摘要翻译：
被动微震数据通常被噪声所掩埋，这给信号检测和恢复带来了极大的挑战。针对表面传感器阵列的记录，提出了一种基于自相关的叠加方法，该方法设计了一个去噪滤波器，并提出了一种多通道检测方案。这种方法避免了在堆叠之前对齐轨迹的时间问题，因为每个轨迹的自相关在滞后域中都以零为中心。白噪声的影响集中在零滞后附近，因此滤波器设计要求对零滞后值进行可预测的调整。利用自相关截断来平滑去噪滤波器的脉冲响应。为了扩展算法的适用性，我们还提出了一种针对有色噪声情况的噪声预白化方案。用合成地震道和实际地震道验证了该方法的简单性和鲁棒性。
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英文标题：
《Microseismic events enhancement and detection in sensor arrays using
  autocorrelation based filtering》
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作者：
Entao Liu, Lijun Zhu, Anupama Govinda Raj, James H. McClellan,
  Abdullatif Al-Shuhail, SanLinn I. Kaka, Naveed Iqbal
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最新提交年份：
2016
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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：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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：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：
  Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose an autocorrelation-based stacking method that designs a denoising filter from all the traces, as well as a multi-channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace's autocorrelation is centered at zero in the lag domain. The effect of white noise is concentrated near zero lag, so the filter design requires a predictable adjustment of the zero-lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with colored noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.
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PDF链接：
https://arxiv.org/pdf/1612.01884