A theoretical analysis of the earthquake prediction problem in space-time is presented. We find an explicit structure of the optimal strategy and its relation to the generalized error diagram. This study is a generalization of the theoretical results for time prediction. The possibility and simplicity of this extension is due to the choice of the class of goal functions. We also discuss issues in forecasting versus prediction, scaling laws versus predictability, and measure of prediction efficiency at the research stage.
Toward Trustworthy Earthquake Catalogs in the Era of Automated Detection: A Probabilistic Framework for Robust Earthquake Location
Institute of Geophysics, China Earthquake Administration（中国地震局地球物理研究所）；Laboratory of Seismology and Physics of Earth’s Interior, School of Earth and Space Sciences, University of Science and Technology of China（中国科学技术大学地球和空间科学学院地球内部地震学与地球物理学实验室）；Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）
Ruptures of the largest earthquakes can last between a few seconds and several minutes. An early assessment of the final earthquake size is essential for early warning systems. However, it is still unclear when in the rupture history this final size can be predicted. Here we introduce a probabilistic view of rupture evolution - how likely is the event to become large - allowing for a clear and well-founded answer with implications for earthquake physics and early warning. We apply our approach to real time magnitude estimation based on either moment rate functions or broadband teleseismic P ar…
SPIDER: Scalable Probabilistic Inference for Differential Earthquake Relocation
Seismological Laboratory, California Institute of Technology（加州理工学院地震实验室）；Nvidia Corporation（英伟达公司）；Earthquake Research Institute, University of Tokyo（东京大学地震研究所）
A probabilistic method for the estimation of earthquake source parameters from spectral inversion : application to the 2016-2017 Central Italy seismic sequence
Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual earthquakes and play an important role in earthquake monitoring. Dense seismic networks and improved phase picking methods produce massive earthquake phase data sets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. We present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model for phase measurements (both time and amplitude), wi…
A new design strategy based on a deterministic definition of the seismic input to overcome the limits of design procedures based on probabilistic approaches
Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校奥登计算工程与科学学院）；Scripps Institution of Oceanography, University of California San Diego（加州大学圣地亚哥分校斯克里普斯海洋研究所）；Walker Department of Mechanical Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校沃克机械工程系）；Department of Earth and Environmental Sciences, Ludwig‐Maximilians‐Universität München（慕尼黑路德维希-马克西米利安大学地球与环境科学系）
Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
University of Virginia（弗吉尼亚大学）；University of California, Davis（加州大学戴维斯分校）；Santa Fe Institute（圣达菲研究所）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；University of California, Irvine（加州大学欧文分校）
Università La Sapienza（罗马第一大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for induced seismicity and successful prediction of laboratory earthquakes. In the lab, frictional stick-slip events provide an analog for earthquakes and the seismic cycle. Labquakes are ideal targets for machine learning (ML) because they can be produced in long sequences under controlled conditions. Recent works show that ML can predict several aspects of labquakes using fault zone acoustic emissions. Here, we general…
The seismogenic area in the lithosphere considered as an "Open Physical System". Its implications on some seismological aspects. Part - II. Maximum expected magnitude determination
Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. The recorded seismic signals by DAS have several distinct characteristics, such as unknown coupling effects, strong anthropogenic noise, and ultra-dense spatial sampling. These aspects differ from conventional seismic data recorded by seismic networks, making it challenging to utilize DAS at present for seismic monitoring. New data analysis algorithms are needed to extract useful information from DAS data. Previous studies on conventional seismic data demonstrated that deep learning m…
本章首先提出了一个相当个人的看法，一些不同方面的可预测性，从简单的线性系统到高维非线性系统与随机强迫，表现出突发的性质，如相变和政权转移渐强。然后，地震，金融崩溃和癫痫发作的现象之间的详细对应关系。所提供的统计证据提供了一般相图的实质内容，用于理解这些系统时空组织的许多方面。一个关键的见解是组织的证据和机制方面的两个总结措施：（i）的幅度的混乱或异质性的系统和（ii）耦合或相互作用的强度在系统的组件。最近确定的地震和癫痫发作之间的显着对应关系的基础上，我们提出了详细的信息一类随机点过程，已被发现是特别强大的描述地震现象，我们认为，在癫痫学有一个很好的未来。所谓的自激霍克斯点过程（self-exciting Hawkes point processes）简单地描述了事件可以触发其他事件的想法，它们之间的级联相互作用和相互影响对于理解这些系统的行为至关重要。
