886
DOI : 10.1016/j.jweia.2012.06.002

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with texture classification using a multifractal approach. More precisely we analyse the singularity/regularity exponent that compose the textures because they theoretically carry most of the information. The analysis is made using the Legendre spectrum. Then a parameter vector is computed to describe this spectrum in order to classify the textures with an unsupervised k-means classifier. The resulting algorithm is evaluated against a classification directly based on the textures.
CAS Proceedings
GOSIER, France

hal-00634750
https://hal.archives-ouvertes.fr/hal-00634750

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
In this paper, we have presented a spectral and a multifractal analysis performed on 412 time series of wind speed data each of duration of 350 s and sampled at 20 Hz. The average spectrum for the wind speed displays a scaling behavior, in the inertial range, over two decades, with β=1.68 close to the Kolmogorov value −5/3. A multifractal analysis has been motivated by the presence of scaling invariance in data set. Then we have considered their scaling properties in the framework of fully developed turbulence and multifractal cascades. The results obtained for wind speed confirm that the exponent scaling function ζV(q) is nonlinear and concave. This exponent characterizes the scaling functions in the inertial range indicating that the wind speed is intermittent and multifractal. Moreover the theoretical quadratic relation for lognormal multifractals is well fitted. We investigate the consequence for wind energy production: we generate stochastic simulations of a multifractal random walk, and using a power curve derived from experimental data, we generate the associated power time series. We show that, due to the saturation of the power curve for large speed values, when the input time series (turbulent wind speed) is multifractal, the output can be almost monofractal.
ISSN: 0167-6105

hal-00779049
https://hal.archives-ouvertes.fr/hal-00779049
DOI : 10.1016/j.jweia.2012.06.002

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with texture classification using a multifractal approach. More precisely we analyse the singularity/regularity exponent that compose the textures because they theoretically carry most of the information. The analysis is made using the Legendre spectrum. Then a parameter vector is computed to describe this spectrum in order to classify the textures with an unsupervised k-means classifier. The resulting algorithm is evaluated against a classification directly based on the textures.
ICSP Proceedings
Beijing, China

hal-00602270
https://hal.archives-ouvertes.fr/hal-00602270