摘要翻译：
大规模地质碳封存的风险之一是潜在的流体从储存地层中迁移。由于地下不确定度大，控制物理复杂，准确、快速地探测这类流体的运移不仅重要而且具有挑战性。传统的渗漏检测和监测技术依赖于包括地震在内的地球物理观测。然而，这些方法的精确度有限，因为它们提供的间接信息需要专家解释，因此对渗漏率和渗漏位置作出了准确的估计。在本文中，我们开发了一个基于深度密连接神经网络的机器学习检测软件包“震网”。为了验证我们提出的泄漏检测方法的性能，我们将我们的方法应用于新墨西哥州Chimay\'o的一个自然模拟现场。数据集中的地震事件是由于间歇泉的喷发而产生的，而喷泉的喷发是由于$\mathrm{CO}_\mathrm{2}$的泄漏而产生的。特别地，我们通过将我们的检测问题描述为具有时间序列数据的事件检测问题来证明我们的地震台网的有效性。一个固定长度的窗口在时间序列数据中滑动，我们建立了一个深度密集连接的网络来对每个窗口进行分类，以确定是否包括间歇泉事件。通过我们的数值测试，我们的模型达到了高达0.889/0.923的查准率/查全率。因此，我们的地震台网在探测$\mathrm{CO}_\mathrm{2}$泄漏方面有很大的潜力。
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英文标题：
《Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic
  Events》
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作者：
Yue Wu, Youzuo Lin, Zheng Zhou, Andrew Delorey
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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：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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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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英文摘要：
  One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the large subsurface uncertainty and complex governing physics. Traditional leakage detection and monitoring techniques rely on geophysical observations including seismic. However, the resulting accuracy of these methods is limited because of indirect information they provide requiring expert interpretation, therefore yielding in-accurate estimates of leakage rates and locations. In this work, we develop a novel machine-learning detection package, named "Seismic-Net", which is based on the deep densely connected neural network. To validate the performance of our proposed leakage detection method, we employ our method to a natural analog site at Chimay\'o, New Mexico. The seismic events in the data sets are generated because of the eruptions of geysers, which is due to the leakage of $\mathrm{CO}_\mathrm{2}$. In particular, we demonstrate the efficacy of our Seismic-Net by formulating our detection problem as an event detection problem with time series data. A fixed-length window is slid throughout the time series data and we build a deep densely connected network to classify each window to determine if a geyser event is included. Through our numerical tests, we show that our model achieves precision/recall as high as 0.889/0.923. Therefore, our Seismic-Net has a great potential for detection of $\mathrm{CO}_\mathrm{2}$ leakage.
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PDF链接：
https://arxiv.org/pdf/1802.02241