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372 results for “Waveforms”
Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"
<p>This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023). MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia. The MESWA model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>). </p> <p> </p> <p>Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format. Lastly, we include a list of all receivers used in the creation and validation of MESWA. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p> </p> <p>The following table provides a listing of the files in the dataset:</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>MESWA.nc</p> </td> <td> <p>MESWA model in NetCDF format</p> </td> </tr> <tr> <td> <p>MESWA.h5</p> </td> <td> <p>MESWA model in HDF5 format, used by Salvus</p> </td> </tr> <tr> <td> <p>MESWA.xmdf</p> </td> <td> <p>Auxiliary file for MESWA.h5, used to import model into Paraview</p> </td> </tr> <tr> <td> <p>events_project.csv</p> </td> <td> <p>Table of event source parameters for all 327 events considered in the project</p> </td> </tr> <tr> <td> <p>inversion_events_192.csv</p> </td> <td> <p>Table of 192 inversion events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>validation_events_66.csv</p> </td> <td> <p>Table of 66 validation events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_inversion.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_validation.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value)</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophys. J. Int.</em>, 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p> </p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. <em>The Visualization Handbook</em>, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a></p> <p> </p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. <em>Journal of Open Research Software</em>, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a></p> <p> </p> <p>Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402</p> <p> </p>
QUMPHY MIMIC IV Waveform Database PPG formatted
<p>Derivative of the <a href="https://physionet.org/content/mimic4wdb/0.1.0/">MIMIC IV Waveform Database</a> formatted to be suitable for machine learning.</p> <p><strong>Formatting</strong></p> <p>All records are split into intervals of roughly 60 seconds. The parameter values are averaged over each 60 second interval. The PPG signal data are unprocessed, i.e. as in the original dataset. Intervals with PPG signals containing missing data or large constant data are excluded. PPG signals and signal times are truncated to have the same amount of data points for all records.</p> <p>Formatted data are split into 3 different file types, namely <code>*_n.csv</code> containing the averaged parameter values, <code>*_s.npy</code> containing PPG signal data and <code>*_t.npy</code> containing the respective signal measurement times. Moreover, formatted data are split into <code>trainXX_*</code>, <code>validation_*</code> and <code>test_*</code> data files, where the training data <code>trainXX_*</code> are split into multiple files for easier handling.</p> <p>This dataset was created using the following code: <a href="https://gitlab.com/qumphy/wp1-benchmark-data-conversion">https://gitlab.com/qumphy/wp1-benchmark-data-conversion</a></p> <p><strong>Funding</strong></p> <p>The creation of this dataset has been supported by the European Partnership on Metrology programme 22HLT01 QUMPHY. This project (22HTL01 QUMPHY) has received funding from the EMPIR programme cofinanced by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
CNNpredIM - Dataset for Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network
<p>The <strong>dataset</strong> available here is the dataset used in the <a href="https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggaa233/5836721"><strong>paper</strong> <em>"Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network".</em></a></p> <p>The <strong>abstract</strong> of the <strong>paper</strong>:</p> <blockquote> <p>This study describes a deep convolutional neural network (CNN) based technique for the prediction of intensity measurements (IMs) of ground shaking. The input data to the CNN model consists of multistation 3C broadband and accelerometric waveforms recorded during the 2016 Central Italy earthquake sequence for M ≥ 3.0. We find that the CNN is capable of predicting accurately the IMs at stations far from the epicenter and that have not yet recorded the maximum ground shaking when using a 10 s window starting at the earthquake origin time. The CNN IM predictions do not require previous knowledge of the earthquake source (location and magnitude). Comparison between the CNN model predictions and the predictions obtained with Bindi et al. (2011) GMPE (which require location and magnitude) has shown that the CNN model features similar error variance but smaller bias. Although the technique is not strictly designed for earthquake early warning, we found that it can provide useful estimates of ground motions within 15-20 sec after earthquake origin time depending on various setup elements (e.g., times for data transmission, computation, latencies). The technique has been tested on raw data without any initial data pre-selection in order to closely replicate real-time data streaming. When noise examples were included with the earthquake data, the CNN was found to be stable predicting accurately the ground shaking intensity corresponding to the noise amplitude.</p> </blockquote>
Figure 5. A Waveform, B in New microhylid Frogs from the Muller Range, Papua New Guinea
Figure 5. A Waveform, B power spectrum, and C spectrogram of the complete call of call "H" (Table 4) of Cophixalus caverniphilus sp. n. (BPBM 33748) recorded from a cave at Mt. Paramo, Muller Range, 1720 m, Southern Highlands Province, Papua New Guinea at 0830 h, 5 April 2009. Air (cave) temperature 17.6 °C.
Figure 2. A Waveform, B in New microhylid Frogs from the Muller Range, Papua New Guinea
Figure 2. A Waveform, B power spectrum, and C spectrogram of call "T" of Albericus murritus sp. n. (BPBM 33641) recorded on E slope Mt. Itukua, Muller Range, Southern Highlands Province, Papua New Guinea on 27 March 2009 at 2020 h. Air temperature 14.7 °C.
Waveforms, relocated earthquake and matched filter catalog of seismic swarm preceding the 2017 Mount Agung eruption
<p>Datasets for the manuscript:</p> <p>Sianipar, D., Ulfiana, E., and Sipayung, R. (2020), Seismic swarm preceding the 2017 Mount Agung eruption in Bali (Indonesia) enhanced by the matched filter approach (submitted) (preprint is available at EarthArxiv: <a href="https://eartharxiv.org/a7yx2/">https://eartharxiv.org/a7yx2/</a>)</p> <p>by Dimas Sianipar, Emi Ulfiana, and Renhard Sipayung (STMKG, BMKG, Indonesia).</p> <p>Files including:</p> <p>1) List of continuous waveforms</p> <p>2) HypoDD files: dt.cc, dt.ct, event.dat, hypoDD.reloc, phase.dat</p> <p>3) Processed (filtered) 407 template waveforms</p> <p>4) BMKG catalog</p> <p>5) MFT catalog in ZMAP format</p> <p>6) Table S1: template candidates</p> <p>7) Table S2: MFT catalog</p> <p>The compressed file (*.rar) has been successfully extracted in Ms. Windows OS using WinRAR.</p>
Waveform Data -NbConst_Syn
<p>Synthetic waveform with EMI constant in magnitude and frequency.</p>
Waveform Data -NbVar_Syn
<p>Synthetic waveform with EMI constant in frequency, varying in magnitude.</p>
Chilean Subduction Zone rupture scenarios and waveform data
<p>Chilean Subduction Zone kinematic rupture scenarios and waveform data for the submitted work <strong>Early warning for great earthquakes from characterization of crustal deformation patterns with deep learning (2020)</strong> by Lin et al.</p>
Solar Eclipse QSO Party Test Waveform
<p>Test waveform and code for the HamSCI Solar Eclipse QSO Party. </p> <p> </p> <p>Version 2: The following changes were made:</p> <ol> <li><strong>Removed downchirps.</strong></li> <li><strong>Eliminate both instances of the fastest 100 Hz/ms chirps. </strong>In V1, these comprise the first group of 5 chirps and the last string of concatenated up/down chirps. There is now ample evidence of problems with the fast chirp rate in radios with both analog and digital audio processing. The fast chirp evidently gets distorted by the multi-pole audio filters used to shape the radio’s passband. In both WA5FRF's original hand-processed data and the automated processing done by students at the University of Scranton, the 100 Hz/ms chirp rate gave very different results than the slower 50, 25, and 10 Hz/ms rates. Not only did the 100 Hz/ms traces not track with the others but they distorted the composite average when included.</li> <li><strong>Add another group of 1 cycle audio pulses with a reduced center frequency of 1000 Hz. </strong>There is evidence the present 1500 Hz center frequency causes a ringing problem similar to the distortion noted with the fastest chirp rate.</li> <li><strong>Preface and conclude the science payload with a ~½ second burst of a 1000 Hz sine wave tone. </strong>This will help with timing for automated data extraction and also serves to confirm the sample rate was correctly interpreted.<br><br></li> </ol> <p>The full signal is composed here: https://github.com/KCollins/seqp </p> <p>V2 of the final signal therefore consists of: </p> <ol> <li>TEST TEST TEST DE <callsign> <grid-square> in Morse code</li> <li>A ½ second 1000 Hz tone burst</li> <li>A 2-second long, non-repeating pseudorandom noise burst</li> <li>5 repetitions of a 1 cycle audio burst at 1000 Hz center frequency</li> <li>5 repetitions of a 1 cycle audio burst at 1500 Hz center frequency</li> <li>5 repetitions of a 50 Hz/ms chirp. (0-5000 Hz in 100 ms)</li> <li>5 repetitions of a 25 Hz/ms chirp. (0-5000 Hz in 200 ms)</li> <li>5 repetitions of a 10 Hz/ms chirp. (0-5000 Hz in 500 ms)</li> <li>Another 2 second PN burst identical to 3.</li> <li>Another ½ second 1000 Hz tone burst.</li> </ol>
Binary black-hole surrogate waveform catalog
<p>This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the <a href="http://www.black-holes.org/SpEC.html">Spectral Einstein Code</a>. The base method for building surrogate models can be found in <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.031006">Field et al., PRX 4, 031006 (2014)</a>.</p> <p>Several numerical relativity surrogate models are currently available in this catalog:</p> <ul> <li>Current models <ol> <li> <p>NRHybSur3dq8_CCE.h5 — This is a surrogate model for binary black hole systems built using CCE waveforms, capturing memory effects, with generic mass ratios but restricted to nonprecessing spins. Before constructing the surrogate, the NR waveforms are hybridized with post-Newtonian waveforms to include the early inspiral. Therefore this model covers full stellar mass range for for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.064027"> Yoo et al., Phys. Rev. D 108, 064027 (2023)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate"> PyPI</a>.</p> </li> <li> <p>NRHybSur2dq15.h5 — This is a surrogate model for binary black hole systems with a high mass ratio (up to 15), but restricted to nonprecessing spins and no secondary spin. Before constructing the surrogate, the NR waveforms are hybridized with SEOBNRv4HM to include the early inspiral. Therefore this model covers 9.5 solar mass or higher total mass system for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.106.044001">Yoo et al., Phys. Rev. D 106, 044001 (2022)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate"> PyPI</a>.</p> </li> <li> <p>NRSur7dq4.h5 — This is a surrogate model for binary black hole mergers with generic spins and mass ratios up to 4. A paper describing it can be found at <a href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.1.033015">Varma et al., Phys. Rev. Research 1, 033015 (2019)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate">PyPI </a>. Instructions for evaluating this surrogate can be found at <a href="https://data.black-holes.org/surrogates/NRSur7dq4.html">this example IPython code </a>.</p> </li> <li> <p>NRHybSur3dq8.h5 — This is a surrogate model for binary black hole systems with generic mass ratios but restricted to nonprecessing spins. Before constructing the surrogate, the NR waveforms are hybridized with post-Newtonian waveforms to include the early inspiral. Therefore this model covers the full stellar mass range for ground-based detectors. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.99.064045">Varma et al., PRD 99, 064045 (2019)</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI </a>. Instructions for evaluating this surrogate can be found this <a href="https://data.black-holes.org/surrogates/NRHybSur3dq8.html">example IPython code </a>.</p> </li> <li> <p>NRSur7dq4Remnant — This is a surrogate model for mass, spin, and recoil kick velocity of the remnant BH left behind in generically precessing binary black hole mergers, with mass ratios up to 4. A paper describing it can be found at <a href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.1.033015">Varma et al., Phys. Rev. Research 1, 033015 (2019)</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</p> </li> <li> <p>NRSur7dq4EmriRemnant — This is a surrogate model for mass and spin of the remnant BH left behind in generically precessing binary black hole mergers, extending to arbitrary mass ratios. A paper describing it can be found at <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.084015">Boschini et al., Phys. Rev. D 108, 084015 (2023)</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</p> </li> <li>NRSur3dq8_RD — This is a surrogate model for mass, spin, and complex quasinormal mode amplitudes of the remnant BH left behind from mergers with mass ratios up to 8 but restricted to nonprecessing spins. A paper describing it can be found at <a href="https://arxiv.org/abs/2408.05300">Magaña Zertuche et al., arxiv:2408.05300</a>. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</li> <li>SEOBNRv4PHMSur — This is a surrogate model for binary black hole systems described by the precessing effective one body (EOB) waveform model SEOBNRv4PHM. The model is valid for mass ratio <= 20. A paper describing it can be found at <a href="https://arxiv.org/abs/2203.00381" target="_blank" rel="noopener noreferrer">Gadre et al., arXiv:2203.00381</a>. It is evaluated with the gwsurrogate Python package, which can be found on <a href="https://pypi.org/project/gwsurrogate/" target="_blank" rel="noopener noreferrer">PyPI</a>.</li> <li>NRSur3dq8BMSRemnant — This is a surrogate model for the initial-to-final BMS transformation from mergers with mass ratios up to 8 but restricted to nonprecessing spins. A paper describing it can be found at Da Re et al., arxiv:2503.09569. It is evaluated with the surfinBH Python package, which can be found on <a href="https://pypi.org/project/surfinBH/">PyPI</a>. Installation instructions and an ipython help notebook can be found in the same link.</li> </ol> </li> <li>Older models <ol> <li> <p>SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5 — A surrogate model for binary black hole mergers with non-spinning black holes. This is describedin <a href="http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.115.121102">Blackman et al., PRL115, 121102 (2015)</a>. It is evaluated with the gwsurrogate python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI </a>. Instructions for evaluating this surrogate can be found in tutorials included with the gwsurrogate package and in this <a href="https://data.black-holes.org/surrogates/GWSurrogate_example.html">example IPython code </a>.</p> </li> <li> <p>NRSur4d2s_FDROM_grid12.h5 and NRSur4d2s_TDROM_grid12.h5 — These are fast frequency-domain and time-domain (respectively) surrogate models for binary black hole mergers where the black holes may be spinning, but the spins are restricted to a parameter subspace which includes some but not all precessing configurations. NRSur4d2s_FDROM_grid12.h5 is the NRSur4d2s_FDROM model described in <a href="https://dx.doi.org/10.1103/PhysRevD.95.104023">Blackman et al., PRD 95, 104023, (2017)</a>, and NRSur4d2s_TDROM_grid12.h5 is built from the underlying (slower) NRSur4d2s time-domain model in the same way but without the FFTs. These surrogates are also evaluated using gwsurrogate, and a tutorial can be found in this <a href="https://data.black-holes.org/surrogates/NRSur4d2s_tutorial.html">example IPython code </a>.</p> </li> <li> <p>NRSur7dq2.h5 — This is a surrogate model for binary black hole mergers with generic spins. A paper describing it can be foundat <a href="https://dx.doi.org/10.1103/PhysRevD.96.024058">Blackman et al., PRD 96, 024058 (2017)</a>. This surrogate is evaluated through a standalone python package contained in NRSur7dq2.tar.gz, which has simple installation instructions in its README file. A tutorial can be found for evaluating this surrogate in this <a href="https://data.black-holes.org/surrogates/NRSur7dq2_tutorial.html">example IPython code </a>.</p> </li> </ol> </li> </ul> <p> </p> <p> </p> <p>If you find these surrogate models useful in your own research please cite the Field et al., PRX (2014) paper as well as the relevant paper describing the specific numerical relativity surrogate model, if available (e.g., the Blackman et al. 2015 paper for non-spinning binary black hole coalescences).</p> <p>Caveats:</p> <ol> <li> <p>Evaluating surrogate models outside of the ranges they were trained upon may give inaccurate results. Please use with caution when extrapolating.</p> </li> <li> <p>The surrogate data available here for non-spinning binary black holes produced in Blackman et al. 2015 contains the (2,0) mode. However, this mode was not used in the paper. While this surrogate can predict a (2,0) mode, current numerical relativity simulations may not yet be able to accumulate (non-oscillatory) Christodoulou memory sufficiently. The surrogate (2,0) mode is founded upon basis SpEC waveforms that have been hybridized with leading order post-Newtonian waveforms. Therefore, the (2,0) mode can be included in the mode’s output but should be used with caution. Currently, the default option to evaluate this surrogate (using GWSurrogate) is to exclude all m=0 modes.</p> </li> </ol>
Towards a traceable divider for composite voltage waveforms below 1 kV
<p>In the framework of the European Project 19NRM07 HV-com<sup>2</sup> supporting the standardization in high-voltage testing with<br> composite and combined wave shapes, a divider to employ in a test set-up for validation of electrical devices submitted to<br> composite voltages below 1 kV has been developed at the Istituto Nazionale di Ricerca Metrologica (INRIM) and currently<br> is under extensive testing. The data relating to the paper are reported here.</p>
Rupture Process of the 2017 Mw 6.3 Earthquake in Jinghe, Northwest China Constrained by GNSS, InSAR and teleseismic waveforms
<p>This dataset include:</p> <p>1. Slip model of 2017 Mw 6.3 Jinghe earthquake invert with GNSS, InSAR and teleseismic waveforms.</p> <p>2. InSAR LOS offsets caused by the mainshock (The file named by sar.static )</p> <p>3. Aftershocks locations relocated with hypoDD</p> <p> </p>
Dataset for "WUS256: An Adjoint Waveform Tomography Model of the Crust and Upper Mantle of the Western United States for Improved Waveform Simulations"
<p>This dataset contains the WUS256 seismic model and auxiliary data used in the creation of the model (Rodgers et al., 2022). WUS256 is a three-dimensional model of the seismic properties of crust and upper mantle of the western United States. The WUS256 model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).</p> <p>Also included are the earthquake source parameters for the 72 inversion events and 18 validation events in ASCII text format. Lastly, we include a list of all waveforms used in the creation of WUS256. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p>This effort was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-833624</p>
Spatiotemporal attosecond control of electron pulses via subluminal terahertz waveforms
<p>The dataset contains the electron deflectograms and evanescent wave profiles. </p>
Waveforms and results of seismic attenuation in Sumatra subduction zone, Indonesia
<p>Datasets for the manuscript:</p> <p>Styawan, Y., Kuo, C.-H., Huang, B.-S., Wen, K.-L., Haridhi, H. A., Sianipar, D., Characteristics of seismic attenuation in Sumatra subduction zone, Indonesia (submitted)</p> <p>The attached files include:</p> <p>1) 0.2 Hz Highpass filtered waveforms for Z and T components.</p> <p>2) Results (α, event, station, t*, Q, corner frequency, Ω0, SNR, component (Z or T), category (forearc, mountain, or backarc), and Qp/Qs).</p> <p>3) Additional data (information on events and stations).</p> <p>4) site amplification factors (P and S of all stations in different α).</p>
A waveform dataset in continuous mode of the Montefeltro seismic network (MF) in central-northern Italy from 2018 to 2020
<p>The Montefeltro seismic network (FDSN Network code: 1S) was deployed in the Apennines area of<br> northern Marche and southern Emilia-Romagna regions (central Italy). The network has been set up<br> starting from December 2018, and the array consists of stations equipped with dynamic digitizers<br> and three component short/extended/broad band seismometers (Guralp 3D/40s, Lennartz 3D/5s,<br> SS20 3D/0.5s sensors). The temporary network records in continuous mode at 100 sps. The data are<br> used to analyse the seismicity and the spatio-temporal evolution of small seismic sequences,<br> occurring in the considered area and surrounding zones, strongly clustered in time and space.<br> Stations (registered in ISC) in this Network:<br> Station code Location Station name Data acquisition<br> Lat(N) Lon(E) Ele(m) Start End<br> MF01 43.82150 12.57190 368 Auditore (PU) 2018-11-29 2020-12-31<br> MF02 43.86540 12.21070 626 Sant'Agata Feltria (RN) 2019-04-19 2020-05-30<br> MF03 43.84860 12.47990 541 Monte Grimano (PU) 2019-10-07 2020-12-31<br> MF04 43.81030 12.05620 1043 Verghereto (FC) 2020-10-09 2020-12-31<br> The data of dataset files are miniseed formatted and subdivided by the following tree:<br> (1) the dataset is divided by years;<br> (2) the divided by years dataset is subdivided by stations;<br> (3) finally, the data are divided by days of each year in every station folder.</p> <p><br> Response information:<br> Station code Sensor Recorder Recorder period<br> Start End<br> MF01 Lennartz 3D/5s Reftek 130 2018-11-29 2019-10-07<br> MF01S Lennartz 3D/5s Sara SL06 2019-10-07 2020-12-31<br> MF02 Guralp CMG/20s Reftek 130 2019-04-19 2020-05-30<br> MF03 Guralp CMG/30s Reftek 130 2019-10-07 2020-12-31<br> MF04 Sara SS02/0.5s Sara SL06 2020-10-09 2020-12-31</p> <p>In this dataset the data recorded by MF01 station are been acquisited by different recorders,<br> the firth record period by Reftek 130 (MF01) and the second record period by Sara SL06 (MF01S).</p> <p>List for the responses of the Seismic Instruments<br> Guralp CMG/20s sensor response: RESP_XX_NS444_BHZ_CMG40T_20_50_800.txt<br> Guralp CMG/30s sensor response: RESP_XX_NS041_BHZ_CMG40T_30_800.txt<br> Lennartz 3D/5s sensor response: RESP_XX_NS484_SHZ_LE-3D5sMkIII_5_800.txt<br> Sara SS02/5s sensor response: RESP_XX_NS505_SHZ_SS02_5.txt<br> Reftek 130 datalogger response: RESP_XX_NR008_HHZ_130_1.txt<br> Sara SL06 datalogger response: RESP_XX_NS000_HHZ_SL06_88_L22x3_100_4.txt</p>
Dataset for paper "Mitigating the effect of errors in source parameters on seismic (waveform) inversion"
<p>Dataset corresponding to the journal article "Mitigating the effect of errors in source parameters on seismic (waveform) inversion" by Blom, Hardalupas and Rawlinson, accepted for publication in Geophysical Journal International. In this paper, we demonstrate the effect or errors in source parameters on seismic tomography, with a particular focus on (full) waveform tomography. We study effect both on forward modelling (i.e. comparing waveforms and measurements resulting from a perturbed vs. unperturbed source) and on seismic inversion (i.e. using a source which contains an (erroneous) perturbation to invert for Earth structure. These data were obtained using Salvus, a state-of-the-art (though proprietary) 3-D solver that can be used for wave propagation simulations (Afanasiev et al., GJI 2018).</p> <p>This dataset contains:</p> <ul> <li>The entire Salvus project. This project was prepared using Salvus version 0.11.x and 0.12.2 and should be fully compatible with the latter.</li> <li>A number of Jupyter notebooks used to create all the figures, set up the project and do the data processing.</li> <li>A number of Python scripts that are used in above notebooks.</li> <li>two conda environment .yml files: one with the complete environment as used to produce this dataset, and one with the environment as supplied by Mondaic (the Salvus developers), on top of which I installed basemap and cartopy.</li> <li>An overview of the inversion configurations used for each inversion experiment and the name of hte corresponding figures: inversion_runs_overview.ods / .csv .</li> <li>Datasets corresponding to the different figures. <ul> <li>One dataset for Figure 1, showing the effect of a source perturbation in a real-world setting, as previously used by Blom et al., Solid Earth 2020</li> <li>One dataset for Figure 2, showing how different methodologies and assumptions can lead to significantly different source parameters, notably including systematic shifts. This dataset was kindly supplied by Tim Craig (Craig, 2019).</li> <li>A number of datasets (stored as pickled Pandas dataframes) derived from the Salvus project. We have computed: <ul> <li>travel-time arrival predictions from every source to all stations (df_stations...pkl)</li> <li>misfits for different metrics for both P-wave centered and S-wave centered windows for all components on all stations, comparing every time waveforms from a reference source against waveforms from a perturbed source (df_misfits_cc.28s.pkl)</li> <li>addition of synthetic waveforms for different (perturbed) moment tenors. All waveforms are stored in HDF5 (.h5) files of the ASDF (adaptable seismic data format) type</li> </ul> </li> </ul> </li> </ul> <p>How to use this dataset:</p> <ul> <li>To set up the conda environment: <ol> <li>make sure you have anaconda/miniconda</li> <li>make sure you have access to Salvus functionality. This is not absolutely necessary, but most of the functionality within this dataset relies on salvus. You can do the analyses and create the figures without, but you'll have to hack around in the scripts to build workarounds.</li> <li>Set up Salvus / create a conda environment. This is best done following the instructions on the Mondaic website. Check the changelog for breaking changes, in that case download an older salvus version.</li> <li>Additionally in your conda env, install basemap and cartopy: <pre><code class="language-bash">conda-env create -n salvus_0_12 -f environment.yml conda install -c conda-forge basemap conda install -c conda-forge cartopy</code></pre> </li> <li> <p>Install LASIF (https://github.com/dirkphilip/LASIF_2.0) and test. The project uses some lasif functionality.</p> </li> <li> <p> </p> </li> <li> <p> </p> </li> </ol> </li> <li>To recreate the figures: This is extremely straightforward. Every figure has a corresponding Jupyter Notebook. Suffices to run the notebook in its entirety. <ul> <li>Figure 1: separate notebook, Fig1_event_98.py</li> <li>Figure 2: separate notebook, Fig2_TimCraig_Andes_analysis.py</li> <li>Figures 3-7: Figures_perturbation_study.py</li> <li>Figures 8-10: Figures_toy_inversions.py</li> </ul> </li> <li>To recreate the dataframes in DATA: This can be done using the example notebook Create_perturbed_thrust_data_by_MT_addition.py and Misfits_moment_tensor_components.M66_M12.py . The same can easily be extended to the position shift and other perturbations you might want to investigate.</li> <li>To recreate the complete Salvus project: This can be done using: <ul> <li>the notebook Prepare_project_Phil_28s_absb_M66.py (setting up project and running simulations)</li> <li>the notebooks Moment_tensor_perturbations.py and Moment_tensor_perturbation_for_NS_thrust.py</li> <li>For the inversions: using the notebook Inversion_SS_dip.M66.28s.py as an example. See the overview table inversion_runs_overview.ods (or .csv) as to naming conventions.</li> </ul> </li> </ul> <p> </p> <p>References:</p> <ul> <li>Michael Afanasiev, Christian Boehm, Martin van Driel, Lion Krischer, Max Rietmann, Dave A May, Matthew G Knepley, Andreas Fichtner, Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophysical Journal International</em>, Volume 216, Issue 3, March 2019, Pages 1675–1692, <a href="https://doi.org/10.1093/gji/ggy469">https://doi.org/10.1093/gji/ggy469</a></li> <li>Nienke Blom, Alexey Gokhberg, and Andreas Fichtner, Seismic waveform tomography of the central and eastern Mediterranean upper mantle, <em>Solid Earth</em>, Volume 11, Issue 2, 2020, Pages 669–690, 2020, <a href="https://doi.org/10.5194/se-11-669-2020">https://doi.org/10.5194/se-11-669-2020</a></li> <li>Tim J. Craig, Accurate depth determination for moderate-magnitude earthquakes using global teleseismic data. <em>Journal of Geophysical Research: Solid Earth</em>, 124, 2019, Pages 1759– 1780. <a href="https://doi.org/10.1029/2018JB016902">https://doi.org/10.1029/2018JB016902</a></li> </ul> <p> </p>
Final data of the adjoint-state full waveform tsunami source inversion, applied to Chile-Iquique tsunami event
<p>We develop an adjoint-state full waveform inversion procedure to recover the initial water elevation of a tsunami event. Traditional finite-fault tsunami source inversion methods suffer from the uncertainty of fault parameters or crustal rigidity. Moreover, the heavy computational burden of calculating Green’s functions results in limited spatial resolution and hinders the real-time applicability of the traditional methods to tsunami early warning. In this work, we apply the adjoint-state full waveform inversion method to the tsunami source inversion. The benefits of the adjoint inversion are two folds: 1) independence of fault parameters, and 2) high computational efficiency, especially for dense tsunami arrays and high resolution grids. We valid this approach with synthetic tsunami sources, and apply it to the 2014 Chile-Iquique tsunami event. Both synthetic and real-data preliminary results show that the adjoint-state method is of high efficiency and high resolution, outperforming the traditional tsunami source inversions. </p> <p>The data is in three comma-separated ascii files. We shared the three inversion results with different starting models. The source region is 70.3~71.5W, 18.5~21S on uniform grids. The src_TRIstart.txt is the inversion result with TRI image starting model, src_USGS_unistart.txt is the inversion result with USGS uniform slip model (https://earthquake.usgs.gov/earthquakes/eventpage/usc000nzvd/finite-fault). The src_zerostart.txt is the inversion result with zero starting model. The text file has longitude (in degrees), latitude (in degrees) and water elevation (in meters) of each column.</p>
Synthetic tetrode recording dataset with spike-waveform drift
<p><strong>Introduction</strong></p> <p>This synthetic ground-truth dataset accurately models long-term, continuous extracellular tetrode recordings from the rodent brain over a time-period of 256 hours. Each "recording" comprises spiking of 8 distinct single-units with firing rates ranging from 0.1 - 6 Hz, superimposed on background multi-unit spiking activity at 20 Hz. The recording sampling rate is 30 kHz. Single-unit spike amplitudes drift over a range of 100 to 400 <span class="math-tex">\(\mu V\)</span> based on the drift we observe in our own long-term recordings from the rodent motor cortex and striatum. For more details, please see our paper "Automated long-term recording and analysis of neural activity in behaving animals" ( https://doi.org/10.1101/033266).</p> <p>These recordings can be used to test the accuracy of spike-sorting algorithms when clustering non-stationary spike waveform data, such as our own Fast Automated Spike Tracker (FAST) outlined in our paper and available at https://github.com/Olveczky-Lab/FAST.</p> <p> </p> <p><strong>Dataset</strong></p> <p>Due to size restrictions, we provide here 1 sample tetrode of the full 6 tetrode dataset. Please contact us (https://olveczkylab.oeb.harvard.edu/about) if you require access to the other 5 synthetic tetrode recordings.</p> <p> </p> <p><strong>Instructions</strong></p> <p>The dataset comprises spike times and spike waveform snippets extracted a continuous synthetic tetrode recording. Provided are...</p> <ul> <li>A <strong>SpikeTimes</strong> file with a list of sample numbers for detected events (spikes) at <em>uint64</em> precision.</li> <li>A <strong>Spikes</strong> file with the waveforms of the detected events in <em>int16</em> precision. Each event waveform comprises <em>4 channels X 64 samples</em> 16-bit words arranged in the order [Ch0-Sample0, Ch1-Sample0, Ch2-Sample0, Ch3-Sample0, Ch0-Sample1, etc.]. To convert to units of voltage, change type to double precision and multiply by 1.95e-7.</li> <li>A <strong>SnippeterSettings.xml</strong> file with snippeting parameters (this is auto-generated by the FAST snippeting algorithm).</li> <li>A <strong>dataset_params.mat</strong> MATLAB data file containing the simulation parameters. The most important variables in the mat file are <em>sp</em> which contains a list of true spike-times (in samples @ 30 kHz) for all single-units in the dataset, and <em>sp_u</em> which specifies which unit (1-8) each spike originates from. Spike-times are generated by a homogenous Poisson process with firing rate specified for each unit by the variable <em>uFRs</em> and an absolute refractory period of 2 ms. The variable <em>d_Amps</em> specifies the amplitude of each single-unit spikes. The basic spike-waveform shape of each unit is provided in the variable <em>uWVs</em>. The spike-times and identity of background (multi-unit) spikes are specified in <em>b_sp</em> and <em>b_sp_u</em>. </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.