Upper crustal structure of Madeira Island revealed from ambient noise tomography

We present the first image of the Madeira upper crustal structure, using ambient seismic noise tomography. 16 months of ambient noise, recorded in a dense network of 26 seismometers deployed across Madeira, allowed reconstructing Rayleigh wave Green's functions between receivers. Dispersion analysis was performed in the short period band from 1.0 to 4.0 s. Group velocity measurements were regionalized to obtain 20 tomographic images, with a lateral resolution of 2.0 km in central Madeira. Afterwards, the dispersion curves, extracted from each cell of the 2D group velocity maps, were inverted as a function of depth to obtain a 3D shear wave velocity model of the upper crust, from the surface to a depth of 2.0 km. The obtained 3D velocity model reveals features throughout the island that correlates well with surface geology and island evolution. (C) 2015 Elsevier B.V. All rights reserved.

Upper crustal structure of Madeira Island revealed from ambient noise tomography

We present the first image of the Madeira upper crustal structure, using ambient seismic noise tomography. 16 months of ambient noise, recorded in a dense network of 26 seismometers deployed across Madeira, allowed reconstructing Rayleigh wave Green's functions between receivers. Dispersion analysis was performed in the short period band from 1.0 to 4.0 s. Group velocity measurements were regionalized to obtain 20 tomographic images, with a lateral resolution of 2.0 km in central Madeira. Afterwards, the dispersion curves, extracted from each cell of the 2D group velocity maps, were inverted as a function of depth to obtain a 3D shear wave velocity model of the upper crust, from the surface to a depth of 2.0 km. The obtained 3D velocity model reveals features throughout the island that correlates well with surface geology and island evolution. (C) 2015 Elsevier B.V. All rights reserved.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.