摘要翻译：
我们提出了一种二维走时层析成像方法，该方法通过将离散慢度图中的慢度像素组建模为字典中原子的稀疏线性组合来规整反演。我们建议在倒置过程中使用字典学习来使字典适应特定的慢度图。这种被称为局部模型的斑块正则化被集成到被称为全局模型的整体慢度图中。局部模型使用稀疏性约束来考虑小尺度变化，全局模型使用$\ell_2$正则化来考虑大尺度特征。在局部稀疏走时层析成像(LST)方法中，这种策略能够同时建模平滑和不连续的慢度特征。这与传统的层析成像方法形成鲜明对比，后者将模型限制为完全光滑或不连续。我们为LST开发了$\textIt{最大后验概率}$公式，并使用字典学习来利用慢度补丁的稀疏性。在密集但不规则采样的合成慢度图上，LST方法优于平滑和全变差正则化方法。
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英文标题：
《Travel time tomography with adaptive dictionaries》
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作者：
Michael Bianco and Peter Gerstoft
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最新提交年份：
2018
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分类信息：

一级分类：Physics        物理学
二级分类：Geophysics        地球物理学
分类描述：Atmospheric physics. Biogeosciences. Computational geophysics. Geographic location. Geoinformatics. Geophysical techniques. Hydrospheric geophysics. Magnetospheric physics. Mathematical geophysics. Planetology. Solar system. Solid earth geophysics. Space plasma physics. Mineral physics. High pressure physics.
大气物理学。生物地质学。计算地球物理学。地理位置。地理信息学。地球物理技术。水层地球物理学。磁层物理学。数学地球物理学。行星学。太阳系。固体地球地球物理学。空间等离子体物理。矿物物理学。高压物理。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  We develop a 2D travel time tomography method which regularizes the inversion by modeling groups of slowness pixels from discrete slowness maps, called patches, as sparse linear combinations of atoms from a dictionary. We propose to use dictionary learning during the inversion to adapt dictionaries to specific slowness maps. This patch regularization, called the local model, is integrated into the overall slowness map, called the global model. The local model considers small-scale variations using a sparsity constraint and the global model considers larger-scale features constrained using $\ell_2$ regularization. This strategy in a locally-sparse travel time tomography (LST) approach enables simultaneous modeling of smooth and discontinuous slowness features. This is in contrast to conventional tomography methods, which constrain models to be exclusively smooth or discontinuous. We develop a $\textit{maximum a posteriori}$ formulation for LST and exploit the sparsity of slowness patches using dictionary learning. The LST approach compares favorably with smoothness and total variation regularization methods on densely, but irregularly sampled synthetic slowness maps.
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PDF链接：
https://arxiv.org/pdf/1712.08655