The Collaboratory for the Study of Earthquake Predictability (CSEP) aims to prospectively test time-dependent earthquake probability forecasts on their consistency with observations. To compete, time-dependent seismicity models are calibrated on earthquake catalog data. But catalogs contain much observational uncertainty. We study the impact of magnitude uncertainties on rate estimates in clustering models, on their forecasts and on their evaluation by CSEP's consistency tests. First, we quantify magnitude uncertainties. We find that magnitude uncertainty is more heavy-tailed than a Gaussian, such as a double-sided exponential distribution, with scale parameter nu_c=0.1 - 0.3. Second, we study the impact of such noise on the forecasts of a simple clustering model which captures the main ingredients of popular short term models. We prove that the deviations of noisy forecasts from an exact forecast are power law distributed in the tail with exponent alpha=1/(a*nu_c), where a is the exponent of the productivity law of aftershocks. We further prove that the typical scale of the fluctuations remains sensitively dependent on the specific catalog. Third, we study how noisy forecasts are evaluated in CSEP consistency tests. Noisy forecasts are rejected more frequently than expected for a given confidence limit. The Poisson assumption of the consistency tests is inadequate for short-term forecast evaluations. To capture the idiosyncrasies of each model together with any propagating uncertainties, the forecasts need to specify the entire likelihood distribution of seismic rates.
The Andravida, Greece EQ (8/06/2008, Ms=7.0R). An "a posteriori" analysis for the determination of its location, occurrence time and magnitude parameters in terms of short-term predictability
How to quantify earthquake predictability? Advances in earthquake forecasting and predictability limits
The Institute of Statistical Mathematics（统计数学研究所）；the Graduate University for Adavanced Studies (SOKENDai)（advanced studies 研究生院（SOKENDai））；Institute of Risk Analysis, Prediction and Management, Southern University of Science and Technology（南方科技大学风险分析、预测与管理研究院）
Physics-Based Seismic Hazard and Risk Assessment: A New Paradigm for Earthquake Forecasting
Institute of Risk Analysis, Prediction and Management (Risks-X), Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology (SUSTech)（南方科技大学高等交叉研究院风险、预测与管理研究所）；Istituto Nazionale di Geofisica e Vulcanologia (INGV)（意大利国家地球物理学与火山学研究所）
On the Imminent Regional Seismic Activity Forecasting Using INTERMAGNET and Sun-Moon Tide Code Data
本文提出了一种利用地磁和固体潮资料进行区域地震活动性短临预报的方法。地震活动性的时间周期是潮汐矢量模的日均值在日月极值附近的时间周期。为了分析地磁数据行为，我们使用地磁矢量分量F的日标准差来计算时间方差Geomag信号。通过使用每日A指数（由NOAA发布）的数据来避免太阳风暴的影响。用于预测未来区域地震活动性的前兆信号是当前和前一天的地磁信号和A指数值的简单函数。本文通过对预报地震与发震时间日差的统计分析，论证了地磁区域前兆的可靠性。分析的基础是一个自然的假设，即预测地震是监测点的表面能密度大于同时期、同区域所有发生地震的能量密度的地震。使用位于保加利亚、Panagurishte、PAG的INTERMAGNET台站数据对该方法的可靠性进行了测试（Jan 1，2008-Jan 29，2014），罗马尼亚，Surlari，SUA（Jan 1，2008-Jan 27，2014），Italy，L'Aquila，AQU（Jan 1，2008-May 30，2013）in the time of EU IRSES BlackSeaHazNet（2011-2014）project.简要介绍了解决何时、何地、如何进行地震预测问题的程序步骤。
Text Classification of the Precursory Accelerating Seismicity Corpus: Inference on some Theoretical Trends in Earthquake Predictability Research from 1988 to 2018
Prediction of geophysical properties of rocks on rare well data and attributes of seismic waves by machine learning methods on the example of the Achimov formation
Systematic procedural and sensitivity analysis of the pattern informatics method for forecasting large (M > 5) earthquake events in southern California
Integrating Artificial Intelligence and Geophysical Insights for Earthquake Forecasting: A Cross-Disciplinary Review
University of Science and Technology Beijing（北京科技大学）；Beijing Engineering Research Center of Industrial Spectrum Imaging（北京市工业光谱成像工程研究中心）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Southern University of Science and Technology（南方科技大学）；China University of Geosciences (Beijing)（中国地质大学（北京））
