The equation of motion is solved with the hypothesis that the acceleration varies linearly in the step.At the end of the calculation a very nice animation is performed.You can choose 3 different constitutive models:- linear elastic- elastic - perfectly plastic- Armstrong - Frederick cyclic hardening plasticityYou can choose among 20 Italian earthquakes.I suggest this choice: Armstrong - Frederick with earthquake 16

Matlab codes to read and write SAC seismic data file

The script analizes the dynamic response of a linear system subject to a seismic input through the Response History Analysis (RHA) and the Response Spectrum Analysis (RSA).After the calculations there is a comparison between results from the two methods.At the end will appear an animation that shows the total response of the structure in terms of displacements, shear forces and bending moments.

The algorithm uses the Newmark's Method for linear and nonlinear SDOF systems.Ref: Anil K. Chopra, Dynamics of Structures 4th Edition.

% % Author(s): Xuepeng Cui (China University of Petroleum,Beijing)% % Copyright 2021 Xuepeng Cui% % Revision: 1.0 Date: Nov/2021This code can automatically pick up three-dimensional label samples along the well track, define the label type as reservoir and lithology, and add inclined well track search and label information such as fractures and faults in the later stage.

SedPro: This code allows the treatment, processing and calculation of seismic signal parameters.NewmarkRS and FFTsignal: complementary functions for the code

MATLAB Toolbox for Seismic Data Analysis

Seismic Reservoir Modeling MATLAB packageThe SeReM package includes five folders:Data: this folder contains six datasets used for the examples and the elevation dataset from Yellowstone National Park;RockPhysics: this folder contains functions with several rock physics models;Geostats: this folder contains functions for kriging and geostatistical simulations of random variables;Inversion: this folder contains functions for seismic and rock physics inversion using the analytical and numerical solutions; the functions are subdivided into three subfolders: Seismic, Petrophysical, and EnsembleSmoother;Facies: this folder contains functions for facies classification and simulation. The SeReM package includes multiple scripts with several examples.For a detailed description, we refer to Grana, D., Mukerji, T., and Doyen, P., 2021, Seismic reservoir modeling: Wiley.

Normalized spectrogramNormalized spectrogram of a seismic accelerationNormalized spectrogram to seismic acceleration written in Matlab. The algorithm used is the following:1. Baseline correction2. Tuckey window is applied with r=5%.3. FFT on window signal.4. Spectrum is smoothed by 5 points halfwidth moving average.5. Each element of spectrum is normalized by maximum spectral amplitude.The functionThe normalized spectrogram function is defined by:[matrix, matrix_t, matrix_f] = norm_spectrogram(t, acc)Where:VariableDescriptiontTime of the seismic accelerogramaccAcceleration (g) of the seismic accelerogramThis function returns a matrix in where columns refer to mean window time (matrix_t), rows are the frequency of the spectrogram (matrix_f) and the value of the matrix are the amplitude of the spectrogram from each time-window on each frequency.To print matrix you should use:plot_norm_matrix(m, mt, mf, t, acc, regname)Where:VariableDescriptionmNormalized matrixmtTime array from matrixmfFrequency array from matrixtTime of the seismic dataaccAcceleration of seismic dataregnameName of the seismic data (plot title)ExampleLets suppose that a seismic registry is stored on data/CNV_APED_201604162359_N_100.txt, the file structure is like:0.000000-6.3295000.0100002.5396000.02000012.8229000.0300009.4353000.040000-5.3971000.050000-14.233900...Then:% Load the datadata = load('data/CNV_APED_201604162359_N_100.txt');% Set time and acceleration arrayt = data(:, 1);acc = data(:, 2) ./ 980; % Convert from cm/s2 to gAfter that we will use norm_spectrogram function[m, mt, mf] = norm_spectrogram(t, acc);Then plot:plot_norm_matrix(m, mt, mf, t, acc, 'APED 2016/04/16 23:59 N-S');Obtaining:LicenseThis project is licensed under GPLv2 [https://www.gnu.org/licenses/gpl-2.0.html]AuthorPablo Pizarro R. | 2017 - 2019

Project website: https://geoscience-community-codes.github.io/GISMOGISMO is designed to allow easy retrieval of seismic waveform data, event catalogs and station metadata from a variety of data formats, databases and online data sources, eliminating a major barrier to rapid development of new seismic research programs and workflows, new seismic monitoring tools, etc. The foundation is a set of object oriented classes that describe different seismic data types and have built-in methods for loading from common formats and sources, and common visualization and processing tools. GISMO is a community driven project that aims to encourage scientists to write code that builds on GISMO and then contributed code back to the project, so that GISMO can become ever more capable - and useful to more scientists. An aim is to make research more repeatable and lower the cost of research by providing a set of tools that allow scientists to get to the important part of research faster - doing the science, rather than the frustrations of how data is stored and how to get it in to MATLAB. Features include: * Plotting tools for waveforms, catalogs, instrument responses etc. * Waveforms work for any regularly sampled data stream * Mathematical functions make manipulating easier: +, -, /, .*, .^ * Standard waveform manipulations: filter, crop, subset, subset, stack, normalize, automatic gain control, demean, detrend, taper, etc. * Statistics: max, min, mean, median, etc. * Waveform operations: hilbert transform, integration and differentiation * Operates N-dimensional waveforms (in most cases without requiring loops) * Object architecture provides a stable base for writing more complex programsSeismic data can be imported from Antelope databases (via the BRTT Antelope toolbox), Earthworm/Winston databases, the IRIS-DMC, SAC files, Seisan databases, .mat files, hypoellipse files, or with minimal coding your own homegrown file format/directory structure.GISMO makes: * playing with data easier by automating the tedious aspects of data manipulation * programs more stable by ensuring proper data typing * code more portable by reading multiple formats and functioning on multiple systems * troubleshooting faster by providing more detailed messages and warningsAdditional information can be found via the GISMO wiki: https://github.com/geoscience-community-codes/GISMO/wikiGISMO (which includes The Waveform Suite) and related codes can also be discussed at: http://groups.google.com/group/gismotoolsAs always, Comments are welcome, as are reviews and bug reports. Thanks!

Process corrections, filters, Fourier Spectra and Response 1GL for seismic acceleration records files

Arias IntensityFunction in matlab to calculate AI (Arias Intensity) parameter from a seismic accelerogramUsageThe arias intensity function is defined by:ai = arias_intensity(t, acc)Where:VariableDescriptiontTime of the seismic accelerogramaccAcceleration (g) of the seismic accelerogramExampleLets suppose that a seismic registry is stored on data/CNV_APED_201604162359_N_100.txt, the file structure is like:0.000000-6.3295000.0100002.5396000.02000012.8229000.0300009.4353000.040000-5.3971000.050000-14.233900...Then:ai = arias_intensity(t, acc);>> ai = 0.198232LicenseThis project is licensed under GPLv2 [https://www.gnu.org/licenses/gpl-2.0.html]AuthorPablo Pizarro R. | 2017

G_SeisSimple 2D-Seismic data processing GUI applicationFunctionalitySEGY read/write (reads to binary file of format single)visualize data with three keys sortingsurface-consistent first arrival (or amplitude) decomposition according to 2, 3, 4 factor modelinteractively build velocity model based on decomposed arrival picksperform static, amplitude and spectrum correction (deconvolution)perform some basic header and data arithmeticUsageThe main file is G_Seis.mBefore running the application you should:set path to the root folder and include all the folders inside itbuild mex function in /g_other folder. Commands >> mex -setup and >> mex typecastx.c (or >> mex g_other/typecastx.c depending on current path) may helprun the app: >> G_SeisDocumentation

HOVSRMatlab application that finds main frecuency from seismic timeseries acceleration data using SH/SV Stockwell Transform/Nakamura methodLicenseThis project is licensed under GPLv2 [https://www.gnu.org/licenses/gpl-2.0.html]AuthorPablo Pizarro R. | 2017 - 2019

Stacking repeated records can improve the signal-to-noise ratio (SNR) of seismic, geo-radar, or nuclear magnetic resonance measurements. Stacking usually employs a simple summation of amplitudes, but some procedures are also available based on non-linear stacking. Three methods of non-linear stacking are implemented as MATLAB functions: 1. The PWS (Phase-Weighted Stacks) method. It is based on the similarity of instantaneous phases on the corresponding complex signals. 2. The GAS (Generalized Average of Signals) method. It averages complex spectra in the frequency domain, employing the Generalized Average of Complex Numbers (GACN). 3. The CSA (Complex Signal Average) method. This method is based on complex averaging signals using GACN. Non-linear stacking can improve the SNR by up to the first tens of dB. The advantages of time-domain methods PWs and CSA are their lower computational complexity and the fact that they do not depend on the choice of the computation window length compared to GAS.Demo_script can be used with demo_data - downloade here.

"Extended generalized non-hyperbolic moveout approximation" accurately approximates full-offset ray-traced traveltimes in a CMP acquisition, using only two rays. A demo of EGMA for accurate modeling of CMP traveltime in layered VTI media is also provided.

GAOT-ECM (GAOT - Extension For Control And Modeling) is an extension of the Genetic Algorithm Optimization Toolbox (GAOT) by C.R. Houck, J.A. Joines, M.G. Kay, that implements genetic algorithms for industrial plant identification and PID controller design.This file contains the Seismic Vibration Case Study. For other examples, please consult the GAOT-ECM: Extension For Control And Modeling main file.This extension brings forth the practical aspect of using genetic algorithms in aiding plant modeling and PID control design for real world industrial processes. Plants such water tanks, heaters, fans and motors are usually hard to model or tune on-site, especially after prolonged use of the equipment when degradation of performances is inevitable. Therefore, this toolbox extension introduces a series of practical steps that can be taken by control engineers in order to re-design viable PID controllers for their plants.Inside the GAOT-ECM Seismic Vibration Case Study archive you will find the folder: MR Damper, which runs a PID controller design genetic algorithm under various customizable performance criteria.For this case study, a base isolation system is considered for a three story building. The damper is mounted in the base of the structure, which is controlled via an LQR law on the outer loop of a cascaded control system. A PID controller is chosen to be designed using GAs for the inner loop containing the damper. The output forces generated by seismic dampers are required to be maintained between specific limits, so they do not cause instability to the structure, breaks support beams and so on. Therefore, a control loop for the actuator is required, that will receive the desired control forces computed by higher algorithms (such as robust laws, intelligent controllers, adaptive, modal, etc.) as setpoints and ensure that they are precisely reproduced by the actual damper output force. Analysis for validation and LQR command matrix are included.GAOT-ECM was implemented and tested under Matlab 7 (R2011b).Enjoy!Acknowledgements: GAOT-ECM Toolbox(GAOT - Extension For Control And Modeling ) was inspired by Genetic Algorithm Optimization Toolbox (GAOT) by C.R. Houck, J.A. Joines, M.G. Kay.Citing information: Patrascu M., Ion A. 2016 - Evolutionary Modeling of Industrial Plants and Design of PID Controllers. Case Studies and Practical Applications, Nature-Inspired Computing for Control Systems (Ed. H.E.Ponce Espinosa), Series Studies in Systems, Decision and Control, vol. 40, pp. 73-119, Springer International Publishing, doi: 10.1007/978-3-319-26230-7_4

This script aims to offer the Shock Response Spectrum of an acceleration time history of an earthquake which is known. Therefore, it enables the user to calculate the Peak Ground Acceleration (PGA) and Pseudo Velocity. It also plots the solution and stores them in a file allowing certain changes such as the acceleration unit.The mathematical algorithm used for the calculation can be chosen by the user among the following, which are included in the script: Kelly Richman, Smallwood and Newmark. The user must provide the acceleration time history of the earthquake he wants to test, as well as introduce the damping parameter and the starting frequency for the iteration. The damping parameter can be either a damping ratio or quality factor.The time history of the earthquake, must have two columns: time(sec) and acceleration. This data can be already pre-loaded into Matlab, or introduced by selecting the option ‘Open new ASCII file’ inside the popup menu named ‘File Input Method’.As soon as the method has been selected the script will automatically plot the Peak Ground Acceleration. Nevertheless the user can obtain the results of the Pseudo Velocity.Lastly, the script offers the possibility to store the results obtained by clicking ‘Output data in a file’. The script will request a filename and a directory and automatically create a .dat file.- This file includes the srs.m file, uploaded by Tom Irvine in 2006 as one of the eligible calculation methods.- For further information, please relate to the document "SeismicResponseSpectrumGUI.pdf" included in the .zip file.

This MATLAB function conducts a simple Probabilistic Seismic Hazard Analysis by making use of the truncated Gutenberg-Richter recurrence model (Baker, 2013). It considers only a uniform circular source in which the site-in-question is assumed to be located in the center of the circular source. It uses ASB_2014 GMPE file to calculate mean Sa and corresponding sigma values (Akkar et al., 2014).References:Akkar, S., Sandıkkaya, M. A., & Bommer, J. J. (2014). Empirical ground-motion models for point-and extended-source crustal earthquake scenarios in Europe and the Middle East. Bulletin of earthquakeengineering, 12(1), 359-387.Baker, J. W. (2013). An introduction to probabilistic seismic hazard analysis. White paper version, 2(1), 79.

This package reproduces the numerical study and examples of the following study in seismic anisotropy: Abedi, M. M., 2020, Rational approximation of P-wave kinematics: Part II orthorhombic media: Geophysics

This package reproduces the numerical study and examples of the following study in seismic anisotropy: Abedi, M. M., 2020, Rational approximation of P-wave kinematics: Part I transversely isotropic: Geophysics

Seismic processing bits and pieces. Note that you may need to hunt for dependencies in some of the other slepian_alpha, slepian_bravo, etc, packages.

This MATLAB function conducts a vector-valued probabilistic seismic hazard analysis (VPSHA) by making use of the truncated Gutenberg-Richter recurrence model. It considers only a uniform circular source in which the site-in-question is assumed to be located in the center of the circular source. The VPHSA model is based on Bazzurro and Cornell (2002). It uses ASB_2014 GMPE file to calculate mean Sa and corresponding sigma values (Akkar et al., 2014).References:Akkar, S., Sandıkkaya, M. A., & Bommer, J. J. (2014). Empirical ground-motion models for point-and extended-source crustal earthquake scenarios in Europe and the Middle East. Bulletin of earthquake engineering, 12(1), 359-387.Bazzurro, P., & Cornell, C. A. (2002, July). Vector-valued probabilistic seismic hazard analysis (VPSHA). In Proceedings of the 7th US national conference on earthquake engineering (Vol. 21, p. 25).

Seismic surveying requires placing a large number of sensors (geophones) in a large grid pattern, triggering a seismic event, and recording accelerometer readings at each sensor. These readings are inverted to infer the location of hydrocarbons. Traditional seismic surveying employs human laborers for sensor placement and retrieval. Use of explosives, harsh climatic conditions, high costs and time associated with human deployment are the major drawbacks of traditional surveying. We propose an autonomous heterogeneous sensor deployment system using drones to plant and recover sensors.INPUTS:x,y: size of region to surveyT = Initial HomeBase location in [x,y];hex: number of hexapods (mobile seismic sensors)drones: number of deployment vehicles (fast moving vehicles that can hold up to drone_cap darts)darts: seismic sensors that can be deployed by drones or peoplepeople: humans that can deploy seismic sensors (can carry up to people_cap Srikanth KVS and Aaron T. Beckersrikanth.kvs11@gmail.com and atbecker@uh.eduSee video at https://youtu.be/NvYyT66U8JM

You can watch the archived version of this webinar at http://www.mathworks.com/videos/large-data-in-matlab-a-seismic-data-processing-case-study-81792.html (recommended). The demos show how to manage out of memory data using a memory mapped file and customizing the object for array indexing. This enables reuse of the memory mapped file inside functions or with parallel computing without needing to rewrite code or recreate the memory mapped file on each worker manually. The data files are not inlcluded in this download. Read the README file to locate the public data sources on the internet.The demo also shows how to speed up the solution of the wave equation (finite difference PDE) using a custom CUDA kernel. The relative speedup observed was around 1.6X. The demos start with: 1 - and introduction to seismic analysis (Kirchhoff migration, reverse time migration) 2 - Large data extension of the functionality shown in (1) and parallel computing for speeding up the processing time 3 - GPU extension to (1) showing how to use a custom CUDA kernel to solve the wave equation compared to a MATLAB implementation (written in vectorized form)

One of the main problems in time history analysis is the definition of a proper input and arranging the records for different structural analysis programs. In the case of transient analyses, ground acceleration–time history data are used for seismic analysis. Such time histories may be derived synthetically, by either numerical simulation of the source and wave propagation mechanisms, or by considering proper stochastic models. The best way to evaluate seismic performance of structures is monitoring structural behaviour under real earthquake records. Seismic records from tectonically active regions throughout the world may be downloaded from PEER Strong Motion Database. In this study, a simple assistant program is developed for implementing earthquake analyses of structures with ANSYS and SAP2000. The seismic records are loaded from PEER and earthquake analysis files are produced in ANSYS Parametric Design Language (APDL). Anyone who modeled a structure in ANSYS can use the analysis files produced with ANSeismic by just calling them. ANSYS program may also be called from ANSeismic if APDL file is available. Beside ANSYS, a SAP2000 time history data file may also be produced with ANSeismic.

A Matlab toolbox for first arrival time tomography is presented.We limit our code in the field of ocean bottom seismometer (OBS) survey (Figure 1) based on two factors. First, OBS survey becomes common in the study of deep structure beneath ocean, hence the code is meaningful. Secondly, since OBS is mostly deployed with a large spacing, the computational cost is substantially reduced when compared to the dense receiver case. This makes it possible to run a tomography job in the Matlab setting. For other types of geometry such as the first arrival tomography for near-surface imaging, the computational cost is much large and more efficient code should be chosen.First arrival seismic tomography is a nonlinear inverse problem.Currently, within our toolbox, only the local linearization method has been implemented. It starts from an initial model and travel time tables. The codes contains the following parts : (1) data structure, (2) model parameterization, (3) ray-tracing, (4) jacobi matrix construction, (5) regularization and smoothing and (6) solving a large scale system of linear equations.only Matlab is required.It could also run on Octave.

The present code is a Matlab function that implements a novel technique named “statiogram” concerning the estimation of the short-time stationarity frame duration of a given nonstationary signal. The procedure answers what should be the length of the frames into which a nonstationary signal is divided so that the signal be considered as short-time wide-sense stationary in the boundaries of the (most of the) frames, with some predefined confidence level. This is of particular importance when one is going to perform some kind of time-localized analysis (e.g., STFT or signal feature extraction), in the fields of speech and sound processing; vibration and seismic analysis; neuroscience, etc. A few examples are given in order to clarify the usage of the function. For convenience, the input and output arguments are given in the beginning of the function. The code is based on the theory described in: [1] H. Zhivomirov, I. Nedelchev. A Method for Signal Stationarity Estimation. Romanian Journal of Acoustics and Vibration, ISSN: 1584-7284, Vol. XVII, No. 2, pp. 149-155, 2020. (http://rjav.sra.ro/index.php/rjav/article/view/178/103).[2] --- a research article dedicated to the proposed routine is about to come by the end of December 2022. ---

This function generates elastic response specra including Displacement Spectrum, Pseudo Acceleration Spectrum, and Pseudo Velocity Spectrum which are needed in a "Response Spectrum Analysis" of structures. To solve the "equation of motions" for different periods, the Newmark Linear Method was used.

Function calculates P-wave, S-wave, SH-wave and SV-wave radiation pattern using shear-tensile source model [cf. references 1-3 for details]. All input angles (strike, dip, rake of the fault, tensile angle gamma, takeoff angle TKO and azimuth from the source to the observation point AZM) should be provided in degrees. The takeoff angle is measure from bottom. The azimuth to the observation point is measured from north to east. The function returns matrices of the same size as input TKO and AZM matrices which should be of the same size and denote a set of observation points specified by takeoff angles and azimuth.[1] Kwiatek, G. and Y. Ben-Zion (2013). Assessment of P and S wave energy radiated from very small shear-tensile seismic events in a deep South African mine. J. Geophys. Res. 118, 3630-3641, doi: 10.1002/jgrb.50274[2] Ou, G.-B., 2008, Seismological Studies for Tensile Faults. Terrestrial, Atmospheric and Oceanic Sciences 19, 463.[3] Vavryčuk, V., 2001. Inversion for parameters of tensile earthquakes.” J. Geophys. Res. 106 (B8): 16339–16355. doi: 10.1029/2001JB000372.

StackSplit A plugin for multi-event shear wave splitting analyses in SplitLabStackSplit is a plugin for the MATLAB toolbox SplitLab (Wüstefeld et al., 2008) which allows to apply multi-event techniques for shear wave splitting measurements (SWS) directly from within the main program.For details regarding the installation and usage, see the UserGuide.CitationIf you make use of StackSplit in your work please acknowledge my paper in which the program is described:Grund, M. (2017), StackSplit - a plugin for multi-event shear wave splitting analyses in SplitLab, Computers & Geosciences, 105, 43-50, https://doi.org/10.1016/j.cageo.2017.04.015.Optionally, you can also cite the Zenodo DOI given above which is referring the latest version of this GitHub repository.Which stacking methods are available?StackSplit grants easy access to four stacking schemes with which single SWS measurements made with SplitLab can be processed:WS: stacking of error surfaces, normalized on minimum/maximum (depending on input) of each single surface (Wolfe & Silver, 1998)RH: modified WS method with weight depending on SNR of each measurement and normalization regarding the available backazimuth directions (Restivo & Helffrich, 1999)no weight: stacking of error surfaces without weighting following PhD thesis of Wüstefeld (2007)SIMW: simultaneous inversion of multiple waveforms in time domain (Roy et al., 2017)Compatibility with SplitLab and MATLAB versionsStackSplitSplitLabMATLABdev (main branch)1.2.1, 1.0.5 (not tested)>= 2020a (< 2020a might work, but not tested yet)v3.0 (latest release)1.2.1, 1.0.5 (not tested)>= 2020a (< 2020a might work, but not tested yet)v2.01.2.1, 1.0.5>= 2014b (tested up to and including 2018b)v1.01.2.1, 1.0.5<= 2014aFor details regarding the different StackSplit versions see the Changelog.ContributingDid you find a bug or have suggestions for improvements? Simply open a new issue or pull request here on GitHub.Related topicsThe most recent SplitLab version can be found here (not compatible with StackSplit yet): https://github.com/IPGP/splitlabShear wave splitting analysis in Python (based on SplitLab): https://github.com/paudetseis/SplitPyShear wave splitting analysis in Julia: https://github.com/anowacki/SeisSplit.jlReferencesRestivo, A. & Helffrich, G. (1999), Teleseismic shear wave splitting measurements in noisy environments, Geophysical Journal International 137, 821-830, https://doi.org/10.1046/j.1365-246x.1999.00845.x.Roy, C., Winter, A., Ritter, J. R. R., Schweitzer, J. (2017), On the improvement of SKS splitting measurements by the simultaneous inversion of multiple waveforms (SIMW), Geophysical Journal International, 208, 1508–1523, https://doi.org/10.1093/gji/ggw470.Wolfe, C. J. & Silver, P. G. (1998), Seismic anisotropy of oceanic upper mantle: Shear wave splitting methodologies and observations, Journal of Geophysical Research 103(B1), 749-771, https://doi.org/10.1029/97JB02023.Wüstefeld, A. (2007), Methods and applications of shear wave splitting: The East European Craton. Ph.D. thesis, Univ. de Montpellier, France, http://splitting.gm.univ-montp2.fr/.Wüstefeld, A., Bokelmann, G., Zaroli, C., Barruol, G. (2008), SplitLab: A shear-wave splitting environment in Matlab, Computers & Geosciences 34, 515–528, https://doi.org/10.1016/j.cageo.2007.08.002.

Description in English:This Matlab function was developed to generate "Design Response Spectrum" based on the last edition of the American structural loading standard named "ASCE/SEI 7-16". This function can obtain this spectrum for any seismic design categories and site classes.Description in Persian (Farsi):با استفاده از این تابع متلب می توانید طیف طرح لرزه ای آیین نامه بارگذاری امریکا (ویرایش 2016) را به ازای هر نوع منطقه ی لرزه خیزی و هر نوع خاک ساختگاه بدست آورید.

This zip file contains a sample shot-gather data set and a script to calculate the dispersion image of the data. The script uses the phase-shift dispersion imaging scheme of Park et al., 1998a to create the dispersion curve image. This script has been updated to include MANUAL and AUTOMATIC curve picking options.

Description in English:This Matlab function was developed to generate "Design Response Spectrum" based on the 4th edition of the Iranian seismic standard of the buildings named "Standard 2800". This function can obtain this spectrum for any seismic design categories (seismic regions) and site classes (soil types).Description in Persian (Farsi):با استفاده از این تابع متلب می توانید طیف طرح استاندارد 2800 ایران (ویرایش چهارم) را به ازای هر نوع منطقه ی لرزه خیزی و هر نوع خاک ساختگاه بدست آورید.

Simple program to calculate CQC for Modal Analysis.Input = Frequency, Damping, ComponentComponent MatrixThe component that wants to be calculated ....Row = DOF component; Columns = ModeFrequency MatrixCan either be column or row oriented, the first mode frequency should can be accessed using frequency(1), the second mode should can be accessed using frequency(2)DampingSame concept applies from Frequency MatrixReference Paper:A Replacement for the SRSS Method in Seismic AnalysisE. L. Wilson, A. Der. Kiureghian, and E. P. BayotEarthquake Engineering and Structural Dynamics, Vol. 9 (187-194)

The purpose of this study is to develop a seismic analysis of MDOF Structure based on Response Spectrum Method. This analysis tool is written in MATLAB software to determine structural responses or a combined response of each mode shape.

Robust function to generate multi-component constant-ductility nonlinear-inelastic response spectra for multi-axial simultaneous excitation including horizontal, vertical and rotational motions (i.e. rotational acceleration and tilt).It also computes components of seismic input energy imparted to the SDOF oscillator. Its features are:- Multi-axial excitation (simultaneous horizontal, vertical and rotational input ground motions)- Material nonlinearity is presented by Ozdemir's rate-independent force-displacement model- P-delta (at global level) is included- Spectral analysis for constant yield displacement- Input energy computations and output results for components of absolute and relative energyzip file contains reference papers and example input ground motions from Pacoima Dam and output results.

EXPORT_FIGURE is a simple and user-friendly program that provides the following features to assist people in exporting/saving figures based on journals' standards. EXPORT_FIGURE does NOT generate a figure; it changes the style's properties of a generated figure in a way that to be suitable for scientific publication. EXPORT_FIGURE: 1) can place a label on the upper-left corner of the current axes; 2) can load and apply a style to the current figure; 3) contains pre-defined styles that are suitable for scientific publications; 4) can change a specific property of a style, e.g., FontName, Width; 5) can save the current figure with different file formats ('jpeg', 'pdf', 'eps', 'png', etc.)IF YOU USE THIS PROGRAM IN YOUR RESEARCH, PLEASE CITE THE FOLLOWING PAPER:Afshin Aghayan, Priyank Jaiswal, and Hamid Reza Siahkoohi (2016)."Seismic denoising using the redundant lifting scheme." GEOPHYSICS, 81(3), V249-V260 https://doi.org/10.1190/geo2015-0601.1

Matlab tools for geophysical (mainly exploration seismic) interpretation and processing

Multi-Hazard Reliability Analysis of Networks using Minimal Path Sets Method

Provides minimum rotation between two DC (double couple) seismic source mechanisms minimum rotation ROTANGLE along axis given by THETA and PHI After Kagan, Y. Y. (1991). 3-D rotation of double-couple earthquake sources, Geophys. J. Int., 106(3), 709-716. almost "literary" translated from original Fortran code by P. Kolar (kolar@ig.cas.cz) 18/01/2019 cf. eg. : http://moho.ess.ucla.edu/~kagan/doc_index.html http://peterbird.name/oldFTP/2003107-esupp/

The toolbox includes follow functions sets:gTraining -- Training data and scripts for marine engineering geophysical tasks decision;gFields -- Row, RowM content's functions (example: manipulation with coordinates and time for seismic traces);gData -- Matrix content's functions (example: seismic traces filtration, gain, denoise, etc);gNav -- manipulations with Navigation and coordinates;gMap -- Geometrics tasks decision, trackplots/pipelines/lineplanning, picking, graphics;gLog -- Serial data logging (Free Pascal), read/write;gJsf -- Jsf files read/write/manipulations;gXtf -- Xtf files read/write/manipulations;gSgy -- Sgy files read/write/manipulations;gUhr -- HR/UHR Seismic logs and geometry processing;gMagy -- Magnetometers data read/write/manipulations;gP190 -- P1/90 files read/write/manipulations;gWfr -- Images with coordinate world-files and XYZ-grid-files manipulations;gAcad -- AutoCAD scripts creation.

PPHASEPICKER is a powerful tool for automatically picking P-phase onsets with high precision without requiring detection interval or threshold settings. The algorithm detects P-phase onset in single-component acceleration or broadband velocity records using the histogram method. It also computes signal-to-noise ratio (SNR). PPHASEPICKER has been integrated into the “Automated Processing and Review Interface for Strong Motion Data (PRISM)” software (https://earthquake.usgs.gov/research/software/prism/) of the U.S. Geological Survey in order to identify pre-event time-window for systematic and automated processing of large numbers of strong-motion data. An example MatLAB code is provided in zip file to show how to run PPHASEPICKER using three sample waveforms, one from strong earthquake and others from micro seismic events. For your intended application, please note that filter corner frequencies may need to be updated manually within the code based on the frequency content of the input record. Reference: Kalkan, E. (2016). “An Automatic P-phase Arrival Time Picker“, Bull. of Seis. Soc. of Am., 106(3): 971-986, doi: 10.1785/0120150111.

This function calculates seismic parameters from an acceleration time series. Specifically, it calculates velocity vs time, displacement vs time, peak ground acceleration (PGA), peak ground velocity (PGV), peak ground displacement (PGD), Arias intensity vs time, total Arias intensity (Ia), time between when 5% and 75% of Ia has occurred (significant duration D5-75), time between when 5% and 95% of Ia has occurred (significant duration D5-95), mean period (Tm), pseudo-acceleration response spectrum (Sa), pseudo-velocity response spectrum (Sv), displacement response spectrum (Sd), and the Fourier amplitude spectrum (FAS).

A method for fault network reconstruction based on the 3D spatial distribution of seismicity. This method uses a bottom-up approach that relies on initial sampling of the small scale features and reduction of this complexity by optimal local merging of substructures. The method provides the following advantages: 1) a bottom-up approach that explores all possible merger options at each step and moves coherently towards a global optimum; 2) an optimized atomization scheme to isolate the background (i.e. uncorrelated) points; 3) improved computation performance due to geometrical merging constrains. The method will be published in the following paper. Kamer Y., Ouillon G., Sornette D. (2020) "Fault Network Reconstruction using Agglomerative Clustering: Applications to South Californian Seismicity" Natural Hazards and Earth System SciencesThe submission includes the additional scripts to generate the synthetic tests featured in the paper.

Computes P-phase arrival time in windowed digital single-component acceleration or broadband velocity record without requiring threshold settings using AKAIKE INFORMATION CRITERION. Returns P-phase arrival time index.

cornerFreqs automatically detects appropriate bandpass filter cornerfrequencies by comparing the input signal's spectrum with the noise spectrum. MOTIVATION:Processing of seismic waveforms often requires bandpass filtering.Selection of filter corner frequencies has been not only a manualprocess but also subjective. There is a need for automaticallydetecting corner frequencies for processing a large number of seismicrecordings.ALGORITHM:First, "PphasePicker" function (Kalkan, 2016) is used to determineP-phase arrival time (event onset) to get the background noise. Next, Fourier amplitude spectra for noise and signal are calculated. These two spectra are smoothed using "smoothSpectra" function. Finally, intersection points of the smoothed spectra within low-pass and high-pass frequency regions are searched to determine the appropriate corner frequencies to be used for bandpass filtering.High-pass region is defined between 0.1 Hz and 1 Hz. If no intersectionpoint detected, default value of 0.1 Hz is used. Low-pass region is defined between the characteristic frequencyof the recording instrument (fc) (often 25 Hz) and Nyquist (half ofsampling frequency of waveform data). If no intersection point detected, 80% of Nyquist is used.This code uses the following external functions:[1] PphasePicker.m --> This function computes P-Phase onset time, also available at MatLAB FEX[2] smoothSpectra.m --> This function smooth FAS using Konno-Ohmachi window, also available at MatLAB FEXUSAGE:[hp_freq, lp_freq] = cornerFreqs(x,dt)STATIC INPUT: x = broadband velocity or acceleration data in single-column format (1xn) or (nx1) dt = sampling interval in second (e.g., 0.005)VALID PROP_NAME / PROP_VAL PAIRS:-----------------------------------------'plot_name' --> [text]-[default: None]'plot_path' --> [text]-[default: None]'debug' --> [text]-[default: False]OUTPUT: hp_freq = high-pass corner frequency in Hz lp_freq = low-pass corner frequency in HzEXAMPLES:see demo.m file REQUIREMENTS:cornerFreqs function does not require any MatLAB toolbox.ACKNOWLEDGEMENT:In preparing this function, I benefitted from Curve Intersections(InterX.m) function written by NS, which is available at MathWorks FEX.REFERENCE:Kalkan, E. (2016). “An Automatic P-phase Arrival Time Picker“, Bulletin of Seismological Society of America,106(3): 971-986, doi: 10.1785/0120150111 If you find this code useful for your application, please don't forget to rate it. For questions / suggestions / comments and bug reports: kalkan76@gmail.com

The GITANES software package allows an effective application of the Generalized Inverse Technique (Andrews, 1986) in the MATLAB environment for the joint evaluation of the source spectra and site spectral amplification using earthquake waveforms collected by a network of stations. The package includes a GUI, which allows the user to have a rapid visual overview of the available data, and an immediate insight in the corresponding results, when applying changes to the propagation model and to the selection of stations and events.As it is provided, the GITANES package is equally suitable for site amplification surveys intended for microzonation studies as well as for studies regarding the spectrum of the seismic source.

This script calls other functions to import and processes acceleration time series, calculate various seismic parameters and export figures and results. It runs the functions ImportGM, ProcessGM, seismicparam, and PlotGM.

This code is computation of linear response history analysis on Multi Degree of Freedom (MDOF) system, made from following reference:Liang, Z. et al. (2012). Structural Damping - Application in Seismic Response Modification. Example 3.5as part of assignment on Special Topics in Aseismatic Analysis course by Prof. Han-Seon Lee, Korea University. Step-by-step explanation is included here, please refer to subchapter 3.3.3 in the book for more detailed explanation.

For more detailed information about subject, “Seismic Crystals And Earthquake Shield Application “ article, which is found at;http://arxiv.org/ftp/arxiv/papers/0902/0902.1429.pdfUser guide to run simulation;http://www.oncubilim.net/Fizik/Fiz0902/UserGuideForSeismicWaveSimulation.pdf

The code features the implementation of the sampling methods and synthetic tests used in [Kamer, Y. (2014), Comment on "Systematic survey of high-resolution b value imaging along Californian faults: Inference on asperities", JGR. Solid Earth, 119, doi:10.1002/2014JB011147.]The submission also includes Parkield seismicty as a sample data set.ATTENTION: If you plan to use this code on real earthquake data, please consider the conclusions from the Parkfield synthetics: "...the emergence of high and low b value anomalies is a mere artifact of under sampling. These artifacts lead to differences of up to 2 orders of magnitude in the recurrence times; thus, it would be precarious to use such maps for assessment of probabilistic seismic hazard on faults. Since one cannot know in advance what b value the real data features and thus choose parameters accordingly, we maintain that the approach presented by Tormann et al. [2014] cannot be used on real data sets as its results depend on the assumed input b values chosen to derive its parameters. [...] We encourage the reader to download the codes (see Acknowledgements), try different parameter sets and explore the large variety of b value maps that one can obtain from a single data set in the absence of an objective criteria. "

Please citeNuha, Hilal H., Adil Balghonaim, Bo Liu, Mohamed Mohandes, Mohamed Deriche, and Faramarz Fekri. "Deep Neural Networks with Extreme Learning Machine for Seismic Data Compression." Arabian Journal for Science and Engineering 45, no. 3 (2020): 1367-1377.Kasun, Liyanaarachchi Lekamalage Chamara, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong. "Representational learning with extreme learning machine for big data." IEEE intelligent systems 28, no. 6 (2013): 31-34.

ft_spect (version 2.0) calculates Amplitude and Phase spectra of an input signal with the desired frequency resolution and also filters the Phase spectrum for suppressing the floating rounding-off error.NOTICE#1: ft_spect can NOT remove the spectral leakage.NOTICE#2: Discrete Fourier transform (DFT) looks at the input signal as one period of a periodic signal and discretizes the frequency spectrum of this periodic signal based on the length of the input signal. For a signal with sampling frequency Fs, over the time of T=NΔt, the frequency bins (a.k.a frequency resolution in the meaning of distinguishing frequency of f1 from f2) are spaced Δf=1/T=Fs/N; thus, the frequency resolution of DFT only depends on the length of the input signal (T). BUT, zero-padding does NOT increase the frequency resolution and does NOT reveal more information about the spectrum; it only interpolates amplitudes between bins. For increasing the spectral resolution, a long duration of measurements is necessary, because DFT looks at the input signal as one period of a periodic signal; therefore, repeating the input signal is acceptable and doesn't produce any artifacts. BUT, in this case, the length of the input signal is increased, and consequently, the spectral resolution also increases.NOTICE#3: Phase spectrum because of floating rounding-off error is very noisy. Small rounding-off error in the "arctan" calculation produces significant noise in the result of the phase spectrum. For suppressing this kind of noise, ft_spect uses a threshold filtering. It means if the amplitude of specific frequency is less than the predefined threshold value, it put zero instead of it.IF YOU USE THIS PROGRAM IN YOUR RESEARCH, PLEASE CITE THE FOLLOWING PAPER:Afshin Aghayan, Priyank Jaiswal, and Hamid Reza Siahkoohi (2016)."Seismic denoising using the redundant lifting scheme." GEOPHYSICS, 81(3), V249-V260.https://doi.org/10.1190/geo2015-0601.1** Please share your suggestions and idea for improving ft_spectthrough aghayan@okstate.edu or afshin.aghayan@gmail.comVersion 1.0 (Spring 2017) ft_spect v1.0 is written and tested in MATLAB R2013a.Version 2.0 (Spring 2020) the following changes are applied:1) You can define your desired frequency resolution (Δf)2) It is much faster than the v1.03) Added an example for comparing the output of the usual FFT and this program (Only type ft_spect for DEMO; look at ft_spect_demo function at the end of the program for more details) Afshin Aghayanafshin.aghayan@gmail.com405-334-7184

A set of about 170 functions (plus support functions called by them) for analysis and display of exploration-seismic data and well logs. These functions read and write seismic data in standard SEG-Y format, read and write well logs in LAS-format 2.0 (also read LAS-format 3.0), and perform many of the manipulations usually performed on these data types. They use standardized structures to represent seismic data and well data and thus allow simple concatenation of function calls. The functions come with a manual in PDF format and scripts with examples. Several versions of Matlab were released during their development. Presently, I ran limited tests under them under R2015a, but I try not to use Matlab syntax introduced after R2007a. Also, SeisLab 2.01, which works with Matlab 6.1 (2001) and higher, can still be downloaded from the Matlab File Exchange (file 8827).Generally, I make an effort to avoid functions from toolboxes; however, I am aware of at least one call to a function in the Optimization Toolbox.In case you already have SeisLab installed you can find its distribution ID by typing "ddid" at the Matlab prompt. If you get the error message "Undefined function or variable 'ddid'." then you have the very first release of SeisLab. Otherwise, compare the distribution ID with that of this release (15.09.20). If it is lower then this release is newer.There are no major changes from SeisLab 3.01.

Various problems in science and engineering require a finite-difference approximation to first order derivatives on a staggered grid, for example in seismic wave modelling. Such finite-differences use points at ±[0.5,1.5,2.5,...]dx away from the evaluated point. A first order example is given below, with a stencil of just one half point to the left and right:f'(x) = [f(x+1/2dx) - f(x-1/2dx)]/dx.The coefficients for longer finite-difference stencils are typically (at an introductory level) derived from Taylor series expansion, which provides a 'spectrally' accurate derivative up to a limited wavenumber. Beyond this critical wavenumber, we cannot properly compute the derivative. To increase the wavenumber-performance of the finite-difference method we then need larger stencils, i.e., a set of even higher-order finite-difference coefficients.An alternative to using even longer finite-difference stencils, is to use 'optimized' finite-difference coefficients. These coefficients trade off small errors in the lower wavenumber range to gain an enlarged wavenumber range in which the computed derivative is 'approximately' correct. Such coefficients can be designed in various ways, and a large amount of literature exists on the topic. I have implemented two different 'optimized' coefficient algorithms, differing mostly in their behaviour at k=0 (constant functions). Holberg (1987) allows the error at k=0 to be maximum, thereby achieving the largest wavenumber range theoretically possible. Kindelan et al (1990) offer an alternative way to solve this system. Liu (2014) allows no error at k=0, thereby solving for a wavenumber range slightly smaller than that achieved by Holberg (1987). Another paper from Mittet & Arntsen (2000) falls right in between these two approaches by solving Holberg (1987)'s objective function in a least-squares sense.As an example, we use a 10-point stencil (5 on either side of the point in question) and allow derivative group-errors of 0.3%. With Taylor's coefficients, this requires 4.6 nodes per minimum wavelength; Liu (2014)'s coefficients require 3.04 nodes per minimum wavelength; Mittet & Arntsen (2000)'s coefficients require 2.99 points per wavelength; with Holberg (1987)'s (and Kindelan et al. (1990)'s) coefficients we require 2.91 nodes per minimum wavelength. We can run this example by executing:>> deriv1_staggered_coeffs(5,0.003)using the supplied files.The code provided in this submission designs these Holberg (1987), Mittet & Arntsen (2000) and Liu (2014) coefficients, using the Optimization Toolbox.These codes are my own implementations based on the aviailable literature:Holberg (1987): https://doi.org/10.1111/j.1365-2478.1987.tb00841.xKindelan, Kamel & Sguazzero (1990): https://library.seg.org/doi/pdf/10.1190/1.1442763Mittet & Arntsen (2000): http://www.ipt.ntnu.no/~barn/Myarticles/Mittet2000b.pdfLiu (2014): https://doi.org/10.1093/gji/ggu032My implementation of Holberg's algorithm is more exact within the given bounds than that suggested in his original paper (Fig. 4, right-most plots, are all supposed to go between ±0.03, but for higher orders seem not to do so...), also because I take the objective function to the power 6 rather than 4. My implementation of Kindelan's algorithm is slightly more overdetermined than that described in the paper, I found no other way to make it work well for any operator-length.

Robust MatLAB function for nonlinear-inelastic time-history analysis of SDOF oscillator subjected to multi-axial simultaneous excitation including horizontal, vertical and rotational motions. It also computes components of seismic input energy imparted to SDOF oscillator. The main features are:- Multi-axial excitation- Material nonlinearity is presented by Ozdemir's rate-independent force-displacement model- P-Delta (global level) is included- Absolute and relative energy components computationSyntax: nonlinearSDOFmultiAxialAcknowledgement:Differentiation function is adapted from Wang (1996).Wang, L. J. (1996). Processing of Near-Field Earthquake Accelerograms, California Institute of Technology (unpublished), available at http://resolver.caltech.edu/CaltechEERL:1996.EERL‑96‑04 Questions? Contact: ekalkan@usgs.gov

Fast MatLAB function for nonlinear-inelastic time-history analysis of a single-degree-of-freedom (SDOF) oscillator.The code runs for a single or a series of input excitations for parametric study. MatLAB is used for pre-processing; nonlinear SDOF system is constructed and solved using OpenSEES (http://opensees.berkeley.edu/index.php) in the background. The user can define any nonlinear material model available in OpenSEES. Material properties are defined in InSPecReg/material.tcl.Syntax: nonlinearSDOFmultiAxialNote: Input ground motions are currently set to PEER format in PEER ground motion database at http://ngawest2.berkeley.edu/Two example ground motions are provided, results are in ASCII format in ".out" files as inch/s² for acc.out, inch for disp.outReferences (PDFs are available in folder named "docs"):Kalkan, E. and Graizer V. (2007). "Multi-Component Ground Motion Response Spectra for Coupled Horizontal, Vertical, Angular Accelerations and Tilt", ISET, Journal of Earthquake Technology, March.Kalkan, E. and Graizer V. (2007). "Coupled Tilt and Translational Ground Motion Response Spectra", ASCE Journal of Structural Engineering, 133(5): 609-619, 2007.Kalkan, E. and Kunnath, S.K. (2008). "Relevance of Absolute and Relative Energy Content in Seismic Evaluation of Structures", Advances in Structural Engineering, 11(1): 17-34.Kalkan, E. and Kunnath S.K. (2007). "Effective Cyclic Energy as a Measure of Seismic Demand", Journal of Earthquake Engineering, 11(5): 725-751.Graizer, V. and Kalkan, E. (2008). "Response of Pendulums to Complex Input Ground Motion", Soil Dynamics and Earthquake Engineering, 28(8): 621-631.Questions? Contact: ekalkan@usgs.gov

Calculation of eigenquakes from an earthquake record suite, and reproduction of the initial earthquake suite from the basis eigenquakes. It is found that the difference between the initial records and their corresponding simulated ones is small. Eigenquakes can be used for the generation of artificial earthquake ground motion records for the dynamic analysis of structures during their seismic design.References:1) Alimoradi, A., & Beck, J. L. (2014). Machine-learning methods for earthquake ground motion analysis and simulation. Journal of Engineering Mechanics, 141(4), 04014147.2) Alimoradi, A. (2011). Earthquake ground motion simulation using novel machine learning tools.

Copyright: 2018 - Teknik Geofisika, Universitas Pertamina URL: https://sites.google.com/site/metkomup/programming Update: https://github.com/Metkom/OSGPUP/blob/master/Forward%20modeling/seismic_wedge_model.mCite: Anugerah, Nisfu; Ginting, Gamaliel Rhema; Wicaksono, Gigih Aji; Salsabila, Alda; Subakti, Puguh Ari; Syahputra, Loris Alif (2018): Membuat Model Sintetik untuk Model Pembajian. figshare. https://doi.org/10.6084/m9.figshare.5946691.v1