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33 results for “Ambient Noise Tomography”
Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation
<p>We used DSurfTomo (<a href="https://github.com/HongjianFang/DSurfTomo">HongjianFang/DSurfTomo: Direct inversion of surface dispersion data based on ray tracing (github.com)</a>) for the tomography.</p> <p>ABC2_2023.dat is the travel time of C1 and C2 cross-correlation functions, used in our tomography.</p> <p>ManualDSurfTomoV1.3.pdf is the manual of DSurfTomo, including the data format description for ABC2_2023.dat.</p> <p>yunqiVs3D.txt is the interpolated 3D Vs model, including longitude, latitude, depth (meter), Vs (m/s).</p> <p>Previous version error: I forgot to write the Vs value.</p>
Data for "Variation in Upper Plate Crustal and Lithospheric Mantle Structure in the Greater and Lesser Antilles from Ambient Noise Tomography"
<p>This is the phase velocity information and the shear wave model for the g-cubed paper:</p> <p>"Variation in Upper Plate Crustal and Lithospheric Mantle Structure in the Greater and Lesser Antilles from Ambient Noise Tomography"</p>
Magmatic System of the Hainan Hotspot Revealed by Ambient Noise Tomography
<p>The datasets consists of three parts:</p> <p>1.Cross-correlation Functions Data(all_CCFs.zip):<br>Format:<br> Lon (station A) Lat (station A) Elevation (A)<br> Lon (station B) Lat (station B) Elevation (B)<br> Time (t=0) GAB(t) GBA(t)<br> Time (t=dt) GAB(t) GBA(t) <br> Time (t=2dt) GAB(t) GBA(t)</p> <p><br>2. Rayleigh Wave Phase Dispersion Data (CDisp.zip):<br>Format:<br> Lon (station A) Lat (station A)<br> Lon (station B) Lat (station B)<br> Period (s) Vs (km/s)</p> <p>3.S-Velocity Model:<br>Format:<br> Longitude Latitude Depth Absolute_velocity Average_velocity Relative_velocity</p>
Adjoint tomography data for LASSIE array by ambient noise differential adjoint tomography
<p>The LASSIE array shear-wave velocity model from ambient noise differential adjoint tomography. </p><p>If you use this dataset, please cite the article published online: </p><p>Liu, X., Beroza, G.C. & Li, H. Ambient noise differential adjoint tomography reveals fluid-bearing rocks near active faults in Los Angeles. <i>Nat Commun</i> <strong>14</strong>, 6873 (2023). <a href="https://doi.org/10.1038/s41467-023-42536-4">https://doi.org/10.1038/s41467-023-42536-4</a> </p><p>Here are the explanations for each of the five columns of the CSV file:<br>- lat: Latitude is a geographic coordinate <br>- lon: Longitude is a geographic coordinate <br>- depth: Depth from surface of the earth<br>- Vs_init: the initial shear wave velocity from ray theory and layered-cake inversion below each surface location<br>- Vs_adj: the adjoint shear wave velocity tomography result from ambient noise differential adjoint tomography</p><p> </p>
Data and program codes to reproduce the results presented in "Crustal structure beneath Central Kamchatka inferred from ambient noise tomography"
<p>This archive contains the SURF_TOMO code for the surface-wave tomography (<em>Koulakov et al., 2016</em>) and all the necessary files and instructions for reproducing the results presented in the paper “Crustal structure beneath Central Kamchatka inferred from ambient noise tomography” by Igor Egorushkin, Ivan Koulakov, Andrey Jakovlev, Hsin-Hua Huang, Eugeny I. Gordeev, Ilyas Abkadyrov, and Danila Chebrov.</p>
Mechanisms for Layered Anisotropy and Anomalous Magmatism of Alaska Subduction System Revealed by Ambient Noise Tomography and the Wave Gradiometry Method
<p>Depth Coverage: 2.0 - 230.0 km<br>Areal Coverage: Latitude: 54.8 to 72.0 Longitude: -168.0 to -129.0<br>Model Description: A high-resolution 3D azimuthal anisotropic shear wave velocity model beneath Alaska and its surroundings on a ~50‐km grid extracted by ambient noise tomography and wave gradiometry method .</p> <p>Columns in the subfile, from left to right, are latitude, longitude, shear wave velocity, fast direction, and the magnitude of anisotropy, respectively. </p>
Upper Crustal Structure of the Xinfengjiang Reservoir from Ambient Noise Double Beamforming Tomography and Its Implications for Induced Seismicity
<p>The file "CC.tar.gz" contains the linearly stacked ZZ component cross-correlations for all station pairs.</p> <p>The file "Model.tar.gz" contains the 3-D upper crustal model of the Xinfengjiang Reservoir via ambient noise Double-Beamforming tomograpy.</p>
Three-dimensional crustal channel flows beneath the southeastern Tibetan Plateau revealed by full-waveform ambient noise tomography
<p>This is a new version of Vp and Vs models for paper titled "Three‐Dimensional Crustal Channel Flows Beneath the Southeastern Tibetan Plateau Revealed by Full‐Waveform Ambient Noise Tomography" published in Geophysical Research Letters.</p> <p>Modification history: new Vs model includes from surface downward to 120 km depth.</p> <p>Please ignore the models in version 1 and 2.</p>
Geothermal and structural features of La Palma island (Canary Islands) imaged by ambient noise tomography
<p>These folders contain all the results obtained in the ambient noise tomography of La Palma for geothermal exploration. </p>
Continental Reworking in the Eastern South China Block and Its Adjacent Areas Revealed by F-J Multimodal Ambient Noise Tomography
<p><span>It contains ZZ-component cross-correlation functions and the 3-D Vsv model in the eastern South China Block.</span></p>
A Mid-crustal Channel of Positive Radial Anisotropy Beneath the Eastern South China Block From F-J Multimodal Ambient Noise Tomography
<p><span>It contains the CCAB-FJpy, the Python code package for calculating the multi-component cross-correlation functions (CCFs) and extracting multimodal Love dispersions with multi-component frequency-Bessel (F-J) transform method. . Also, it contains multi-component CCFs (i.e., RR and TT), Vsh model and 3-D crustal radial anisotropy model of the eastern South China Block (ESCB).</span></p>
Crustal and upper mantle structure of the Tien Shan orogenic belt from full-wave ambient noise tomography
<p>Empirical Green’s functions, phase delays and shear wave velocity model of Tien Shan</p>
Mid-crustal low-velocity zones beneath Southeastern Coastal China revealed by multimodal ambient noise tomography: insights into Mesozoic Magmatic Activities
<p><span>It contains Rayleigh dispersion data </span><span>and</span><span> a three</span><span>-</span><span>dimensional crustal shear</span><span>-</span><span>wave velocity model of the Southeastern Coastal China </span><span>(</span><span>SCC</span><span>)</span><span>.</span></p>
Data from: 3D shear-wave velocity model of central Makran using ambient-noise adjoint tomography
<p>The Makran subduction zone is unique in its wide onshore thick accretionary prism, and a volcanic arc not parallel to the E-W trend of the Makran accretionary prism. To investigate the internal structure of the accretionary prism, the crustal nature of Jaz Murian Depression, and the trend of the buried trench we have calculated a 3D shear-wave velocity model for a region around the border between eastern and western Makran using ambient-noise adjoint tomography and data from IASBS/CAM Makran temporary seismic network. In close agreement with previous works, our velocity model shows that the onshore accretionary prism consists of a low-velocity zone in the south and a high-velocity zone in the north with an average thickness of accreted sediments of 22 and 30 km, respectively. The young age of the surface rocks of the high-velocity part of the prism suggests the presence of a significant volume of igneous rocks scraped from the subducting oceanic slab. The velocity model indicates a continental crust of ~40 km with a thick sedimentary cover of ~20 km for the eastern part of Jaz Murian Depression. The presence of a NE-SW trending low-velocity region at a depth interval of 40-60 km subparallel with the trend of the volcanic arc, intermediate-depth earthquakes, and geometry of the overriding plate might be related to the trend of the buried trench. This implies that the observed NE-SW trending volcanic arc might be related to the geometry of the buried trench and not the eastward reduction of the subduction angle.</p>
Data from: 3D shear-wave velocity model of central Makran using ambient-noise adjoint tomography
Open the record for dataset details and reuse information.
Different Formation Modes of the North‒South-Trending Rifts in Southern Tibet: Implications from Ambient Noise Tomography
<p>To reveal the formation modes of the southern Tibetan rifts, we performed ambient noise tomography to image the shear-wave velocity structure beneath different rifts. This dataset consists of there parts. The CFs_STB.zip contains correlation functions of all the station pairs. The disps_STB.zip contains the Rayleigh surface wave phase velocity dispersion curves. And the 3-D shear-wave model beneath the rifts of southern Tibet Pleatau is provided in Rift_STB_Vs_model.zip.</p>
Crust and uppermost mantle magma plumbing system beneath Changbaishan intraplate volcano, China/North Korea, revealed by ambient noise adjoint tomography
<p>This dataset contains the original ambient noise cross-correlation data and the final inverted 3D Vs model for our research paper "Crust and uppermost mantle magma plumbing system beneath Changbaishan intraplate volcano, China/North Korea, revealed by ambient noise adjoint tomography", which has been published in <strong><em>Geophysical Research Letters</em></strong> (<a href="https://doi.org/10.1029/2022GL098308">https://doi.org/10.1029/2022GL098308</a>).</p>
Ambient noise tomography of Gran Canaria island (Canary Islands) for geothermal exploration
<p>This file contains the 3-D S-wave velocity model of Gran Canaria island.</p> <p><strong>Abstract</strong></p> <p>Ambient noise tomography (ANT) has proved to be efficient in resolving crustal structures for different purposes. Despite the numerous studies carried out in Gran Canaria in the past for purposes of geothermal exploration, ANT had never been used before on this island. We applied this technique to understand Gran Canaria island's shallow structure, determining the 3-D S-wave velocity model of the island and focusing on its implications for geothermal exploration. We used data from 30 seismic stations installed between October 2019 and February 2020. Our findings revealed five relevant velocity anomalies helpful in understanding the island's geology and its geothermal potential. In particular, we identified two high-velocity anomalies in the central part of the island aligned with the primary northwest-southeast structural trends of Gran Canaria. We interpret them as evidence of intrusive volcanic bodies emplaced during the early stages of the island's geological history. We also identified three low-velocity zones located in different parts of the island. We maintain that these anomalies could be associated with porous and highly fractured materials produced during the more recent volcanic episodes. In addition, we observed a spatial correlation of anomalies in the electric conductivity identified in previous studies and strong lateral gradients in our S-wave velocity model. We interpret them as evidence of hydrothermal circulation and thermal anomalies in correspondence of lateral contacts between different geological units and/or faults.</p> <p> </p>
The dataset for the paper "Ambient noise tomography reveals asymmetric impact damage zone beneath Lonar crater, India: Implications for oblique impact cratering in heterogeneous basalt with planetary applications"
<p>These datasets are used to produce the results and generate figures and tables in the manuscript titled as "Ambient Noise Tomography Reveals Asymmetric Impact Damage Zone Beneath Lonar Crater, India: Implications for Oblique Impact Cratering in Heterogeneous Basalt with Planetary Applications" submitted to JGR-Planets.</p>
High resolution shallow structure of Ebao basin revealed with DAS ambient noise tomography and its relation to earthquake ground motion
<p>EBAO dataset: fig2b, fig3a, fig9, figS5</p>
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