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5 results for “Clinoforms”
3D geological models of dolomitized clinoforms and flow simulation results: scenario 2 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 1 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 1 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
Data for: Quantifying Bankfull Flow Width Using Preserved Bar Clinoforms from Fluvial Strata
<p>Reconstruction of active channel geometry from fluvial strata is critical to constrain the water and sediment fluxes in ancient terrestrial landscapes. Robust methods—grounded in extensive field observations, numerical simulations, and physical experiments—exist for estimating the bankfull flow depth and channel-bed slope from preserved deposits; however, we lack similar tools to quantify bankfull channel widths. We combined high-resolution lidar data from 134 meander bends across 11 rivers that span over two orders of magnitude in size to develop a robust, empirical relation between the bankfull channel width and channel-bar clinoform width (relict stratigraphic surfaces of bank-attached channel bars). We parameterized the bar cross-sectional shape using a two-parameter sigmoid, defining bar width as the cross-stream distance between 95% of the asymptotes of the fit sigmoid. We combined this objective definition of the bar width with Bayesian linear regression analysis to show that the measured bankfull flow width is 2.34 ± 0.13 times the channel-bar width. We validated our model using field measurements of channel-bar and bankfull flow widths of meandering rivers that span all climate zones (R2 = 0.79) and concurrent measurements of channel-bar clinoform width and mud-plug width in fluvial strata (R2 = 0.80). We also show that the transverse bed slopes of bars are inversely correlated with bend curvature, consistent with theory. Results provide a simple, usable metric to derive paleochannel width from preserved bar clinoforms.</p>
Data for: Quantifying Bankfull Flow Width Using Preserved Bar Clinoforms from Fluvial Strata
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The synthetic and field seismic datasets for "ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation"
<p>This is the synthetic and field siesmic dataset used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". The dimensions of the large-scale and small-scale synthetic seismic datasets are 1600×256 pixels and 900×256 pixels. The field seismic data contains the subsets of the F3 Block, Australia Poseidon, Alaska North Slope seismic data.</p>
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