Full Waveform Inversion (FWI) success depends on producing seamless updates of the short-and longwavelength features missing in the starting velocity model while avoiding cycle skipping. The use of cross-correlation gradients in FWI can lead to updates with the reflectivity imprint (high-wavenumbers) before the long wavelength updates have been constructed. In addition, the use of L2norm to measure the data misfit is prone to cycle skipping. This may conduct to a local-minimum if the data lacks of low frequency information and/or the initial model is far from the true earth model. We offer a solution to these two FWI fundamental problems that combines a robust implementation of the velocity sensitivity kernel and the optimal transport norm to measure the data misfit. The new scheme can retrieve the long wavelength updates and reduce the cycle skipping problem. The velocity kernel eliminates the migration isochrones emphasizing the longwavelength updates produced by the diving waves and the "rabbit ears" provided by reflections. The optimal transport norm accentuates those long-wavelength updates while minimizing the cycle skipping. We demonstrate the advantages of our implementation on synthetic and field data examples.
We propose a novel method for improving wireless network capacity by resorting to a so-called "multi-cell access" (MCA) scheme. An MCA scheme is reminiscent of the conventional multiple access problem in multi-user networks. However, in an MCA scheme cells (rather than users) compete for access. Furthermore, an example of such a scheme is introduced whereby a cell obtains a credit that is a function of the channel gain of its scheduled user, and it is allowed to transmit with a probability that is computed based on the credit value. The network capacity under this scheme is analyzed and we propose an optimization method in the case where the probability distribution for access is binary.
Abstract-The cost of sonars scales to the number of active elements. Therefore, it is favorable to reduce the number of elements without loss in imaging quality. This is a combinatorial problem, but of such a large dimension, even for small arrays, that an exhaustive search is futile.In this work, we have found layout-optimized sparsed cylindrical arrays, i.e. arrays with binary weights. In underwater applications, with demands for omni-directional imaging, cylindrical arrays have shown to have beneficial qualities, and are commonly used in fishery.We have found that to optimize the layout of the cylindrical array it is sufficient to optimize a small sub-array. With a reasonable number of elements in the elevation direction, finding the optimal array is then possible through an exhaustive search. The cylindrical array was then sparsed so that each line of elements in the elevation direction were chosen to correspond to the sub-array array.
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