scholarly journals Real-time Prediction of Earthquake Ground Motion Using Seismic Records Observed in Deep Boreholes

2014 ◽  
Vol 55 (3) ◽  
pp. 164-170
Author(s):  
Hiroyuki MIYAKOSHI ◽  
Seiji TSUNO
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Earthquake Ground Motion ◽  
Time Prediction ◽  
Seismic Records ◽  
Deep Boreholes

scholarly journals Ground motion prediction maps using seismic microzonation data and machine learning

2021 ◽  
Author(s):  
Federico Mori ◽  
Amerigo Mendicelli ◽  
Gaetano Falcone ◽  
Gianluca Acunzo ◽  
Rose Line Spacagna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Ground Motion ◽  
Scale Effects ◽  
Earthquake Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Intensity Measures ◽  
Local Site ◽  
Ground Motion Variability

Abstract. Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by local stratigraphic and/or topographic setting and buried morphologies, that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near real time prediction of damage pattern over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16'000 accelometric data and about 46'000 geological and geophysical data were retrieved from Italian and European databases. The intensity measures of interest were estimated based on 9 input proxies. The adopted machine learning regression model (i.e., Gaussian Process Regression) allows to improve both the precision and the accuracy in the estimation of the intensity measures with respect to the available near real time predictions methods (i.e., Ground Motion Prediction Equation and shaking maps). In addition, maps with a 50 × 50 m resolution were generated providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed sub-soil models. The variability at short distances (hundreds of meters) was demonstrated to be responsible for 30–40 % of the total variability of the predicted IM maps, making it desirable that seismic hazard maps also consider short-scale effects.

Real-Time Ground Motion Forecasting Using Front-Site Waveform Data Based on Artificial Neural Network

2009 ◽  
Vol 4 (4) ◽  
pp. 588-594 ◽  
Author(s):  
H. Serdar Kuyuk ◽  
◽  
Masato Motosaka ◽  
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Structural Control ◽  
Japan Meteorological Agency ◽  
Earthquake Ground Motion ◽  
S Wave ◽  
Property Damage ◽  
Ground Acceleration ◽  
Waveform Data ◽  
Artificial Neural

Real-time earthquake information made available by the Japan Meteorological Agency (JMA) publicly since October 2007 is intended to dramatically reduce human casualties and property damage following earthquakes. Its current limitations, however, such as a lack of applicability to near-source earthquakes and the insufficient accuracy of seismic ground motion intensity leave much to be desired. The authors have suggested that the forward use of front-site waveform data leads to improve accuracy of real-time ground motion prediction. This paper presents an advanced methodology based on artificial neural networks (ANN) for the forward forecasting of ground motion parameters, not only peak ground acceleration and velocity but also spectral information before S wave arrival using the initial P waveform at a front site. Estimated earthquake ground motion information can be used as a warning to lessen human casualties and property damage. Fourier amplitude spectra estimated highly accurately before strong shaking can be used for advanced engineering applications, e.g., feed-forward structural control. The validity and applicability of the proposed method have been verified using Kyoshin Network (K-NET) observation datasets for 39 earthquakes occurring in the Miyagi Oki area.

scholarly journals Real-Time Prediction of Impending Ground Shaking: Review of Wavefield-Based (Ground-Motion-Based) Method for Earthquake Early Warning

2021 ◽  
Vol 9 ◽  
Author(s):  
Mitsuyuki Hoshiba
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Ground Motions ◽  
Source Parameters ◽  
Earthquake Early Warning ◽  
Peak Ground Velocity ◽  
Ground Shaking ◽  
Time Prediction ◽  
Seismic Waveform

Earthquake early warning (EEW) systems aim to provide advance warning of impending ground shaking, and the technique used for real-time prediction of shaking is a crucial element of EEW systems. Many EEW systems are designed to predict the strength of seismic ground motions (peak ground acceleration, peak ground velocity, or seismic intensity) based on rapidly estimated source parameters (the source-based method), such as hypocentral location, origin time, magnitude, and extent of fault rupture. Recently, however, the wavefield-based (or ground-motion-based) method has been developed to predict future ground motions based directly on the current wavefield, i.e., ground motions monitored in real-time at neighboring sites, skipping the process of estimation of the source parameters. The wavefield-based method works well even for large earthquakes with long duration and huge rupture extents, highly energetic earthquakes that deviate from standard empirical relations, and multiple simultaneous earthquakes, for which the conventional source-based method sometimes performs inadequately. The wavefield-based method also enables prediction of the ongoing seismic waveform itself using the physics of wave propagation, thus providing information on the duration, in addition to the strength of strong ground motion for various frequency bands. In this paper, I review recent developments of the wavefield-based method, from simple applications using relatively sparse observation networks to sophisticated data assimilation techniques exploiting dense networks.

scholarly journals Real-Time Prediction of the Trend of Ground Motion Intensity Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Tao Liu ◽  
Zhijun Dai
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Ground Motion ◽  
Structural Damage ◽  
Time Prediction ◽  
The Neural Network ◽  
Input Sample ◽  
Response Measures ◽  
Deep Learning Model ◽  
Emergency Measures

In order to predict the intensity of earthquake damage in advance and improve the effectiveness of earthquake emergency measures, this paper proposes a deep learning model for real-time prediction of the trend of ground motion intensity. The input sample is the real-time monitoring recordings of the current received ground motion acceleration. According to the different sampling frequencies, the neural network is constructed by several subnetworks, and the output of each subnetwork is combined into one. After the training and verification of the model, the results show that the model has an accuracy rate of 75% on the testing set, which is effective on real-time prediction of the ground motion intensity. Moreover, the correlation between the Arias intensity and structural damage is stronger than the correlation between peak acceleration and structural damage, so the model is useful for determining real-time response measures on earthquake disaster prevention and mitigation compared with the current more common antiseismic measures based on predictive PGA.

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