Trends in volcano seismology: 2010 to 2020 and beyond
Trends in volcano seismology: 2010 to 2020 and beyond
复制标题
火山地震学趋势：2010年至2020年及以后
DOI：
10.1007/s00445-022-01530-2
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发表时间：
2022
影响因子：
3.5
通讯作者：
Hotovec-Ellis, Alicia J.
中科院分区：
文献类型：
--
作者：
Thelen, Weston A.;Matoza, Robin S.;Hotovec-Ellis, Alicia J.
火山地震学是我们目前了解地壳岩浆迁移和喷发的基础。便携式地震网络的日益普及，创造性地利用了地震源和环境噪音，使人们更好地了解许多火山的火山结构，并制作出越来越详细的火山地下图像。过去十年（2010-2020年），我们通过精确定位，并通过分析地震信号的震源机制，对火山下和周围的震源有了进一步的了解，这些信号变化更大，震级更小，超越了传统技术。随着对基于基础物理学的火山地震源理解的持续研究，包括机器学习方法在内的计算分析的新进展将推动我们对火山过程的理解。结合多学科地球物理观测（特别是次声）已变得司空见惯，我们对次声传播和来源的了解将反馈到我们监测持续喷发和地表物质运动的能力中。开放源代码将允许广泛评价和采用火山地震分析和反演的新方法。这些新方法与使用改进的结构约束的定量和概念震源模型相结合，将更好地描述火山-地震信号演变情景的范围，并有望建立更好的短期预测。最后，在越来越多的火山上有了永久性的仪器，开放数据政策越来越多地使科学界能够接近真实的时间获得这些数据。
Volcano seismology has been fundamental to our current understanding of crustal magma migration and eruption. The increasing availability of portable seismic networks with the creative use of seismic sources and ambient noise has led to a better understanding of the volcanic structure of many volcanoes and is producing increasingly detailed images of the volcanic subsurface. The past decade (2010-2020) has seen advances in our understanding of seismic sources under and surrounding volcanoes through precise locations, and through analysis of source mechanisms from seismic signals that are more varied and smaller in magnitude, reaching beyond traditional techniques. In tandem with continued research on fundamental physics-based understanding of volcano-seismic sources, new advances in computational analyses including machine learning methods will push our understanding of volcanic processes into the future. Incorporation of multidisciplinary geophysical observations (especially infrasound) has become commonplace, and our understanding of infrasound propagation and sources will feed back into our ability to monitor ongoing eruptions and surficial mass movements. Open-source codes will permit widespread evaluation and adoption of new methodologies for volcano-seismic analysis and inversion. Combined with quantitative and conceptual source models using improved structural constraints, these new methodologies will better characterize the range of volcano-seismic signal evolution scenarios and hold promise for creating better short-term forecasts. Finally, permanent instrumentation is available on an expanding range of volcanoes, and open data policies are increasingly making these data available to the scientific community in near real time.
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影响因子：
5.3
作者：
Yutaka Nagaoka;Kiwamu Nishida;Y. Aoki;M. Takeo;T. Ohminato
通讯作者：
Yutaka Nagaoka;Kiwamu Nishida;Y. Aoki;M. Takeo;T. Ohminato
基于阵列的卷积神经网络用于夏威夷地震的自动检测和 4D 定位
影响因子：
3.3
作者：
Shen, Heather;Shen, Yang
通讯作者：
Shen, Yang
利用次声抑制灰羽上升
影响因子：
--
作者：
O. Lamb;S. De Angelis;Y. Lavallée
通讯作者：
Y. Lavallée
第59章-火山地震活动
DOI：
--
发表时间：
2015
期刊：
影响因子：
--
作者：
S. McNutt
通讯作者：
S. McNutt
影响因子：
3.5
作者：
Wessel, P.;Luis, J. F.;Tian, D.
通讯作者：
Tian, D.