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Seismic analysis and design using the endurance time method
\"The endurance time method (ETM) is a seismic analysis procedure in which intensifying dynamic excitations are used as the loading function, and it provides many unique benefits in the design of structures. It can largely reduce the computational effort needed for the response history analysis of structures. This aids in the practical application of response history-based analysis in problems involving very large models and/or requiring numerous analyses to achieve optimal design goals. A single response history analysis through ETM provides an estimate of the system response at the entire range of seismic intensities of interest, thus making it ideal for applications such as seismic risk assessment, life-cycle cost analysis, and value-based seismic design. Conceptual simplicity also makes ETM a useful tool for preliminary response history analysis of structural systems. Features: Presents full coverage of the subject from basic concepts to advanced applied topics. Provides a coherent text on endurance time excitation functions that are essential in endurance time analysis. Seismic Analysis and Design using the Endurance Time Method serves as a comprehensive resource for students, researchers, and practicing structural engineers who want to familiarize themselves with the concepts and applications of the endurance time method (ETM) as a useful tool for dynamic structural analysis\"-- Provided by publisher.
Book
Robust Earthquake Cluster Analysis Based on K-Nearest Neighbor Search
Grouping of earthquakes into distinct clusters is applied to improve mechanism identification and pattern recognition for active seismicity in a region. One of the important issues concerning earthquake data clustering is determining the optimum number of clusters (ONC) at the early stages of algorithms. In this paper a robust method based on K-nearest neighbor search (KNNS) is presented to achieve three goals: improving output accuracy, improving output stability, and adding the ability to weight the features used in ONC determination. By introducing a new formula, the proposed method utilizes the error calculated for clustered data based on the similarity between the members in each cluster. An outlier attenuation algorithm is also used to improve the performance of the method. Both the Krzanowski–Lai Index (KLI) and the silhouette coefficient (SC), as two conventional methods, were used to compare the results and evaluate the performance. Experiments on synthetic data sets verified the effectiveness of the method, with considerable differences found. The clustering of a real earthquake catalogue related to the seismogenic province of Zagros in Persia using our proposed methodology suggests using 13-cluster analysis for clustering based on the spatiotemporal features with the same weights, and seven-cluster analysis for a case where priority is given only to the spatial parameters of the epicenters. Under the same circumstances, the KLI and SC methods suggest three and 18 clusters, respectively. The results of the experiments on synthetic data sets indicate that the proposed method is quantitatively more stable and more accurate than the other two methods.
Journal Article
Using Deep Learning for Flexible and Scalable Earthquake Forecasting
Seismology is witnessing explosive growth in the diversity and scale of earthquake catalogs. A key motivation for this community effort is that more data should translate into better earthquake forecasts. Such improvements are yet to be seen. Here, we introduce the Recurrent Earthquake foreCAST (RECAST), a deep‐learning model based on recent developments in neural temporal point processes. The model enables access to a greater volume and diversity of earthquake observations, overcoming the theoretical and computational limitations of traditional approaches. We benchmark against a temporal Epidemic Type Aftershock Sequence model. Tests on synthetic data suggest that with a modest‐sized data set, RECAST accurately models earthquake‐like point processes directly from cataloged data. Tests on earthquake catalogs in Southern California indicate improved fit and forecast accuracy compared to our benchmark when the training set is sufficiently long (>104 events). The basic components in RECAST add flexibility and scalability for earthquake forecasting without sacrificing performance. Plain Language Summary We explore the potential for deep learning in earthquake forecasting. Prior work has relied heavily on statistical models that do not scale to fully utilize the currently available large earthquake data sets. Here we build on recent developments in deep learning for forecasting event sequences in general to create an implementation for earthquake data. The new approach allows us to incorporate larger data sets, potentially with more information about each earthquake. We also avoid a specific functional form, so the method naturally adapts to additional information about events, like magnitude or variations in behavior over time. As we add more data, results show continued improvements. This ability to incorporate and improve continually as training data sets increase indicates that there is more information in the earthquake catalogs than has yet been used for earthquake forecasting. Key Points We introduce a deep learning model for earthquake forecasting and explore its performance on synthetic and regional earthquake data sets It is flexible in the sense that a predefined functional form is not required It is scalable in two senses: it is efficient on large data sets, and its performance relative to benchmarks improves with more training data
Journal Article
GRAPES: Earthquake Early Warning by Passing Seismic Vectors Through the Grapevine
Estimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield‐based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake. Plain Language Summary Have you ever heard something through the grapevine? It often takes you by surprise to hear a message from someone other than the original source. You might have felt an earthquake in a similar way: experiencing shaking (the message) at your location rather than movement along a fault (the source). We apply grapevine‐style communication to earthquake early warning (EEW). The goal of EEW is to warn people to prepare for earthquake shaking before damaging seismic waves arrive at their location. We build on recent work that used deep learning and large earthquake data sets to predict earthquake shaking. We developed a deep learning algorithm called GRAPES which predicts shaking in a manner similar to a game of seismic telephone: when a seismic sensor detects shaking, it sends a message to its neighboring sensors, warning them to expect shaking soon. These sensors then pass on the message to their more distant neighbors along the grapevine. We show that the messages GRAPES learned to send between sensors are like seismic status updates: “I'm seeing this type of seismic wave right now”. We applied GRAPES to the 2019 M7.1 Ridgecrest, CA earthquake and it predicted shaking accurately and quickly. Key Points A deep learning network trained to predict ground motion learned an internal representation of the seismic wavefield Individual neurons within the network activate with the arrival of P waves, S waves, surface waves, coda waves, and ambient noise While trained on earthquakes in Japan, the model generalizes well to predicting ground motions for the 2019 Ridgecrest, CA earthquake
Journal Article
Earthquake Magnitude With DAS: A Transferable Data‐Based Scaling Relation
Distributed Acoustic Sensing (DAS) is a promising technique to improve the rapid detection and characterization of earthquakes. Previous DAS studies mainly focus on the phase information but less on the amplitude information. In this study, we compile earthquake data from two DAS arrays in California, USA, and one submarine array in Sanriku, Japan. We develop a data‐driven method to obtain the first scaling relation between DAS amplitude and earthquake magnitude. Our results reveal that the earthquake amplitudes recorded by DAS in different regions follow a similar scaling relation. The scaling relation can provide a rapid earthquake magnitude estimation and effectively avoid uncertainties caused by the conversion to ground motions. Our results show that the scaling relation appears transferable to new regions with calibrations. The scaling relation highlights the great potential of DAS in earthquake source characterization and early warning. Plain Language Summary Distributed Acoustic Sensing (DAS) is an emerging technique that can convert an optical fiber cable into a dense array to record seismic waves from earthquakes. The recorded seismic signals contain essential information about earthquakes. For example, DAS can record high‐amplitude signals from earthquakes with large magnitudes. However, the exact setting of the optical cables (i.e., installation conditions and coupling with the surrounding medium) is often unknown, thus preventing quantitative estimations of earthquake magnitudes with DAS. In this study, we analyze earthquake data recorded by different DAS arrays and develop a data‐driven method to obtain an empirical relation between the earthquake magnitude and the amplitude of DAS signals. We show that this empirical relation can accurately estimate the earthquake magnitude directly from the DAS data. Furthermore, the empirical relation we obtain from one area can also be applied to new regions with slight calibrations. Our empirical relation can significantly expand the applications of DAS in earthquake research, such as seismic hazard assessment and earthquake early warning. Key Points We present the first data‐based scaling relation between Distributed Acoustic Sensing (DAS) amplitude and earthquake magnitude Earthquake magnitude can be reliably estimated from DAS amplitude with the scaling relation The DAS scaling relation can be transferred from one region to another with minor calibrations
Journal Article
Earthquake magnitude prediction in Turkey: a comparative study of deep learning methods, ARIMA and singular spectrum analysis
The Aegean region is geologically situated at the western end of the Gediz Graben system, influenced by the Western Anatolian Regime. In addition, the region is characterized by various active fault lines that can generate earthquake activity. Numerous earthquakes have been recorded in the region, causing significant material and moral damage from the past to the present. In this study, earthquake data from three different catalogs are examined. The non-clustered catalog is compiled for the years 1970 to 2020, including earthquakes with a moment magnitude (Mw) greater than 3.0. The monthly average magnitudes of earthquakes in the region are obtained and analyzed using ARIMA, singular spectrum analysis (SSA), and deep learning methods including convolutional neural network (CNN) and long short-term memory (LSTM), as these methods have not been compared for the region previously. Each method has a different benefit. ARIMA analyzes time series trends and seasonal patterns, while SSA focuses on decomposition and feature extraction. LSTM attempts to capture complex relationships using memory mechanisms, while CNN is powerful at pattern recognition and extracting important features. Thanks to this diversity, our study allows for more comprehensive and reliable forecasts of average earthquake magnitudes for the next 36 periods. The estimation capabilities and error rates of each method were analyzed based on earthquake magnitude data, and it was determined that the LSTM method provided the most effective and accurate predictions.
Journal Article
New Insights Into Active Faults Revealed by a Deep‐Learning‐Based Earthquake Catalog in Central Myanmar
Myanmar bears a high risk of destructive earthquakes, yet detailed seismicity catalogs are rare. We designed a deep‐learning‐based data processing pipeline and applied it to the data recorded by a large‐aperture (∼400 km) seismic array in central Myanmar to produce a high‐resolution earthquake catalog. We precisely located 1891 earthquakes at shallow (<50 km) depth, a 2‐fold increase compared to the traditional procedures. The new catalog reveals the Kabaw Fault seismicity disappears south of ∼22.8°N, where the deeper (20–40 km) seismicity appears west of the southern Kabaw Fault. Such seismicity contrast along the strike of the Kabaw Fault possibly implies an along‐strike change of deformation responses to the shortening process by the India plate oblique subduction. The middle segment of the Sagaing Fault is likely locked and prone to hosting large earthquakes according to the derived low b‐value. Plain Language Summary Myanmar is a highly seismically active region, yet fault geometry and activities remain poorly understood because of limited modern seismological investigations. Here, we designed a set of machine‐learning algorithms to detect small earthquakes and determine their locations precisely. The seismic data are recorded by a temporary seismic network deployed in central Myanmar. We obtained twice as many earthquakes as the previous research used the regular procedure. Our improved earthquake data set unveils seismic activity changes along the Kabaw Fault through the changes in earthquake locations, depths, and magnitude‐frequency relations. Kabaw Fault is an import boundary fault in the subduction system of the Indo‐Burma Range. This subtle change was not previously observed but means a significant alternation in deformation style along the subduction strike. Moreover, our improved data set indicates that the Sagaing Fault, the most active fault in Myanmar, is prone to generating large earthquakes in the future. This implication warns the nearby populated cities, like Mandalay, of a significant megaquake threat. Key Points We detect 1891 shallow earthquakes in Myanmar with a deep‐learning‐empowered pipeline, a 2‐fold increase against the routine procedure N‐S seismicity discrepancy is observed near the Kabaw Fault and may imply different responses to E‐W shortening by the Indian subduction Low b‐value derived from the new catalog on the middle Sagaing Fault indicates a high risk of destructive earthquakes
Journal Article
The European Preinstrumental Earthquake Catalogue EPICA, the 1000–1899 catalogue for the European Seismic Hazard Model 2020
The European PreInstrumental Earthquake CAtalogue (EPICA) (Rovida and Antonucci, 2021; https://doi.org/10.13127/epica.1.1) is the 1000–1899 seismic catalogue compiled for the European Seismic Hazard Model 2020 (ESHM20), an outcome of the project Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe (SERA), in the framework of the European Union's Horizon 2020 research and innovation programme. EPICA is the update of the SHARE European Earthquake Catalogue (SHEEC) 1000–1899, with which it shares the main principles – mostly transparency, repeatability and continent-wide harmonisation of data – as well as the compilation strategies and methods. Version 1.1 of EPICA contains 5703 earthquakes with either maximum intensity ≥5 or Mw≥4.0, with a spatial coverage from the Atlantic Ocean to the west to 32∘ E in longitude, and from the Mediterranean Sea to Northern Europe. EPICA relies upon the updated knowledge of the European preinstrumental seismicity provided by the data gathered in the European Archive of Historical Earthquake Data (AHEAD). Such data are both macroseismic intensity data supplied by descriptive historical seismological studies and online macroseismic databases, and parameters contained in regional catalogues. As done for the compilation of SHEEC 1000–1899, these datasets were thoroughly analysed in order to select the most representative of the knowledge of each earthquake, independently from national constraints. Selected intensity distributions are processed with three methods to determine location and magnitude based on the attenuation of macroseismic intensity and are combined with parameters harmonised from modern regional catalogues. This paper describes the compilation procedure of EPICA version 1.1, its input data, the assessment of the earthquake parameters and the resulting catalogue, which is finally compared with its previous version. Technical solutions for accessing the catalogue, both as downloadable files and through web services, are also illustrated.
Journal Article
GIS-based earthquake potential analysis in Northwest Himalayan, Pakistan
The present study focuses on the use of integrated remote sensing and the Geographical Information System (GIS) approach for the identification of earthquake potential areas. In the adopted approach Sentinel-2 and Shuttle Radar Topography Mission (SRTM) satellite data, earthquake data, and geological data are used. Important factors related to earthquakes were recognized and relative input data layers (digital elevation model, slope, earthquake magnitude, epicentre location, lineaments, faults, distance to active faults and epicentre) were developed. For data integration in GIS, a numerical ranking scheme has been adopted to establish rank values for each factor for the appraisal of earthquake potential index (EPI) map. The final earthquake potential index map divides the study region into different corresponding potential classes: high, moderate, low, and very low. The earthquake potential map produced for the region was compared with the previous seismic hazard maps derived from traditional techniques. The use of various parameters and implementation of the suggested method in the study region elucidates its good and detailed estimation of earthquake potential areas compared to traditional techniques.
Journal Article
Balancing of geodetic and seismic moment rates and its implications for probabilistic seismic hazard analysis in Taiwan
An integration of geodetic data with observed seismicity which reveals how quickly a region is being deformed due to tectonic plate motions and earthquake activities, plays a pivotal role in earthquake forecast modeling. However, the elastic and inelastic components in geodetically measured total strain budget are implicit in nature, has become one of the major issues. In such scenarios, when reliable quantification of total accumulated energy related to seismic hazard appears to be the need of the hour, an empirical correlation factor is introduced in conversion of geodetic to seismic moment rates to prevent an overestimation of earthquake hazard. In this regard, the present study developed regional earthquake likelihood model for Taiwan by incorporating geodetic measurements and updated earthquake data. For this, a time-independent model is performed to compute probabilities for
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earthquakes within 30 years in 0.1° × 0.1° cells across Taiwan using corrected-geodetic moment rates, truncated G–R law, and the stochastic Poisson process. The 30-year probability forecasts highlight regions with high seismic hazard, including the locked zone of the Longitudinal Valley fault with gradual decaying toward its southern end and in central Taiwan along the Western Foothills. The high strain rates and low earthquake occurrence rates in southwestern Taiwan emphasize a considerable amount of ongoing inelastic strain deformation in this region. In addition, the regional variation in probability distribution not only exhibits an importance of elastic layer parameters, such as seismogenic thickness and rigidity but also evidently link the heterogeneous tectonics with the orogenic process in Taiwan under the plate convergence. Essentially, this study suggests that an integration of geodetic data in probability model can offer rigorous insights for enhancing the current practice of forecasting strategies for seismic hazard analysis in Taiwan.
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