Towards advancing the earthquake forecasting by machine learning of satellite data
Institute of Earthquake Forecasting, China Earthquake Administration（中国地震局地震预测研究所）；National Institute of Natural Hazards, Ministry of Emergency Management of China（中国应急管理部国家自然灾害防治研究院）；University of Leicester（莱斯特大学）；Nanyang Technological University（南洋理工大学）；University of Trento（特伦托大学）；National Institute for Nuclear Physics, the Trento Institute for Fundamental Physics and Applications（意大利国家核物理研究院特伦托基础物理与应用研究所）；Chapman University（查普曼大学）；Queen's University Belfast（贝尔法斯特女王大学）；Nantong University（南通大学）
Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huiyu Zhou
Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1,371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.
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））
Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)
Resource Geophysics Academy, Imperial College London（帝国理工学院资源地球物理学院）；Department of Earth Science and Engineering, Imperial College London（帝国理工学院地球科学与工程系）；European Centre for Medium-Range Weather Forecast（欧洲中期天气预报中心）；Department of Scientific Computing, Florida State University（佛罗里达州立大学科学计算系）
Advancing Long-Term Multi-Energy Load Forecasting with Patchformer: A Patch and Transformer-Based Approach
School of Mathematics, Statistics and Actuarial Science（数学、统计与精算科学学院）；University of Essex（埃塞克斯大学）；Alliance Manchester Business School（曼彻斯特联盟商学院）；University of Manchester（曼彻斯特大学）
Rice University（莱斯大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；University of Nevada, Reno（内华达大学雷诺分校）；Sapienza Universitá di Roma（罗马一大（萨皮恩扎大学））
Rocks under stress deform by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of t…
Assessing the predicting power of GPS data for aftershocks forecasting
The Institute of Statistical Mathematics, Research Organization of Information and Systems（信息与系统研究机构统计数学研究所）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）
A prominent feature of earthquakes is their empirical laws including memory (clustering) in time and space. Several earthquake forecasting models, like the EpidemicType Aftershock Sequence (ETAS) model, were developed based on earthquake empirical laws. Yet, a recent study showed that the ETAS model fails in reproducing significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) recently discovered in the data. Our ge…
Advancing Subseasonal Forecasting with Machine Learning
Harvard College（哈佛学院）；University of Toronto（多伦多大学）；Instituto de Matemática Pura e Aplicada（数学与应用数学研究所）；Massachusetts Institute of Technology（麻省理工学院）；Atmospheric and Environmental Research（大气与环境研究）；Microsoft Corporation（微软公司）；Rhiza Research（Rhiza研究）；Microsoft Research New England（微软新英格兰研究院）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
Forecasting the full distribution of the number of earthquakes is revealed to be inherently superior to forecasting their mean. Forecasting the full distribution of earthquake numbers is also shown to yield robust projections in the presence of "surprise" large earthquakes, which in the past have strongly deteriorated the scores of existing models. We show this with pseudo-prospective experiments on synthetic as well as real data from the Advanced National Seismic System (ANSS) database for California, with earthquakes with magnitude larger than 2.95 that occurred between the period 1971-2016.…
FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
Currently, one of the best performing and most popular earthquake forecasting models rely on the working hypothesis that: "locations of past background earthquakes reveal the probable location of future seismicity". As an alternative, we present a class of smoothed seismicity models (SSMs) based on the principles of the Epidemic Type Aftershock Sequence (ETAS) model, which forecast the location, time and magnitude of all future earthquakes using the estimates of the background seismicity rate and the rates of future aftershocks of all generations. Using the Californian earthquake catalog, we f…
Following N.Kozyrev's idea about the influence of the gravitational fields of the Sun and the Moon on the Earth's crust, we consider a low-frequency resonance of the Earth's crust blocks is happening before the occurrence of the earthquake. The resonance affects several geophysical parameters and is associated with many short-term precursory phenomena; the most notable are the release of underground gases (radon), the air temperature rises, change of thermal radiation in the troposphere, and an increase in the electron density in the ionosphere. During the final stage of the earthquake genesis…
Stanford University（斯坦福大学）；Open Climate Fix（开放气候修复）；National Center for Atmospheric Research（美国国家大气研究中心）
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance …
In this study, we propose an analysis of the earthquake clusters that occurred in North-Eastern Italy and western Slovenia from 1977 to today. Given a mainshock generating alarm in the population, we are interested in forecasting if a similar magnitude earthquake will follow. We classify the earthquake clusters associated with mainshocks of magnitude Mm into two classes: if the strongest aftershock has a magnitude >=Mm-1 (swarms or large aftershock seismic sequences) as type A, otherwise (smaller aftershocks seismic sequences) as type B. A large aftershock following a main shock can cause sign…
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）
We propose two methods to calibrate the parameters of the epidemic-type aftershock sequence (ETAS) model based on expectation maximization (EM) while accounting for temporal variation of catalog completeness. The first method allows for model calibration on long-term earthquake catalogs with temporal variation of the completeness magnitude, $m_c$. This calibration technique is beneficial for long-term probabilistic seismic hazard assessment (PSHA), which is often based on a mixture of instrumental and historical catalogs. The second method generalizes the concept of $m_c$, considering rate- an…
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…
Earthquake Aftershock Forecasting using Conditional Generative Models
Department of Earth and Planetary Science, University of California, Berkeley（加州大学伯克利分校地球与行星科学系）；Berkeley Seismological Laboratory, University of California, Berkeley（加州大学伯克利分校伯克利地震实验室）
Over the past decades much effort has been devoted towards understanding and forecasting natural hazards. However, earthquake forecasting skill is still very limited and remains a great scientific challenge. The limited earthquake predictability is partly due to the erratic nature of earthquakes and partly to the lack of understanding the underlying mechanisms of earthquakes. To improve our understanding and potential forecasting, here we study the spatial and temporal long-term memory of interevent earthquakes above a certain magnitude using lagged conditional probabilities. We find, in real …
Exceedance Probabilities for Large Earthquakes From DIY Local Earthquake Ensemble Nowcasting and Forecasting
University of California, Davis（加州大学戴维斯分校）；Santa Fe Institute（圣塔菲研究所）；Jet Propulsion Laboratory（喷气推进实验室）；Purdue University（普渡大学）；University of California, Irvine（加州大学尔湾分校）；University of Virginia（弗吉尼亚大学）；Shizuoka University（静冈大学）
Systematic procedural and sensitivity analysis of the pattern informatics method for forecasting large (M > 5) earthquake events in southern California
Earthquakes are one of the most devastating natural disasters that plague society. A skilled, reliable earthquake forecasting remains the ultimate goal for seismologists. Using the detrended fluctuation analysis (DFA) and conditional probability (CP) methods we find that memory exists not only in inter-occurrence seismic records, but also in released energy as well as in the series of the number of events per unit time. Analysis of the conventional earthquake model (Epidemic Type Aftershock Sequences, ETAS) indicates that earthquake memory can be reproduced only for a narrow range of model's p…
GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python
School of Transportation Engineering, East China Jiao Tong University（东华交通大学交通运输工程学院）；Jiangxi Provincial Key Laboratory of Water Ecological Conservation in Headwater Regions（江西省头水地区水生态保育重点实验室）；Division of Geodetic Science, School of Earth Sciences, The Ohio State University（地球科学学院地质科学系，俄亥俄州立大学）；Institute Dom Luiz, University of Beira Interior（贝拉内里亚大学多姆路易斯研究所）；Institute of Meteorology and Climatology, Leibniz University Hannover（汉诺威莱比锡大学气象与气候研究所）；Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University（江西省鄱阳湖湿地与流域研究重点实验室，江西省教育厅，江西师范大学）
Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics
University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）；Digital Financial Information Technology Co.LTD（数字金融信息技术有限公司）；Northern Arizona University（北亚利桑那大学）
