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662 results for “seismicity”
Preliminary catalogue of High Agri Valley seismicity (southern Italy) recorded by the temporary INSIEME network.
<p>The preliminary catalogue is a comma-separated values (CSV) file which lists the preliminary location and magnitude estimation of 852 local natural and induced earthquakes occurred between September 2016 and March 2019. The catalogue has been produced with the Origin Locator Viewer (scolv) tool of the software SeisComP3 (<a href="https://www.seiscomp3.org">https://www.seiscomp3.org</a>) running on the server of the INSIEME seismic network (<a href="https://doi.org/10.7914/SN/3F_2016">https://doi.org/10.7914/SN/3F_2016</a>). The information included in this catalogue should be considered preliminary both in terms of earthquake location and local magnitude (ML) estimation.</p> <p>Each row of the CSV file indicates the following source parameters:</p> <p><em>ORIGIN TIME (UTC), LATITUDE </em>˚<em>N, LONGITUDE </em>˚<em>E, DEPTH (KM), MAGNITUDE (ML)</em></p> <p>---</p> <p>This file belongs to the Supplement of the article: Stabile, T. A., Serlenga, V., Satriano, C., Romanelli, M., Gueguen, E., Gallipoli, M. R., Ripepi, E., Saurel, J.-M., Panebianco, S., Bellanova, J., and Priolo, E.: The INSIEME seismic network: a research infrastructure for studying induced seismicity in the High Agri Valley (southern Italy), Earth Syst. Sci. Data, <a href="https://doi.org/10.5194/essd-2019-113">https://doi.org/10.5194/essd-2019-113</a>, 2020.</p>
DATA SET FOR: Active faulting, submarine surface rupture and seismic migration along the Liquiñe-Ofqui fault system, Patagonian Andes
<p>Data description: These data corresponde to high-resolution bathymetry and seismic reflection profiles obtained in the inner fjord west of Puerto Aysén (between 73.13°- 72.68°W and 45.32°-45.47°S; Figs. 1 and 2). The data set was obtained during a geophysical study as part of the DETSUFA project (Deslizamientos Tsunamigénicos en el Fiordo de Aysén; Lastras et al., 2013), which took place between March 4th and 17th, 2013, aboard the R/V BIO Hésperides.<br> <br> KONGSBERG SIMRAD multibeam EM-1002S was used to obtain bathymetric data, and it works with 111 beams at a 96 kHz sonar frequency and with a maximum ping rate of >10 Hz. Equidistant mode was used for swath bathymetry acquisition. This array maximized the number of beams facilitating data acquisition and obtaining a homogenized final grid with improved resolution, with tracks separated every 150 m. The swath thickness was the same regardless of width, generating a 50% overlap between each track, with the exception of areas located near the coast. Expendable Bathythermograph (XBT) probes were used at specific sites to measure changes in water sound velocity due to eventual changes in fresh water circulation, tides, and sediment.<br> <br> Seismic reflection data were acquired using an array of two BOLT air guns (165 and 175 inches3), which were towed behind the vessel stern. The configuration used in the seismic sources was 2,000 psi, a depth of 3 m for the gun, with a firing rate of 15 m over the seafloor. A 100 m long mini-streamer with a 25 m active section, corresponding to one single channel, recovered the shots. The seismic data were recorded by using the DELPH SEISMICPLUS system with a recording length of 4.0 s and a preamplifier gain of 8 Hz. The raw seismic data were processed aboard the SMT Kingdom Suite, including the navigation and standard processes of electrical noise removing (50 Hz filter), gain amplifier and bandpass filtering, to improve data visualization. Postprocessing included the migration of the sea bottom diffractions and the muting of the water column performed in Seismic-Unix.</p> <p>Files:</p> <p>Raw Seismic reflection data for lines 05, 06 and 07 (SU & SEG files)</p> <p>Masked Seismic profiles for lines 05, 06 and 07 (SU, PDF & PS files)</p> <p>Bathymetry of inner and outer Aysén Fjord (ASCII file)</p>
Model data repository of "How sediment thickness influences subduction dynamics and seismicity"
<p>This repository provides the code and data to run the Seismo-Thermo-Mechanical model with a sediment thickness T<sub>sed</sub> of 4 km on a cluster using executables.</p>
Local seismic tomography of the eastern Anatolia region
<p>The tomography model presented in the paper "Local seismic tomography of the eastern Anatolia region" are obtained using the LOTOS code by Koulakov (2009). Here, we present the full version of the code with initial data and parameters used for calculating P and S velocity models beneath the eastern Anatolia. This version of the code is adopted for the Windows OS and contains the entire program listing and the full project structure for Microsoft Visual Studio 2010 and Intel Visual Fortran. Detailed description of the code can be found at <a href="http://www.ivan-art.com/science/LOTOS">www.ivan-art.com/science/LOTOS</a></p>
Co- and Post-seismic Crustal Deformation Associated with the 2016 Kumamoto Earthquake Sequence Revealed by PALSAR-2 Pixel Tracking and InSAR
<p>This archive includes the dataset for the co- and post-seismic displacements associated with the 2016 Kumamoto earthquake in Japan detected by ALOS-2/PALSAR-2 data. All files are formatted as the MATLAB format. ALOS-2/PALSAR-2 level 1.1 data in this study were provided from the PIXEL (PALSAR Interferometry Consortium to Study our Evolving Land Surface) under a cooperative research contract with the Earthquake Research Institute, University of Tokyo. The ownership of ALOS-2/PALSAR-2 level 1.1 data belongs to JAXA.</p> <p> </p> <p>coseismic_datav1.mat contains the original PALSAR-2 pixel tracking data and 3D displacement components. You can display 3D displacement field using "display_3Ddisp.m".</p> <p>coseismc_flocv1.mat contains the isolated displacement discontinuities in the observation data.</p> <p>postseismic_asc_v1.mat contains the PALSAR-2 original InSAR data in the ascending orbit.</p> <p>postseismic_dsc_v1.mat contains the PALSAT-2 original InSAR data in the descending orbit.</p> <p>script_acrossp_p.m generates the profiles of post-seismic displacement profile across the profile.</p> <p>script_alongp_c.m generates the profiles of co-seismic displacement along the fault.</p> <p>script_alongp_p.m generates the profiles of post-seismic displacement along the fault.</p> <p>script_quasi_def.m generates the quasi-eastwest and the quasi-vertical displacement fields.</p> <p>script_ts.m generates the time-series of line-of-sight change using the sequence of InSAR data.</p> <p>-</p> <p>display_3Ddisp.m displays 3D displacement fields using "coseismic_datav1.m".</p> <p>coseis_Fdisp.mat contains data for along-fault displacement. (Lon, Lat, vertical, stv_vertical, horizontal_alongfault, stv_horizontal_alongfault, and topography)</p> <p>display_coseis_alongdisp.m generates figures for the along-fault displacement distribution using "coseis_Fdisp.mat".</p>
Data for the paper "An Inversion Algorithm for Deriving Shallow Structure using Co-located Wind, Pressure, and Seismic Data"
<p>Wind, pressure, and seismic data used in the paper "An Inversion Algorithm for Deriving Shallow Structure using Co-located Wind, Pressure, and Seismic Data"</p>
Co-seismic and post-seismic differential Interferograms and displacement maps for the 2020 M6.5 Monte Cristo Range, Nevada earthquake
<p>Differential interferograms for the Mw 6.5 Monte Cristo, Nevada earthquake, processed with SNAP and CNR-IREA P-SBAS in the <a href="https://geohazards-tep.eu">Geohazards Exploitation Platform</a>.</p> <p>USGS event page for the Monte Cristo earthquake:</p> <p><a href="https://earthquake.usgs.gov/earthquakes/eventpage/nn00725272/">https://earthquake.usgs.gov/earthquakes/eventpage/nn00725272/</a></p> <p>Co-seismic and post-seismic (May 16 - May 23) interferograms are included. For each interferometric pair, three products are included: coherence, phase interferogram and unwrapped interferogram (LOS displacement). Decomposition.zip files includes the processed East-West and Vertical deformation maps from combining ascending and descending interferograms.</p> <p> </p>
Seismic Activity of South Asian Region from Jan 2018 to Jan 2020
<p>This data contains earthquake waveforms recorded for South Asian region between January 2018 to Jan 2020. The data has been saved in mseed format.</p>
An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times
<p>Catalog of 440,697 earthquakes of the 2016-2017 Central Italy seismic sequence semi-automatically generated by Spallarossa et al. (2020). The catalogue covers one year of aftershocks following the first mainshock of the sequence (from 08242016 to 08312017).</p> <p>The catalog has been generated using the Complete Automatic Seismic Processor (CASP) procedure (Scafidi et al., 2019) to detect the events and an advanced picker engine (RSNI-Picker<sub>2</sub>; Scafidi et al., 2018; Spallarossa et al., 2014) to determine their phase arrival times. The final set of about 7 million P- and 10 million S-wave arrival times have been used to locate the events using a non-linear location algorithm (NonLinLoc; Lomax et al. 2000), with a 1D velocity model calibrated for the area (De Luca et al., 2009) and station corrections. For each event, also local magnitudes (M<sub>L</sub>) has been calculated as well as a locations quality.</p> <p>Earthquake locations quality has been classified by means of the procedure proposed by Michele et al., (2019) consisting of the combination of diverse uncertainty parameters provided by the NonLinLoc location code. Locations quality is provided in terms of a unique numeric normalized value, named quality factor, varying between qf=0 (best quality location) and qf=1 (worst quality location). Then locations have been assigned to a quality class depending on the qf parameter value according to the following scheme: A-class (0 < qf ≤ 0.25), B-class (0.25 < qf ≤ 0.50), C-class (0.50 < qf ≤ 0.75), and D-class (0.75 < qf < 1.00). The earthquake locations are distributed between the quality classes as A-30.6%, B-31.4%, C-18.6%, and D-19.4% (details in Spallarossa et al., 2020).</p> <p>We accompanied the catalogue with the 30 events with M>3.5 missed by our procedure (bring the total number of events to 440,727), including the first Amatrice mainshock (M<sub>W</sub>6.0; see Spallarossa et al., 2020). These 30 missing events recognisable by the ID starting with ISI), have been taken from INGV bulletin (<a href="http://terremoti.ingv.it">http://terremoti.ingv.it</a>; ISIDe Working Group., 2007), manually generated. These additional events report INGV locations and magnitude parameters while are missing related quality factors and quality class, being generated by a different procedure.</p> <p>We added to the larger events, the available moment magnitudes (M<sub>W</sub>) from Time Domain Moment Tensor catalogue (<a href="http://terremoti.ingv.it/tdmt">http://terremoti.ingv.it/tdmt</a>; Scognamiglio et al., 2006).</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-event – ID</li> <li>Latitude (°) expressed in decimal degrees - LAT</li> <li>Longitude (°) expressed in decimal degrees - LON</li> <li>Depth(km) hypocentral depth expressed in kilometres - DEP</li> <li>Year of origin time in the format yyyy - YR</li> <li>Month of origin time in the format mo - MON</li> <li>Day of origin time in the format dd - DY</li> <li>Hour of origin time in the format hh - HR</li> <li>Minute of origin time in the format mi - MIN</li> <li>Second of origin time in the format XX.XXX s - SEC</li> <li>Local Magnitude - ML</li> <li>Standard deviation of the Local Magnitude – STD</li> <li>Moment Magnitude – Mw (from TDMT)</li> <li>Horizontal Error (from NLL output) (km) expressed in kilometres - ERH</li> <li>Vertical Error (from NLL output) (km) expressed in kilometres - ERZ</li> <li>RMS (from NLL output) (s) expressed in seconds - RMS</li> <li>Number of Phases – NPHS</li> <li>Stations Azimuthal GAP (°) expressed in decimal degrees - GAP</li> <li>Quality factor - Qf</li> <li>Quality class - Qc</li> </ul> <p> </p> <p>De Luca G., M. Cattaneo, G. Monachesi and A, Amato (2009). Seismicity in the Umbria-Marche region from the integration of national and regional seismic networks. Tectonophysics, 476(1), 219-231. doi: 10.1016/j.tecto.2008.11.032.</p> <p>ISIDe Working Group. (2007). Italian Seismological Instrumental and Parametric Database (ISIDe). Istituto Nazionale di Geofisica e Vulcanologia (INGV); https://doi.org/10.13127/ISIDE.</p> <p>Lomax, A., J. Virieux, P. Volant, and C. Berge-Thierry (2000). Probabilistic earthquake location in 3D and layered models: introduction of a Metropolis–Gibbs method and comparison with linear locations. In: Advances in seismic event location, ed. C. H. Thurber and N. Rabinowitz, 101–134. Dordrecht and Boston: Kluwer Academic Publishers.</p> <p>Michele, M., Latorre, D., Emolo, A. (2019). An Empirical Formula to Classify the Quality of Earthquake Locations. Bulletin of the Seismological Society of America. Vol. 109, No. 6, pp. 2755–2761, December 2019, doi: 10.1785/0120190144.</p> <p>Scafidi, D., Viganò A., Ferretti G., and Spallarossa D. (2018). Robust picking and accurate location with RSNI-Picker2: real-time automatic monitoring of earthquakes and non-tectonic events, Seismol. Res. Lett, Vol. 89 (4), pp. 1478-1487, doi: 10.1785/0220170206.</p> <p>Scafidi D, Spallarossa D, Ferretti G, Barani S, Castello B, Margheriti L (2019). A complete automatic procedure to compile reliable seismic catalogs and travel-time and strong-motion parameters datasets. Seismol Res Lett 90(3):1308–1317.</p> <p>Scognamiglio, L., Tinti, E., Quintiliani, M. (2006). Time Domain Moment Tensor [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/TDMT.</p> <p>Spallarossa, D., G. Ferretti, D. Scafidi, C. Turino, and M. Pasta (2014). Performance of the RSNI-Picker, Seismol. Res. Lett. 85, 1243–1254.</p> <p>Spallarossa D., Cattaneo M., Scafidi D., Michele M., Chiaraluce L., Segou M. and I. G. Main (2020). An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times. Geophys. J. Int. doi: 10.1093/gji/ggaa604.</p>
Local seismic tomography of the Middle Tien Shan region
<p>The tomography model presented in the paper "Studying the depth structure of the Kyrgyz Tien Shan by using the seismic tomography and magnetotelluric sounding methods" are obtained using the LOTOS code by Koulakov (2009). Here, we present the full version of the code with initial data and parameters used for calculating P and S velocity models beneath the Kyrgyz Tien Shan. This version of the code is adopted for the Windows OS and contains the entire program listing and the full project structure for Microsoft Visual Studio 2010 and Intel Visual Fortran. Detailed description of the code can be found at <a href="http://www.ivan-art.com/science/LOTOS">www.ivan-art.com/science/LOTOS</a></p>
Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models
<p>This dataset contains the .npy (numpy) files of the simulation models and reference discussed in the paper "Convolutional Neural Network Formulation to Compare 4D Seismic and Reservoir Simulation Models".</p> <p>The folders contain all simulation models and reference maps already divided in subregions. Each .npy file is a numpy 2D array with delta IP or delta Sw values. The csv files contain the 3-tuples and the selected model in each.</p> <p>There are two csv files: the first is the dataset used for training the CNN, with 1280 labeled tuples evaluated by a single specialist. The second is the ground-truth, with 164 tuples evaluated by three specialists (in which 2 or more agreed on the selected model), used for validating the models and comparing different approaches.</p> <p>We also provide a Python code to read and visualize the .npy files.</p>
Ringlaser and seismic data at Fürstenfeldbruck and Wettzell for time-frequency analysis of microseisms
<p>Ringlaser rotation data and seismic data at Fürstenfeldbruck and Wettzell for the time-frequency analysis of seismic noise. Programs are also attached.</p>
Seismic datasets in "Conjugate fault deformation revealed by aftershocks of the 2013 Mw6.6 Lushan earthquake and seismic anisotropy tomography"
<p>The Lushan seismic dataset used in the manuscript entitled 'Conjugate fault deformation revealed by aftershocks of the 2013 Mw6.6 Lushan earthquake and seismic anisotropy tomography ' submitted to Geophysical Research Letters.</p> <p> </p>
Accuracy of the Group Velocity of Love Waves Extracted from Ambient Seismic Noise
<p>Love wave waveforms derived from the empirical Green's functions and Ground Truth earthquake in my manuscript submitted to Journal of Geophysical Research: Solid Earth.</p>
Sentinel-1 T156 co-seismic interferogram of Kumamoto EQ
<p>Sentinel-1ascending co-seismic interferogram (wrapped) of Kumamoto Earthquake.</p> <p>Master acquisition time: 2016-04-08</p> <p>Slave acquisition time: 2016-04-20 </p> <p>Track: 156</p> <p>Perpendicular Baseline: 68m</p> <p>Multilooking: 10 Azimuth, 2 Range</p>
Sentinel-1 T163 co-seismic interferogram of Kumamoto EQ
<p>Sentinel-1descending co-seismic interferogram (wrapped) of Kumamoto Earthquake.</p> <p>Master acquisition time: 2016-03-27</p> <p>Slave acquisition time: 2016-04-20 </p> <p>Track: 163</p> <p>Perpendicular Baseline: 4m</p> <p>Multilooking: 10 Azimuth, 2 Range</p>
ALOS2 T197 co-seismic interferogram of Amatrice earthquake (Italy)
<p>ALOS2 T197 co-seismic interferogram (wrapped) of Amatrice earthquake (Italy). <br /> Data Type: wrapped Interferogram (radians)<br /> Observation interval: 09092015AL2_24082016AL2<br /> Sensor: ALOS2<br /> Wavelength: 23,6 [cm]<br /> Look Angle: 36.6 [deg]<br /> Projection: Geografic Lat-Long (WGS84)<br /> Applied Phase Filter: Goldstein 0.5<br /> Author: IREA - CNR</p> <p><em>Acknowledgments</em>: JAXA, ESA GEP, CNR-IREA, Italian DPC</p> <p> </p>
Indexed Data Set From Molisan Regional Seismic Network Events
<p>Abstract:</p> <p><em>After the earthquake occurred in Molise (Central Italy) on 31st October 2002 (Ml 5.4, 29 people dead), the local Servizio Regionale per la Protezione Civile to ensure a better analysis of local seismic data, through a convention with the Istituto Nazionale di Geofisica e Vulcanologia (INGV), promoted the design of the Regional Seismic Network (RMSM) and funded its implementation. The 5 stations of RMSM worked since 2007 to 2013 collecting a large amount of seismic data and giving an important contribution to the study of seismic sources present in the region and the surrounding territory. This work reports about the dataset containing all triggers collected by RMSM since July 2007 to March 2009, including actual seismic events; among them, all earthquakes events recorded in coincidence to Rete Sismica Nazionale Centralizzata (RSNC) of INGV have been marked with S and P arrival timestamps. Every trigger has been associated to a spectrogram defined into a recorded time vs. frequency domain.<br> The dataset has been fully indexed in respect of the recorded spectra: list of all records, list of earthquakes, list of multiple earthquakes records.<br> The main aim of this structured dataset is to be used for further analysis with data mining and machine learning techniques on image patterns associated to the waveforms.</em></p>
Helheim Seismic data August 2014-2015
<p>Seismic traces for the BHE/BHN/BHZ channels (Easting, Northing and Vertical components) from August 2014 - August 2015 at Helheim Glacier (66.4N 38.2W). The locations of the seismometers HEL1-HEL4 and more details about the seismometers are given in a Cryosphere journal article (Mei, M. J., Holland, D. M., Anandakrishnan, S., and Zheng, T.: Calving localization at Helheim Glacier using multiple local seismic stations, The Cryosphere, 11, 609-618, doi:10.5194/tc-11-609-2017, 2017) that uses this data.</p>
Data set to article "Synthetic inversions for density using seismic and gravity data" by Blom, Boehm and Fichtner
<p><strong>Data set to “Synthetic inversions for density using seismic and gravity data” by Nienke Blom, Christian Boehm and Andreas Fichtner</strong></p> <p>This data set relates to our paper <em>“Synthetic inversions for density using seismic and gravity data”</em><em>, </em><em>in which we discuss the imaging of density variations inside the Earth as a separate, independent parameter using seismic waveform tomography and gravity measurements</em>. The research consists of synthetic experiments conducted using a home-written MATLAB wave propagation code. The data set contains the code itself, the input files and output files for each of the experiments described in the manuscript and its supplementary material, all the figures, some extra material (such as a video of Figure 1 in the manuscript) and some scripts.</p> <p>Below I’ll give a description of the contents of this data set and how they are structured, followed by an overview of the experiments conducted for the paper.</p> <p>In this data set, the following things can be found:</p> <ul> <li> <p>There is a directory with all the figures: FIGURES. This contains the figures in *.pdf, *.eps and *.png formats.</p> </li> <li> <p>There is a directory FD2D_ADJOINT_CODE with in it the MATLAB code fd2d-adjoint. If you plan on using our code, it would be awfully kind if you'd make a reference both to the code and to this paper. It was a lot of work to develop the code and the experiments. NOTE: the code supplied here is a snapshot of the code taken in February 2017. A more up-to-date version might be found on github (www.github.com/Phlos/fd2d-adjoint)</p> </li> <li> <p>For each (series of) experiment(s) described in the paper, there is a directory T1, T2, …, Tn. This also holds for the supplementary tests, the folders for which are designated with the suffix .SUPPLEMENTARY.</p> </li> <li> <p>For Figure 1 in the manuscript, there is a directory Fig1.snapshots. In this directory, everything pertaining to the snapshots figure and its corresponding video can be found.</p> </li> <li> <p>There is a separate directory SCRIPTS with a couple of useful scripts that might be used in addition to the ones in the fd2d-adjoint code.</p> </li> </ul> <p><br> In each of the test directories T1...Tn, there are subdirectories for each experiment conducted within that test framework. Each of the subdirectories has a name Systematic.test-[xxx]. Within those Systematic.. directories, the following can be found:</p> <ul> <li> <p>an input file Systematic….input_parameters.m that can be copied to [fd2d-adjoint]/input/input_parameters.m in order to re-run the experiment. As the code has been under development while the tests were run, it may be that some input parameters are missing from the earlier experiments.</p> </li> <li> <p>A mat-file obs.all-vars.mat. If this file is copied to [fd2d-adjoint]/output/Systematic.test… , this saves the recalculation of the ‘obs’ data when the code is run.</p> </li> <li> <p>A mat-file initial_misfits.mat. If this file is copied to [fd2d-adjoint]/output/Systematic.test… , this saves the recomputation of the initial misfits with respect to the obs data when the code is run.</p> </li> <li> <p>A file lbfgs_output_log.txt which monitors the misfit and gradient development across the iterations. If the inversion was restarted a couple of times, all of this remains in the logfile.</p> </li> <li> <p>For each iteration of the inversion iter[xxx], an iter[xxx].all-vars.mat file, which contains most of the matlab output files for this iteration.</p> </li> <li> <p>For each iteration of the inversion iter[xxx], some figures:</p> <ul> <li> <p>a model plot of the current model anomalies with respect to the background model iter[xxx].model-diff.rhovsvp.png.</p> </li> <li> <p>a gravity plot of the gravity vector difference between the current model and the background model iter[xxx].gravity_difference.png.</p> </li> <li> <p>a kernel plot of the total relative kernels (whether seis only or seis+grav) of the current model in rho-mu-lambda parametrisation: iter[xxx].rho-mu-lambda.png.</p> </li> </ul> </li> </ul> <p><br> </p> <p>Now follows a brief description of each of the (series of) tests conducted for the paper. The test numbers are mostly chronological, and so are the Systematic.test… subdirectories.</p> <ul> <li> <p><strong>Figure 1</strong>: shows snapshots of wave propagation past a density anomaly. The full data for this and the full video are given in the Fig1.snapshots. <em>Discussed in: Figure </em><em>1 of the manuscript.</em></p> </li> <li> <p><strong>T1: </strong><strong>reference.</strong> A reference test in which we assess to which density can be recovered as an independent parameter. <em>Discussed in: Figure </em><em>4</em></p> <ul> <li> <p>Reference experiment: Systematic.test-033</p> </li> </ul> </li> <li> <p><strong>T2: </strong><strong>ignored density.</strong> A test in which the effect is explored if density is ignored, i.e. if it is kept fixed to the starting model. <em>Discussed in: Figure </em><em>4</em></p> <ul> <li> <p>Fixing density: Systematic.test-040</p> </li> </ul> </li> <li> <p><strong>T3: </strong><strong>starting model</strong>. A series of test in which is explored to what extent the starting models of P and S seismic velocity influence the recovery of density. In the different sub-tests, different levels of information on P and S velocity are already present. <em>Discussed in: Figure </em><em>6</em></p> <ul> <li> <p>vs, vp 100% correct: Systematic.test-029</p> </li> <li> <p>vs, vp 75% correct: Systematic.test-037</p> </li> <li> <p>vs,vp 50% correct: Systematic.test-036</p> </li> </ul> </li> <li> <p><strong>T4: </strong><strong>fixed velocities</strong>. A series of tests in which is explored to what extent one can “get away with” only updating density, assuming that the models for P and S velocity are already sufficiently accurate. <em>Discussed in: Figure </em><em>7</em></p> <ul> <li> <p>vs,vp fixed at 50% correct: Systematic.test-038</p> </li> <li> <p>vs, vp fixed at 75% correct: Systematic.test-041</p> </li> <li> <p>vs, vp fixed at 100% correct: Systematic.test-039</p> </li> </ul> </li> <li> <p><strong>T5: </strong><strong>gravity</strong>. A set of tests in which the addition of gravity data to the (up until here purely) seismic inversion. Both the full gravity vector and its potential are used as gravity data. <em>Discussed in: Figure </em><em>8</em></p> <ul> <li> <p>seismic + full gravity vector (x,z) data: Systematic.test-045</p> </li> <li> <p>seismic + gravity potential data (‘geoid’): Systematic.test-046</p> </li> </ul> </li> <li> <p><strong>T6: noise</strong>. A series of tests in which the addition of noise to the seismic data is explored. Both correlated and uncorrelated noise are explored. Noise levels vary across frequencies. <em>Discussed in: Figure </em><em>9</em></p> <ul> <li> <p>correlated noise: Systematic.test-050</p> </li> <li> <p>uncorrelated noise: Systematic.test-052</p> </li> </ul> </li> <li> <p><strong>T7: impedance</strong>. A test in which the impedance contrast across anomaly boundaries are set to zero. It is explored to what extent the recovery of density relies on the presence of an impedance contrast. <em>Discussed in: Figure </em><em>10</em></p> <ul> <li> <p>no impedance contrast: Systematic.test-055</p> </li> </ul> </li> <li> <p><strong>T8: parametrisation (</strong><em><strong>supplementary</strong></em><strong>)</strong>. A test in which it is explored to what extent the inversion is affected if an inversion parametrisation using density and the elastic parameters mu and lambda is used, instead of the otherwise used parametrisation density-S velocity-P velocity. <em>Discussed in: </em><em>Supplementary </em><em>Figure </em><em>1,2 @ </em><em>Supplementary_material.pdf</em></p> <ul> <li> <p>inversion parametrisation rho-mu-lambda (reference target model): Systematic.test-032</p> </li> <li> <p>inversion parametrisation rho-mu-lambda with ‘scaling’ target model: Systematic.test-062a</p> </li> </ul> </li> <li> <p><strong>T9: scaling relations</strong>. A set of tests in which it is explored to what extent the recovery of density and seismic velocities is influenced if density is scaled to S velocity using a fixed scaling. <em>Discussed in: Figure </em><em>5</em></p> <ul> <li> <p>target model with density scaled to S velocity in different ways; all parameters free: Systematic.test-063</p> </li> <li> <p>same target model, but now density is scaled to S velocity with a fixed relationship: Systematic.test-067</p> </li> </ul> </li> <li> <p><strong>T10: anomaly strength (</strong><em><strong>supplementary</strong></em><strong>)</strong>. A set of tests in which the effect of the strength of the anomalies on the recovery of density and the other parameters is investigated. <em>Discussed in: </em><em>Supplementary </em><em>Figure </em><em>3-5 @ </em><em>Supplementary_material.pdf</em><em> </em></p> <ul> <li> <p>target model like reference case, but the anomalies 10% of PREM instead of 1%: Systematic.test-065</p> </li> <li> <p>target model like reference case, but the anomalies <em>in the upper mantle only</em> 10% of PREM instead of 1%: Systematic.test-064</p> </li> </ul> </li> </ul> <p><br> </p> <p>If you have any further questions, feel free to contact me.</p> <p>All the best,</p> <p>Nienke Blom, Utrecht University<br> n.a.blom@uu.nl<br> nienke.blom@posteo.net</p> <p> </p>
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.