发帖
雷达卡
摘要翻译：
时频分析在许多领域得到了成功的应用。然而，传统的短时傅立叶变换和Cohen分布等方法存在分辨率低或交叉项干扰等问题。为了解决这些问题，我们提出了一种新的稀疏时频分析模型，该模型利用LP-拟诺尔约束，能够在频域内拟合稀疏先验知识。在该模型中，我们将短时截断数据看作稀疏表示的观测，并设计了字典矩阵，建立了短时测量与稀疏谱之间的关系。基于该关系和LP-拟序可行域，建立了该模型。该模型采用乘子交替方向法(ADMM)求解。对几种理论信号进行了实验，并将其应用于地震信号的谱分解，结果表明，该方法比现有的时频方法能得到更高的时频分布。因此，该方法对油藏勘探具有重要意义。
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英文标题：
《Seismic signal sparse time-frequency analysis by Lp-quasinorm constraint》
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作者：
Yingpin Chen, Zhenming Peng, Ali Gholami, Jingwen Yan, Shu Li
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Mathematics 数学
二级分类：Numerical Analysis 数值分析
分类描述：Numerical algorithms for problems in analysis and algebra, scientific computation
分析和代数问题的数值算法，科学计算
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英文摘要：
Time-frequency analysis has been applied successfully in many fields. However, the traditional methods, like short time Fourier transform and Cohen distribution, suffer from the low resolution or the interference of the cross terms. To solve these issues, we put forward a new sparse time-frequency analysis model by using the Lp-quasinorm constraint, which is capable of fitting the sparsity prior knowledge in the frequency domain. In the proposed model, we regard the short time truncated data as the observation of sparse representation and design a dictionary matrix, which builds up the relationship between the short time measurement and the sparse spectrum. Based on the relationship and the Lp-quasinorm feasible domain, the proposed model is established. The alternating direction method of multipliers (ADMM) is adopted to solve the proposed model. Experiments are then conducted on several theoretical signals and applied to the seismic signal spectrum decomposition, indicating that the proposed method is able to obtain a higher time-frequency distribution than state-of-the-art time-frequency methods. Thus, the proposed method is of great importance to reservoir exploration.
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PDF链接：
https://arxiv.org/pdf/1801.05082
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