Divide and Conquer Partition for Fourier Reconstruction Sparse Inversion with its Applications

A partition method, with an efficient divide and conquer partition strategy, for the non-uniform sampling signal reconstruction based on Fourier reconstruction sparse inversion (FRSI) is developed. The novel partition FRSI(P-FRSI) is motivated by the observation that the partition processing of multi-dimensional signals can reduce the reconstruction difficulty and save the reconstruction time. Moreover, it is helpful to choose suitable reconstruction parameters. The P-FRSI employs divide and conquer strategy, and the signal is firstly partitioned into some blocks. Following that, traditional FRSI is applied to reconstruct signals in each block. We adopt linear or nonlinear superposition to determine the weight coefficients during integrating these blocks. Finally, P-FRSI is applied to two-dimensional seismic signal reconstruction. The superiority of the new method over conventional FRSI is demonstrated by numerical reconstruction experiments

Acoustic Impedance Inversion from Seismic Imaging Profiles Using Self Attention U-Net

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismic data. Artificial neural network have been used to solve nonlinear inverse problems in recent years. This research obtained an acoustic impedance profile by feeding seismic profile and background impedance into a well-trained self-attention U-Net. The U-Net got convergence by appropriate iteration, and the output predicted the impedance profiles in the test. To value the quality of predicted profiles from different perspectives, e.g., correlation, regression, and similarity, we used four kinds of indexes. At the same time, our results were predicted by conventional methods (e.g., deconvolution with recursive inversion, and TV regularization) and a 1D neural network was calculated in contrast. Self-attention U-Net showed to be robust to noise and does not require prior knowledge. Furthermore, spatial continuity is also better than deconvolution, regularization, and 1D deep learning methods in contrast. The U-Net in this paper is a type of full convolutional neural network, so there are no limits to the shape of the input. Based on this, a large impedance profile can be predicted by U-Net, which is trained by a patchy training dataset. In addition, this paper applied the proposed method to the field data obtained by the Ceduna survey without any label. The predictions prove that this well-trained network could be generalized from synthetic data to field data

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network

Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current engineering. However, due to the large manifold error of the underwater sonar array and the complexity of ensuring long-term signal stability, traditional high-resolution array signal processing methods are not ideal for practical underwater applications. In conventional beamforming methods, when the signal-to-noise ratio is lower than −43.05 dB, the general direction can only be vaguely identified in the general direction. To address the above challenges, this paper proposes a beamforming method based on a deep neural network. Through preprocessing, the space-time domain of the target sound signal is converted into two-dimensional data in the angle-time domain. Subsequently, we trained the network with enough sample datasets. Finally, high-resolution recognition and prediction of two-dimensional images are realized. The results of the test dataset in this paper demonstrate the effectiveness of the proposed method, with a minimum signal-to-noise ratio of −48 dB

Epidemiology and Evolutionary Characteristics of the Porcine Reproductive and Respiratory Syndrome Virus in China between 2006 and 2010▿†

In 2006, an emerging highly pathogenic strain of porcine reproductive and respiratory syndrome virus (PRRSV), which causes continuous high fever and a high proportion of deaths in vaccinated pigs of all ages, broke out in mainland China and spread rapidly to neighboring countries. To examine the epidemiology and evolutionary characteristics of Chinese PRRSV after the 2006 outbreak, we tested 2,981 clinical samples collected from 2006 to 2010 in China, determined 153 Nsp2 sequences and 249 ORF5 sequences, and analyzed the epidemiology and genetic diversity of Chinese PRRSV. Our results showed that the percentage of PRRSV-positive specimens collected from sick pigs averaged 60.85% in the past 5 years and that the highly pathogenic PRRSV has become the dominant strain in China. Furthermore, a reemerging strain which apparently evolved from the highly pathogenic PRRSV strain in 2006 appeared to be widely prevalent in China from 2009 onwards. Sequence analyses revealed that the hypervariable region of Nsp2 in most of the isolates contained a discontinuous deletion equivalent to 30 amino acids, along with other types of deletions. Extensive amino acid substitutions in the GP5 sequence translated from ORF5 were found, particularly in the potential neutralization epitope and the N-glycosylation sites. Our results suggest that Chinese PRRSV has undergone rapid evolution and can circumvent immune responses induced by currently used vaccines. Information from this study will help in understanding the evolutionary characteristics of Chinese PRRSV and assist ongoing efforts to develop and use PRRSV vaccines in the future