Detection of Dynamic Phenomena Associated with Underground Nuclear Explosion Using Multiple Seismic Surveys and Machine Learning
Detection of Dynamic Phenomena Associated with Underground Nuclear Explosion Using Multiple Seismic Surveys and Machine Learning
复制标题
使用多次地震勘测和机器学习检测与地下核爆炸相关的动态现象
DOI：
10.1007/s00024-022-03145-2
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发表时间：
2022
影响因子：
2
通讯作者：
Mathew S
中科院分区：
文献类型：
--
作者：
Mathew S
应用有源地震方法探测地下核爆炸的震源位置是一个正在进行的研究领域。主动地震现场检测（OSI）的目的是检测地震的静态特征，如地震产生的空洞。除了典型的静态特征外，UNEs还会产生动态现象，如地下水堆积，这些现象逐渐恢复到测试前的状态。这些动态现象可以在很长一段时间内观察到，甚至长达几十年。这些现象的强度在震源附近很突出，是残余能量（如压力、温度和饱和度）重新分配的结果。这些地下岩石和流体性质的动态变化将影响岩石的地震性质，从而导致纵波速度的变化。这些变化可以通过主动地震测量来检测。这项研究强调了利用时移地震通过监测爆炸后地震特征的变化来识别地面零点的潜力。延时地震，也被称为4D地震，是一项众所周知的技术，在石油和天然气行业用于石油生产监测和管理已有几十年的历史。它包括在不同日历时间对同一储层进行多次2D/3D调查，并研究地震属性的差异。本研究探讨了与UNE相关的特征性动力现象及其对就位岩地震特性的影响。地下水丘顶（GWM）是爆炸后最初几天内耗散梯度较大的现象之一。我们研究了GWM变化对地震纵波速度的影响，并讨论了使用时移地震进行探测的潜力。讨论了实施时移地震的挑战，如不可重复性、季节变化和时间限制。提出了一种频繁地震监测测量方法（时移地震）来监测地震后动态现象引起的岩石和流体性质变化。由于OSI活动的时间限制，传统的时移地震处理将不适用。为此，提出了一种基于机器学习的四维检测工作流程。使用机器学习的近实时4D检测工作流程可以在OSI期间实施，以确定源位置或归零点。
The application of an active seismic method for detecting the source location of an underground nuclear explosion (UNE) is an ongoing field of research. The objective of active seismic in On-Site Inspection (OSI) is to detect the static signatures such as the cavity created by the UNE. Along with characteristic static signatures, UNEs produce dynamic phenomena such as groundwater mounding, which gradually revert to pre-test conditions. These dynamic phenomena are observable for an extended period, even up to several decades. The magnitude of these phenomena is prominent near the source origin and results from the redistribution of residual energy, such as pressure, temperature, and saturation. These dynamic changes in sub-surface rock and fluid properties will affect the seismic property of the rock, resulting in changes of P-wave velocity. These changes can be detected by using an active seismic survey. This study highlights the potential of using time-lapse seismic to identify ground zero by monitoring post-explosion variation in the seismic signature. Time-lapse seismic, also known as 4D seismic, is a well-known technology, used in the oil and gas industry for several decades for petroleum production monitoring and management. It involves taking more than one 2D/3D survey at different calendar times over the same reservoir and studying the difference in seismic attributes. This study investigates the characteristic dynamic phenomena associated with the UNE and their impact on the emplacement rock’s seismic property. Groundwater mounding (GWM) is one of the phenomena with a high gradient of dissipation during the initial days immediately after the explosion. We look at the impact of GWM variation on seismic P-wave velocity and discuss the potential of using time-lapse seismic for its detection. The challenges of implementing time-lapse seismic, such as non-repeatability, seasonal variations and time constraints, are discussed. A frequent seismic monitoring survey method (time-lapse seismic) is proposed to monitor rock and fluid properties changes due to the post-UNE dynamic phenomena. Due to the time constraint for the OSI activity, conventional time-lapse seismic processing would not be suitable. Therefore, a machine learning-based 4D detection workflow is presented. The near-real-time 4D detection workflow using machine learning can be implemented during the OSI to identify the source location or ground zero.
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影响因子：
4.6
作者：
Carrigan CR;Sun Y;Hunter SL;Ruddle DG;Wagoner JL;Myers KB;Emer DF;Drellack SL;Chipman VD
通讯作者：
Chipman VD
DOI：
10.5220/0007364803360344
发表时间：
2019
期刊：
Water-Resources Investigations Report
影响因子：
--
作者：
Ricardo Hormann;Eric Fischer
通讯作者：
Eric Fischer
影响因子：
3.1
作者：
R. Calvert
通讯作者：
R. Calvert
延时（4D）地震技术作为储层监测和监视工具的作用：全面回顾
影响因子：
--
作者：
C. Sambo;C. C. Iferobia;A. Babasafari;Shiba Rezaei;Owolabi A. Akanni
通讯作者：
Owolabi A. Akanni
U2ez就位孔基础数据报告
影响因子：
25.2
作者：
J. Wagoner
通讯作者：
J. Wagoner