INTRINSIC MODE FUNCTIONS OF EARTHQUAKE SLIP DISTRIBUTION

2010 ◽  
Vol 02 (02) ◽  
pp. 193-215 ◽  
Author(s):  
S. T. G. RAGHU KANTH
Keyword(s):  
Spatial Variability ◽  
Random Field ◽  
Ground Motion ◽  
Slip Distribution ◽  
Decomposition Technique ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Finite Fault ◽  
Fluctuation Component ◽  
Time Histories

In this paper, empirical mode decomposition technique is used to analyze the spatial slip distribution of five past earthquakes. It is shown that the finite fault slip models exhibit five empirical modes of oscillation. The last intrinsic mode is positive and characterizes the non-stationary mean of the slip distribution. This helps in splitting the spatial variability of slip into trend and the remaining modes sum as the fluctuation in the data. The fluctuation component indicates that it can be modeled as an anisotropic random field. Important parameters of this random field have been estimated. The effect of these modes on ground motion is presented by simulating both acceleration and displacement time histories.

scholarly journals A Stochastic source model for the 2015 Mw 7.9 Gorkha, Nepal, Earthquake using Multi-Dimensional Ensemble Empirical Mode Decomposition technique

2018 ◽  
Vol 148 ◽  
pp. 08004
Author(s):  
S Sangeetha ◽  
S.T.G Raghukanth
Keyword(s):  
Empirical Mode Decomposition ◽  
Source Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Spectral Density Function ◽  
Slip Distribution ◽  
Intrinsic Mode Functions ◽  
Nepal Earthquake ◽  
Mode Decomposition ◽  
Fluctuation Component ◽  
Exponential Power

The present study aims at developing a new strategy to model the spatial variability of slip on the rupture plane using multi-dimensional ensemble empirical mode decomposition (MEEMD) technique. Here, the earthquake slip distribution is split into finite number of empirical modes of oscillation called the intrinsic mode functions (IMFs). This help in identifying the fluctuation component and trend in the slip data. The trend is positive and characterizes the nonstationary mean of the slip distribution. The fluctuation component can be modelled as a stationary random field using an exponential power spectral density function. The trend can be modeled as an elliptic patch. This new technique is demonstrated for the slip distribution of the recent Nepal Earthquake, 2015. It is observed that the new model can be used to simulate the spatial complexity of slip distribution of any earthquake.

An Improved Stochastic Finite-Fault Method Based on Energy

2015 ◽  
Vol 744-746 ◽  
pp. 878-883
Author(s):  
Ju Fang Zhong ◽  
Jun Wei Liang ◽  
Zhi Peng Fan ◽  
Luo Long Zhan
Keyword(s):  
Ground Motion ◽  
Amplification Factor ◽  
Response Spectrum ◽  
Near Field ◽  
Energy Accumulation ◽  
Accumulation Curve ◽  
Finite Fault ◽  
Acceleration Time Histories ◽  
Time Histories ◽  
Finite Fault Method

Owing to the simulated ground motion energy distribution by stochastic finite-fault method is not reasonable, near-field bedrock strong ground motion acceleration time histories are used to study. Fourier transform is adapted to analysis the variation of the energy accumulation curve with frequency. The results show that the record energy accumulation curve is a steep rise curve, 80% of total energy of the vertical ground motion is concentrated on the 2.5-15Hz, while the horizontal is mainly concentrated on the 2-11Hz. An improved stochastic finite-fault method is proposed by multiplying an amplification factor in some frequency. The results show that multiplying an amplification factor, the simulated acceleration energy accumulation curve matches to the record acceleration energy accumulation curve, and the peak of simulated acceleration response spectrum tends to the record acceleration value.

EMPIRICAL MODE DECOMPOSITION OF EARTHQUAKE ACCELEROGRAMS

2012 ◽  
Vol 04 (04) ◽  
pp. 1250022 ◽  
Author(s):  
S. T. G. RAGHUKANTH ◽  
S. SANGEETHA
Keyword(s):  
Ground Motion ◽  
Empirical Mode Decomposition ◽  
Strong Motion ◽  
Hilbert Huang Transform ◽  
Motion Data ◽  
Mode Decomposition ◽  
Acceleration Time Histories ◽  
Recording Station ◽  
Time Histories ◽  
Motion Parameters

This article analyzes the strong motion records of past earthquakes by empirical mode decomposition (EMD) technique. The recorded earthquake acceleration time histories are decomposed into a finite number of empirical modes of oscillation. The instantaneous frequency and amplitude of these modes and evolutionary power spectral density (PSD) is estimated from the Hilbert–Huang transform (HHT). Strong motion parameters such as spectral and temporal centroid, spectral and temporal standard deviation, Arias intensity, correlation coefficient of frequency and time are derived from the evolutionary PSD. The variation of these parameters with magnitude, distance and shear wave velocity of the recording station is reported. Empirical equations to estimate these six ground motion parameters are derived from the strong motion data by regression analysis. These equations can be used by engineers to estimate the design ground motion.

A Parametric Investigation of Near‐Fault Ground Strains and Rotations Using Finite‐Fault Simulations

2019 ◽  
Vol 109 (5) ◽  
pp. 1758-1784 ◽  
Author(s):  
Yenan Cao ◽  
George P. Mavroeidis
Keyword(s):  
Slip Velocity ◽  
Velocity Slip ◽  
Strike Slip ◽  
Slip Distribution ◽  
Burial Depth ◽  
Spatial Distributions ◽  
Rupture Velocity ◽  
Near Fault ◽  
Finite Fault ◽  
Time Histories

Abstract Although previous studies have performed finite‐fault simulations of actual or hypothetical earthquakes to generate time histories of near‐fault ground strains and rotations, no systematic attempt has been made to assess the sensitivity of these motions to variations in seismic source parameters (e.g., fault type, magnitude, rupture velocity, slip velocity, hypocenter location, burial depth). Such a parametric investigation is presented in this article by generating time histories of ground strains and rotations at near‐fault stations and at a dense grid of observation points extending over the causative fault for a suite of hypothetical strike‐slip and dip‐slip earthquakes. The simulation results show that strike‐slip earthquakes produce large shear strain and torsion, whereas dip‐slip earthquakes generate large axial strain and rocking. The time histories of specific components of displacement gradient, strain, and rotation at near‐fault stations may be estimated from those of ground velocities using a simple scaling relation, whereas peak rotational motions in the near‐fault region may be reasonably estimated from peak translational motions using a properly selected scaling factor. The parametric analysis results show that near‐fault ground strains and rotations exhibit strong sensitivity to variations in rupture velocity, slip velocity, and burial depth, whereas a change in hypocenter location significantly alters the spatial distributions of peak ground strains (PGSs) and rotations (PGRs). The presence of a low‐velocity surface layer increases the amplitude and duration of ground strains and rotations, whereas their static offsets are also amplified. Distinct attenuation characteristics are observed for PGSs and PGRs depending on the component of interest, the earthquake magnitude, and the rupture distance. Finally, the spatial distributions of PGSs and PGRs obtained from a stochastically generated variable slip distribution are overall similar to those obtained from a tapered uniform slip distribution, whereas the spatial distributions of the respective static offsets differ significantly.

SIMULATION OF EARTHQUAKE RECORDS BY MEANS OF EMPIRICAL MODE DECOMPOSITION AND HILBERT SPECTRAL ANALYSIS

2014 ◽  
Vol 08 (01) ◽  
pp. 1450002 ◽  
Author(s):  
ABDOLLAH BAGHERI ◽  
AMIR A. FATEMI ◽  
GHOLAMREZA GHODRATI AMIRI
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
Time History ◽  
Response Spectra ◽  
Frequency Function ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Earthquake Records ◽  
Mode Decomposition ◽  
Time Histories

One of the most important problems in the design of earthquake resistance structures at sites with no strong ground motion data is the generation and simulation of earthquake records. In this paper, an effective method based on Hilbert–Huang transform for the simulation of earthquake time histories is presented. The Hilbert–Huang transform consists of the empirical mode decomposition and Hilbert spectral analysis. Earthquake time histories decompose via empirical mode decomposition to obtain the intrinsic mode functions of earthquake time history. Any of intrinsic mode functions is simulated based on the proposed method for simulation. The ground frequency function of the presented model is estimated using Hilbert spectral analysis for the simulation of earthquake accelerograms. The proposed method has been applied to three earthquake records to demonstrate the efficiency and reliability of the approach. The obtained results of simulating method by comparison between pseudo-acceleration and pseudo-velocity response spectra of actual and the average of simulated time histories for these three earthquakes reveal that the simulated earthquake time histories well preserve the significant properties and the nonstationary characteristics of the actual earthquake records. The results indicated that there is a good accord between the response spectra of simulated and genuine time histories.

Prediction of Broadband Ground-Motion Time Histories: The Case of Tehran, Iran

2013 ◽  
Vol 29 (2) ◽  
pp. 633-660 ◽  
Author(s):  
Hamid Zafarani ◽  
Hesam Vahidifard ◽  
Anooshirvan Ansari
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Historical Earthquakes ◽  
Response Spectra ◽  
Wave Number ◽  
Corner Frequency ◽  
Earthquake Scenarios ◽  
Motion Time ◽  
Finite Fault ◽  
Time Histories

The northern Tehran fault (NTF) is potentially capable of causing large earth-quakes (Mmax ~ 7.2) in a very densely populated area of northern Tehran, Iran. Due to the lack of recorded strong motion data for earthquakes on the fault, a hybrid simulation method is used to calculate broadband (0.1–20 Hz) ground-motion time histories at bedrock level for deterministic earthquake scenarios on the NTF. Low-frequency components of motion (0.1–1.0 Hz) are calculated using a deterministic approach and the discrete wave number-finite element method in a regional one-dimensional (1-D) velocity model. High frequencies (1.0–20.0 Hz) are calculated by the stochastic finite fault method based on dynamic corner frequency. The results were validated by comparing the simulated peak values and response spectra with the empirical ground motion models available for the area and the Modified Mercalli intensity (MMI) observations from historical earthquakes of the region.

scholarly journals A Grid-Based Method to Represent the Covariance Structure for Earthquake Ground Motion

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Yingmin Li ◽  
Zheqian Wu ◽  
Huiguo Chen
Keyword(s):  
Spatial Variability ◽  
Random Field ◽  
Spatial Variation ◽  
Ground Motion ◽  
Covariance Structure ◽  
Spatial Variations ◽  
Earthquake Ground Motion ◽  
Seismic Motion ◽  
Almost All ◽  
Grid Based

Spatial variation of earthquake ground motion is an important phenomenon that cannot be ignored in the design and safety of strategic structures. However, almost all the procedures for the evaluation of variation assumed that the random field is homogeneous in space. It is obvious that reality does not fully conform to the assumption. How to investigate the inhomogeneous feature of ground motion in space is a challenge for researcher. A body-fitted grid-coordinates-based method is proposed to estimate and describe the local spatial variations for the earthquake ground motion; it need not to make the assumption that the random field of earthquake is homogeneous in space. An analysis of spatial variability of seismic motion in smart-1 array monitored in Lotung, Taiwan demonstrates this methodology.

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection

2020 ◽  
Vol 65 (6) ◽  
pp. 693-704
Author(s):  
Rafik Djemili
Keyword(s):  
Involuntary Movement ◽  
Epileptic Seizures ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Time Segment ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Finite Set ◽  
Student’S T

AbstractEpilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.

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