Acquisitions of the Sentinel-1 satellite are processed and comprehensively analyzed to investigate the ground displacement during a three-year period above a double gas storage site (Lussagnet and Izaute) in Southwestern France. Despite quite low vertical displacements (between 4 and 8 mm) compared to the noise level, the cyclic motion reflects the seasonal variations due to charge and discharge during summer and winter periods, respectively. We can simulate the ground deformation at both storage sites by a simple mechanical model. However, ground movements of low-magnitude may be also induced by natural factors, such as the temperature or the soil moisture. Using a wavelet-based analysis, we show there is a soil expansion in the Lussagnet zone that contrasts both in phase and period with the seasonal deformation and that is linked to the surface soil moisture measured by the SMOS satellite. This other displacement is consistent with the water infiltration in the unsaturated zone followed by the swelling of a clay layer. This work reveals the combination of two different processes driving the ground displacement with the same order of magnitude (about 6 mm), namely the pressure variation of a deep gas reservoir and the swelling/shrinking of the shallow subsurface.
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http://dx.doi.org/10.1038/s41598-019-45302-z | DOI Listing |
Numer Funct Anal Optim
November 2024
MaLGa Center, Department of Mathematics, University of Genoa, Genova , Italy.
In this work, we present and study Continuous Generative Neural Networks (CGNNs), namely, generative models in the continuous setting: the output of a CGNN belongs to an infinite-dimensional function space. The architecture is inspired by DCGAN, with one fully connected layer, several convolutional layers and nonlinear activation functions. In the continuous setting, the dimensions of the spaces of each layer are replaced by the scales of a multiresolution analysis of a compactly supported wavelet.
View Article and Find Full Text PDFBoundary Layer Meteorol
April 2024
Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697 USA.
Wildland fire-atmosphere interaction generates complex turbulence patterns, organized across multiple scales, which inform fire-spread behaviour, firebrand transport, and smoke dispersion. Here, we utilize wavelet-based techniques to explore the characteristic temporal scales associated with coherent patterns in the measured temperature and the turbulent fluxes during a prescribed wind-driven (heading) surface fire beneath a forest canopy. We use temperature and velocity measurements from tower-mounted sonic anemometers at multiple heights.
View Article and Find Full Text PDFJ Electromyogr Kinesiol
February 2025
Hungarian University of Sports Science, Department of Kinesiology, Budapest, Hungary; University of Groningen, Groningen, University Medical Center Groningen (UMCG), Department of Human Movement Science, Groningen, The Netherlands; Department of Sport Biology, Institute of Sport Sciences and Physical Education, University of Pécs, Pécs, Hungary.
Mental fatigue can affect cognitive function and interfere with motor performance. We examined if mental fatigue affected gait through age-specific modulation of wavelet-based time-frequency intermuscular beta-band coherence in muscles while walking on a treadmill at 1.2 m·s-1.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
November 2024
Department of Nuclear Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Purpose: Recent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverage leading to total-body PET imaging. This encouraging breakthrough brings the hope of ultra-low dose PET imaging equivalent to transatlantic flight with the assistance of deep learning (DL)-based methods. However, conventional DL approaches face limitations in addressing the heterogeneous domain of PET imaging.
View Article and Find Full Text PDFSci Rep
October 2024
Department of Nephrology, Jinshan District Central Hospital affiliated to Shanghai University of Medicine & Health Sciences, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, People's Republic of China.
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