摘要翻译：
水停留时间分布相当于允许水通过介质传播的线性系统的脉冲响应，例如雨水从山顶向含水层传播。我们将输出含水层水位视为输入降雨水位和水停留时间之间的卷积，从初始含水层基准面开始。水停留时间的估算对于更好地理解湿地的水文-生物-地球化学过程和湿地在生态应用中作为过滤器的混合特性以及保护水井淡水水源免受污染具有重要意义。常用的水停留时间估计方法主要有互相关法、参数拟合法和非参数反褶积法。本文提出了一种一维全反褶积正则化非参数反问题算法，该算法利用因果性和正性约束来估计水停留时间曲线。与贝叶斯非参数反卷积方法相比，它具有每个测试用例的快速运行时间；与目前流行的快速互相关方法相比，该方法即使在有噪声的情况下也能得到更精确的水停留时间曲线。该算法只需一个正则化参数就能在水停留时间的平滑性和重建精度之间取得平衡。提出了一种从输入数据中自动找到合适的正则化参数值的方法。对实际资料的检验表明了该方法在水文资料分析中的潜力。
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英文标题：
《Water Residence Time Estimation by 1D Deconvolution in the Form of a
  l2-Regularized Inverse Problem With Smoothness, Positivity and Causality
  Constraints》
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作者：
Alina G. Meresescu and Matthieu Kowalski and Fr\'ed\'eric Schmidt and
  Fran\c{c}ois Landais
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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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英文摘要：
  The Water Residence Time distribution is the equivalent of the impulse response of a linear system allowing the propagation of water through a medium, e.g. the propagation of rain water from the top of the mountain towards the aquifers. We consider the output aquifer levels as the convolution between the input rain levels and the Water Residence Time, starting with an initial aquifer base level. The estimation of Water Residence Time is important for a better understanding of hydro-bio-geochemical processes and mixing properties of wetlands used as filters in ecological applications, as well as protecting fresh water sources for wells from pollutants. Common methods of estimating the Water Residence Time focus on cross-correlation, parameter fitting and non-parametric deconvolution methods. Here we propose a 1D full-deconvolution, regularized, non-parametric inverse problem algorithm that enforces smoothness and uses constraints of causality and positivity to estimate the Water Residence Time curve. Compared to Bayesian non-parametric deconvolution approaches, it has a fast runtime per test case; compared to the popular and fast cross-correlation method, it produces a more precise Water Residence Time curve even in the case of noisy measurements. The algorithm needs only one regularization parameter to balance between smoothness of the Water Residence Time and accuracy of the reconstruction. We propose an approach on how to automatically find a suitable value of the regularization parameter from the input data only. Tests on real data illustrate the potential of this method to analyze hydrological datasets.
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PDF链接：
https://arxiv.org/pdf/1803.07574