Introduction to POTENTIAL INDUCED
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Calculating Earthquake Probabilities for the Sfbr
CHAPTER 5: CALCULATING EARTHQUAKE PROBABILITIES FOR THE SFBR Introduction to Probability Calculations The first part of the calculation sequence (Figure 2.10) defines a regional model of the long-term production rate of earthquakes in the SFBR. However, our interest here is in earthquake probabilities over time scales shorter than the several-hundred-year mean recurrence intervals of the major faults. The actual time periods over which earthquake probabilities are calculated should correspond to the time scales inherent in the principal uses and decisions to which the probabilities will be applied. Important choices involved in engineering design, retrofitting homes or major structures, and modifying building codes generally have different time-lines. Accordingly, we calculate the probability of large earthquakes for several time periods, as was done in WG88 and WG90, but focus the discussion on the period 2002-2031. The time periods for which we calculate earthquake probability are the 1-, 5-, 10-, 20-, 30- and100-year-long intervals beginning in 2002. The second part of the calculation sequence (Figure 5.1) is where the time-dependent effects enter into the WG02 model. In this chapter, we review what time-dependent factors are believed to be important and introduce several models for quantifying their effects. The models involve two inter-related areas: recurrence and interaction. Recurrence is concerned with the long-term rhythm of earthquake production, as controlled by the geology and plate motion. Interaction is the syncopation of this rhythm caused by recent large earthquakes on faults in the SFBR as they affect the neighboring faults. The SFBR earthquake model allows us to portray the likelihood of occurrence of large earthquakes in several ways. -
International Commission on Earthquake Forecasting for Civil Protection
ANNALS OF GEOPHYSICS, 54, 4, 2011; doi: 10.4401/ag-5350 OPERATIONAL EARTHQUAKE FORECASTING State of Knowledge and Guidelines for Utilization Report by the International Commission on Earthquake Forecasting for Civil Protection Submitted to the Department of Civil Protection, Rome, Italy 30 May 2011 Istituto Nazionale di Geofisica e Vulcanologia ICEF FINAL REPORT - 30 MAY 2011 International Commission on Earthquake Forecasting for Civil Protection Thomas H. Jordan, Chair of the Commission Director of the Southern California Earthquake Center; Professor of Earth Sciences, University of Southern California, Los Angeles, USA Yun-Tai Chen Professor and Honorary Director, Institute of Geophysics, China Earthquake Administration, Beijing, China Paolo Gasparini, Secretary of the Commission President of the AMRA (Analisi e Monitoraggio del Rischio Ambientale) Scarl; Professor of Geophysics, University of Napoli "Federico II", Napoli, Italy Raul Madariaga Professor at Department of Earth, Oceans and Atmosphere, Ecole Normale Superieure, Paris, France Ian Main Professor of Seismology and Rock Physics, University of Edinburgh, United Kingdom Warner Marzocchi Chief Scientist, Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy Gerassimos Papadopoulos Research Director, Institute of Geodynamics, National Observatory of Athens, Athens, Greece Gennady Sobolev Professor and Head Seismological Department, Institute of Physics of the Earth, Russian Academy of Sciences, Moscow, Russia Koshun Yamaoka Professor and Director, Research Center for Seismology, Volcanology and Disaster Mitigation, Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan Jochen Zschau Director, Department of Physics of the Earth, Helmholtz Center, GFZ, German Research Centers for Geosciences, Potsdam, Germany 316 ICEF FINAL REPORT - 30 MAY 2011 TABLE OF CONTENTS Abstract................................................................................................................................................................... 319 I. INTRODUCTION 320 A. -
Analyzing the Performance of GPS Data for Earthquake Prediction
remote sensing Article Analyzing the Performance of GPS Data for Earthquake Prediction Valeri Gitis , Alexander Derendyaev * and Konstantin Petrov The Institute for Information Transmission Problems, 127051 Moscow, Russia; [email protected] (V.G.); [email protected] (K.P.) * Correspondence: [email protected]; Tel.: +7-495-6995096 Abstract: The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. -
Predicting Ground Motion from Induced Earthquakes In
Bulletin of the Seismological Society of America, Vol. 103, No. 3, pp. 1875–1897, June 2013, doi: 10.1785/0120120197 Ⓔ Predicting Ground Motion from Induced Earthquakes in Geothermal Areas by John Douglas, Benjamin Edwards, Vincenzo Convertito, Nitin Sharma, Anna Tramelli, Dirk Kraaijpoel, Banu Mena Cabrera, Nils Maercklin, and Claudia Troise Abstract Induced seismicity from anthropogenic sources can be a significant nui- sance to a local population and in extreme cases lead to damage to vulnerable struc- tures. One type of induced seismicity of particular recent concern, which, in some cases, can limit development of a potentially important clean energy source, is that associated with geothermal power production. A key requirement for the accurate assessment of seismic hazard (and risk) is a ground-motion prediction equation (GMPE) that predicts the level of earthquake shaking (in terms of, for example, peak ground acceleration) of an earthquake of a certain magnitude at a particular distance. Few such models currently exist in regard to geothermal-related seismicity, and con- sequently the evaluation of seismic hazard in the vicinity of geothermal power plants is associated with high uncertainty. Various ground-motion datasets of induced and natural seismicity (from Basel, Geysers, Hengill, Roswinkel, Soultz, and Voerendaal) were compiled and processed, and moment magnitudes for all events were recomputed homogeneously. These data are used to show that ground motions from induced and natural earthquakes cannot be statistically distinguished. Empirical GMPEs are derived from these data; and, although they have similar characteristics to recent GMPEs for natural and mining- related seismicity, the standard deviations are higher. To account for epistemic uncer- tainties, stochastic models subsequently are developed based on a single corner frequency and with parameters constrained by the available data. -
Incorporating Induced Seismicity Source Models and Ground Motion Predictions to Forecast Dynamic Regional Risk
Incorporating induced seismicity source models and ground motion predictions to forecast dynamic regional risk Jack W. Baker, Ph.D., M.ASCE,1 and Abhineet Gupta, Ph.D. 2 1 Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305; e-mail: [email protected] 2 Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305; e-mail: [email protected] ABSTRACT Decision-making regarding induced seismicity benefits greatly from quantitative risk assessment. While seismic hazard and risk analysis is widely used for natural seismicity, unique aspects of induced earthquakes require new tools to estimate occurrence rates of future earthquakes, to understand unique features of resulting ground motions, and to understand potential consequences at a regional scale. In this paper, we highlight recent work in dynamic source characterization and induced seismicity ground motion prediction to determine the hazard in the area, followed by prediction of regional scale risk. Results can be used to forecast risk under potential future earthquake activity, to predict benefits of potential interventions, and to quantify important uncertainties in the model that could be refined with further study. Example results for the State of Oklahoma are used to illustrate the potential insights from this analysis approach. INTRODUCTION Risk analysis is a well-established framework for assessing potential impacts of a range of human activities, evaluating the acceptability of those impacts, and taking actions to manage risks. For natural seismicity, risk assessment and hazard assessment are widely used and well established (e.g., McGuire 2004; Petersen et al. 2014). As induced seismicity has emerged as an important issue in a number of regions of the world, adoption of these approaches has naturally arisen as a potentially important tool to support decision-making (e.g., Bommer et al. -
Towards Advancing the Earthquake Forecasting by Machine Learning of Satellite Data
Towards advancing the earthquake forecasting by machine learning of satellite data Pan Xiong 1, 8, Lei Tong 3, Kun Zhang 9, Xuhui Shen 2, *, Roberto Battiston 5, 6, Dimitar Ouzounov 7, Roberto Iuppa 5, 6, Danny Crookes 8, Cheng Long 4 and Huiyu Zhou 3 1 Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, China 2 National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, China 3 School of Informatics, University of Leicester, Leicester, United Kingdom 4 School of Computer Science and Engineering, Nanyang Technological University, Singapore 5 Department of Physics, University of Trento, Trento, Italy 6 National Institute for Nuclear Physics, the Trento Institute for Fundamental Physics and Applications, Trento, Italy 7 Center of Excellence in Earth Systems Modeling & Observations, Chapman University, Orange, California, USA 8 School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, United Kingdom 9 School of Electrical Engineering, Nantong University, Nantong, China * Correspondence: Xuhui Shen ([email protected]) 1 Highlights An AdaBoost-based ensemble framework is proposed to forecast earthquake Infrared and hyperspectral global data between 2006 and 2013 are investigated The framework shows a strong capability in improving earthquake forecasting Our framework outperforms all the six selected baselines on the benchmarking datasets Our results support a Lithosphere-Atmosphere-Ionosphere Coupling during earthquakes 2 Abstract Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. -
Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers
sustainability Article Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers Papiya Debnath 1, Pankaj Chittora 2 , Tulika Chakrabarti 3, Prasun Chakrabarti 2,4, Zbigniew Leonowicz 5 , Michal Jasinski 5,* , Radomir Gono 6 and Elzbieta˙ Jasi ´nska 7 1 Department of Basic Science and Humanities, Techno International New Town Rajarhat, Kolkata 700156, India; [email protected] 2 Department of Computer Science and Engineering, Techno India NJR Institute of Technology, Udaipur 313003, Rajasthan, India; [email protected] (P.C.); [email protected] (P.C.) 3 Department of Basic Science, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India; [email protected] 4 Data Analytics and Artificial Intelligence Laboratory, Engineering-Technology School, Thu Dau Mot University, Thu Dau Mot City 820000, Vietnam 5 Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland; [email protected] 6 Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic; [email protected] 7 Faculty of Law, Administration and Economics, University of Wroclaw, 50-145 Wroclaw, Poland; [email protected] * Correspondence: [email protected]; Tel.: +48-713-202-022 Abstract: Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of Citation: Debnath, P.; Chittora, P.; geoscience for forecasting earthquakes. -
Laboratory Earthquake Forecasting: a Machine Learning Competition PERSPECTIVE Paul A
PERSPECTIVE Laboratory earthquake forecasting: A machine learning competition PERSPECTIVE Paul A. Johnsona,1,2, Bertrand Rouet-Leduca,1, Laura J. Pyrak-Nolteb,c,d,1, Gregory C. Berozae, Chris J. Maronef,g, Claudia Hulberth, Addison Howardi, Philipp Singerj,3, Dmitry Gordeevj,3, Dimosthenis Karaflosk,3, Corey J. Levinsonl,3, Pascal Pfeifferm,3, Kin Ming Pukn,3, and Walter Readei Edited by David A. Weitz, Harvard University, Cambridge, MA, and approved November 28, 2020 (received for review August 3, 2020) Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unex- pected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. -
Induced Seismicity Risk Analysis of the Hydraulic Stimulation of a Geothermal Well on Geldinganes, Iceland
https://doi.org/10.5194/nhess-2019-331 Preprint. Discussion started: 11 November 2019 c Author(s) 2019. CC BY 4.0 License. Induced seismicity risk analysis of the hydraulic stimulation of a geothermal well on Geldinganes, Iceland Marco Broccardo1,4, Arnaud Mignan2,3, Francesco Grigoli1, Dimitrios Karvounis1, Antonio Pio Rinaldi1,2, 5 Laurentiu Danciu1, Hannes Hofmann5, Claus Milkereit5, Torsten Dahm5, Günter Zimmermann5, Vala Hjörleifsdóttir6, Stefan Wiemer1 1 Swiss Seismological Service, ETH Zürich, Switzerland 2 Institute of Geophysics, ETH Zürich, Switzerland 10 3 Institute of Risk Analysis, Prediction and Management, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China 4 Institute of Structural Engineering, ETH Zürich, Switzerland 5 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany 6 Orkuveita Reykjavíkur/Reykjavík Energy, Iceland 15 Correspondence to: Marco Broccardo ([email protected]) Abstract. The rapid increase in energy demand in the city of Reykjavik has posed the need for an additional supply of deep geothermal energy. The deep hydraulic (re-)stimulation of well RV-43 on the peninsula of Geldinganes (north of Reykjavik) is an essential component of the plan implemented by Reykjavik Energy to meet this energy target. Hydraulic stimulation is 20 often associated with fluid-induced seismicity, most of which is not felt on the surface, but which, in rare cases, can cause nuisance to the population and even damage to the nearby building stock. This study presents a first of its kind pre-drilling probabilistic induced-seismic hazard and risk analysis for the site of interest. Specifically, we provide probabilistic estimates of peak ground acceleration, European microseismicity intensity, probability of light damage (damage risk), and individual risk. -
Application of a Long-Range Forecasting Model to Earthquakes in the Japan Mainland Testing Region
Earth Planets Space, 63, 197–206, 2011 Application of a long-range forecasting model to earthquakes in the Japan mainland testing region David A. Rhoades GNS Science, P.O. Box 30-368, Lower Hutt 5040, New Zealand (Received April 12, 2010; Revised August 6, 2010; Accepted August 10, 2010; Online published March 4, 2011) The Every Earthquake a Precursor According to Scale (EEPAS) model is a long-range forecasting method which has been previously applied to a number of regions, including Japan. The Collaboratory for the Study of Earthquake Predictability (CSEP) forecasting experiment in Japan provides an opportunity to test the model at lower magnitudes than previously and to compare it with other competing models. The model sums contributions to the rate density from past earthquakes based on predictive scaling relations derived from the precursory scale increase phenomenon. Two features of the earthquake catalogue in the Japan mainland region create difficulties in applying the model, namely magnitude-dependence in the proportion of aftershocks and in the Gutenberg-Richter b-value. To accommodate these features, the model was fitted separately to earthquakes in three different target magnitude classes over the period 2000–2009. There are some substantial unexplained differences in parameters between classes, but the time and magnitude distributions of the individual earthquake contributions are such that the model is suitable for three-month testing at M ≥ 4 and for one-year testing at M ≥ 5. In retrospective analyses, the mean probability gain of the EEPAS model over a spatially smoothed seismicity model increases with magnitude. The same trend is expected in prospective testing. -
Aftershock Deficiency of Induced Earthquake Sequences During Rapid Mitigation Efforts in Oklahoma
Earth and Planetary Science Letters 522 (2019) 135–143 Contents lists available at ScienceDirect Earth and Planetary Science Letters www.elsevier.com/locate/epsl Aftershock deficiency of induced earthquake sequences during rapid mitigation efforts in Oklahoma ∗ T.H.W. Goebel a, , Z. Rosson b, E.E. Brodsky a, J.I. Walter b a University of California, Santa Cruz, Santa Cruz, CA, USA b Oklahoma Geological Survey, University of Oklahoma, Norman, OK, USA a r t i c l e i n f o a b s t r a c t Article history: Induced seismicity provides a rare opportunity to study earthquake triggering and underlying stress Received 13 March 2019 perturbations. Triggering can be a direct result of induced stress changes or indirect due to elastic stress Received in revised form 18 June 2019 transfer from preceding events leading to aftershocks. Both of these processes are observable in areas Accepted 29 June 2019 with larger magnitude induced events, such as Oklahoma. We study aftershock sequences of M2.5 to Available online xxxx M5.8 earthquakes and examine the impact of targeted injection rate reductions. In comparing aftershock Editor: M. Ishii productivity between California and Oklahoma, we find similar exponential scaling statistics between Keywords: mainshock magnitude and average number of aftershocks. For events with M≥4.5 Oklahoma exhibits aftershock deficiency several mainshocks with total number of aftershocks significantly below the average scaling behavior. induced seismicity mitigation The sequences with deficient aftershock numbers also experienced rapid, strong mitigation and reduced poroelastic stress coupling injection rates, whereas two events with M4.8 and M5.0 with weak mitigation exhibit normal aftershock productivity. -
Global Earthquake Prediction Systems
Open Journal of Earthquake Research, 2020, 9, 170-180 https://www.scirp.org/journal/ojer ISSN Online: 2169-9631 ISSN Print: 2169-9623 Global Earthquake Prediction Systems Oleg Elshin1, Andrew A. Tronin2 1President at Terra Seismic, Alicante, Spain/Baar, Switzerland 2Chief Scientist at Terra Seismic, Director at Saint-Petersburg Scientific-Research Centre for Ecological Safety of the Russian Academy of Sciences, St Petersburg, Russia How to cite this paper: Elshin, O. and Abstract Tronin, A.A. (2020) Global Earthquake Prediction Systems. Open Journal of Earth- Terra Seismic can predict most major earthquakes (M6.2 or greater) at least 2 quake Research, 9, 170-180. - 5 months before they will strike. Global earthquake prediction is based on https://doi.org/10.4236/ojer.2020.92010 determinations of the stressed areas that will start to behave abnormally be- Received: March 2, 2020 fore major earthquakes. The size of the observed stressed areas roughly cor- Accepted: March 14, 2020 responds to estimates calculated from Dobrovolsky’s formula. To identify Published: March 17, 2020 abnormalities and make predictions, Terra Seismic applies various methodolo- Copyright © 2020 by author(s) and gies, including satellite remote sensing methods and data from ground-based Scientific Research Publishing Inc. instruments. We currently process terabytes of information daily, and use This work is licensed under the Creative more than 80 different multiparameter prediction systems. Alerts are issued if Commons Attribution International the abnormalities are confirmed by at least five different systems. We ob- License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ served that geophysical patterns of earthquake development and stress accu- Open Access mulation are generally the same for all key seismic regions.