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15 results for “velocity dispersion”
Fundamental mode Rayleigh wave group velocity dispersion data
<p>Fundamental-mode Rayleigh-wave group-velocity dispersion curves have been computed from 14706 regional waveforms (ray-paths) sampling India, Himalaya, Tibet and surrounding regions. These have been combined through ray-based travel-time tomography to compute 2D group-velocity maps at periods between 10 and 120~s. These ray-paths sample the region with unprecedented density and uniformity. The 2D maps have a lateral resolution of 3\deg x3\deg, comparable to the regional geology. The dispersion curves, at each 1\deg\ spaced node-points, are inverted to obtain a 3D isotropic shear-wave velocity structure.</p>
Fundamental mode Rayleigh wave group velocity dispersion data
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Data from: Covariation in abscission force and terminal velocity of wind-borne sibling seeds alters long distance dispersal projections
1. Despite the fact that seeds are unlikely to be identical—even among siblings within a maternal individual—dispersal models typically use one mean trait value to represent the ability of an entire species to disperse. Previous work has shown that the environmental conditions under which individuals leave the maternal site strongly affect how far seeds will travel. However, less is known about how trait variation within individuals contributes to dispersal or how such variation might interact with abiotic factors. 2. Here, we develop the use of an ergometer in a novel application to investigate variation in seed traits, specifically the force required for seed abscission and seed terminal velocity, exhibited by seeds from different locations within maternal capitula of the invasive species Carduus nutans (Asteraceae). 3. We find that seeds from the center of capitula are significantly easier to liberate and have slower falling velocities than siblings found near the edge of capitula. When abscission force is positively correlated with terminal velocity, slowly falling, easily abscised seeds are projected to travel much further than the average seed on slow winds. 4. Our experimental and theoretical results, which together show that within-individual variation can strongly affect model projections of species dispersal, have important implications for our broader understanding of population spread rates, the spatial structure of populations, metapopulation connectivity, and gene flow in the landscape.
Processed receiver function data, dispersion measurement, and shear velocity model (Dharwar)
<p>Processed receiver function data at 1 sample per second, dispersion measurement, and shear velocity model (Dharwar).</p>
Phase-velocity dispersions and Vs model of Tongzhou,Beijing
<p>Phase-velocity dispersions and Vs model of Tongzhou,Beijing, from the paper 'Improved 3D Shallow-Deep Vs Structure in Tongzhou, Beijing (China), Revealed by Dense Array Ambient Noise Tomography'</p>
Shear wave velocity inversion based on dispersion characteristics of seabed Scholte wave in deep water
<p>Simulated displacement records at the seabed with three different water depths using spectral element method. </p>
Data from: Covariation in abscission force and terminal velocity of wind-borne sibling seeds alters long distance dispersal projections
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Part of the dataset and trained models in "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media" by Meng et al. in Geophysical Research Letters
<p><strong>This repository is created to contain part of the data, codes and trained models in the research project titled "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media".</strong></p> <p> </p>
Improving measurements of the falling trajectory and terminal velocity of wind-dispersed seeds
<p>1. Seed dispersal by wind is one of the most important dispersal mechanisms in plants. The key seed trait affecting seed dispersal by wind is the effective terminal velocity (hereafter "terminal velocity", Vt), the maximum falling speed of a seed in still air. Accurate estimates of Vt are crucial for predicting intra- and interspecific variation in seed dispersal ability. However, existing methods produce biased estimates of Vt for slow- or fast-falling seeds, fragile seeds, and seeds with complex falling trajectories.</p> <p>2. We present a new video-based method that estimates the falling trajectory and Vt of wind-dispersed seeds. The design involves a mirror that enables a camera to simultaneously record a falling seed from two perspectives. Automated image analysis then determines three-dimensional seed trajectories at high temporal resolution. To these trajectories, we fit a physical model of free fall with air resistance to estimate Vt. We validated this method by comparing the estimated Vt of spheres of different diameters and materials to theoretical expectations, and by comparing the estimated Vt of seeds to measurements in a vertical wind tunnel.</p> <p>3. Vt estimates closely match theoretical expectations for spheres and vertical wind tunnel measurements for seeds. However, our Vt estimates for fast-falling seeds are markedly higher than those in an existing trait database. This discrepancy seems to arise because previous estimates inadequately accounted for seed acceleration. </p> <p>4. The presented method yields accurate, efficient and affordable estimates of the three-dimensional falling trajectory and terminal velocity for a wide range of seed types. The method should thus advance the understanding and prediction of wind-driven seed dispersal.</p>
Southeastern Tibetan Plateau Dispersion data & velocity model
<p>We obtained a 3-D isotropic and azimuthal anisotropic model in the southeastern Tibetan Plateau. These datasets contain the dispersion data we picked, and the models we obtained.</p>
3D Vs model and phase velocity dispersion data of the Pearl River Delta onshore-offshore area
<p>3D Vs model and phase velocity dispersion data for PRD.</p>
Improving measurements of the falling trajectory and terminal velocity of wind-dispersed seeds
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Cross-correlation functions, dispersion data, and 3D velocity models of the Chao lake
<p>We obtained a 3-D isotropic and azimuthal anisotropic model beneath the Chao Lake. These datasets contain the cross-correlation functions, dispersion data we picked, and the models we obtained.</p>
Isotropic phase velocity dispersion curves for Superior Province.
<p>SuperiorProvince-DispCurves is a composite dataset of dispersion curves and their associated uncertainties extracted from three Rayleigh wave tomographic studies Darbsyhire et al., 2013, Petrescu et al., 2017, and Foster et al., 2020. We took a weighted average of phase velocities in regions where these different datasets overlapped, with a higher weighting for the dataset with the densest ray path coverage in order to reduce edge effects. It includes a README file.</p>
CSRM Level 2 dataset: event-station Rayleigh wave phase and group velocity dispersion curves in continental China from regional earthquake data
<p>This dataset contains the <strong>54,792</strong> event-station Rayleigh wave phase and group velocity dispersion curves from vertical-component waveforms of <strong>226 </strong>regional earthquakes recorded by <strong>1463</strong> seismic stations. This dataset results from a project of constructing the high-resolution China Seismological Reference Model (<strong>CSRM-1.0</strong>) in the top 100 km of the crust and uppermost mantle in continental China (<a href="https://doi.org/10.1029/2024JB029520">Xiao et al, JGR, 2024</a>) (<a href="https://doi.org/10.5281/zenodo.11098135">model link</a>). </p> <p>本数据库包含了利用中国大陆区域 <strong>1463</strong> 个地震台站记录的 <strong>226</strong> 个区域地震的波形数据计算的 <strong>54,792 </strong>条事件-台站间瑞利波相/群速度频散曲线。该数据库源于构建中国大陆区域高精度地壳和上地幔顶部 100 公里三维地震学模型(<strong>CSRM-1.0</strong>)的工作 (<a href="https://doi.org/10.1029/2024JB029520">Xiao et al, JGR, 2024</a>) (<a href="https://doi.org/10.5281/zenodo.11098135">模型链接</a>) 。</p> <p>If you face any problem or issue in the usage of this dataset, please feel free to communicate with the corresponding author Xiao Xiao (<strong>xiaox.seis@gmail.com</strong>). </p>
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