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33 results for “geological modeling”
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>
Evolution of model and geological inconsistencies during inversion
<p>Supplementary material to: </p> <p>Giraud, J., Caumon, G., Grose, L., Ogarko, V., and Cupillard, P.: Integration of automatic implicit geological modelling in deterministic geophysical inversion, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-129, 2023</p> <p>The GIF shows a 3D view of the inverted model and its geological inconsistencies during inversion when geological correction is applied at each iteration. </p>
Model data repository of "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins"
<p>This dataset contains the data used in Wu et al. (2022): "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins".</p>
Yerrida Basin 3D geological model and gravity inversion results
<p>This dataset contains an archive for an implicit 3D geological model of the Yerrida Basin, southern Capricorn region, Western Australia.</p> <p><strong><em>Yerrida_Basin_3D.zip </em></strong>is a GeoModeller three dimensional geological model. Also included are 2D and 3D voxets resulting from inversion of gravity data using the geological model as a constraint. Geomodeller software is available from here: <a href="https://www.intrepid-geophysics.com/ig/index.php?page=downloads">https://www.intrepid-geophysics.com/ig/index.php?page=downloads</a></p> <p>This is a companion dataset for the paper submitted to the scientific journal Solid Earth: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin.<em> </em>Mark D Lindsay, Sandra Occhipinti, Crystal LaFlamme, Alan Aitken, Lara Ramos.</p> <p> </p>
Yerrida Basin 3D 'Noddy' geological and forward models
<p>This dataset contains an archive for kinematic 3D geological models of the Yerrida Basin, southern Capricorn region, Western Australia. These models were used to investigate the influence of adding high density material (the mafic Killara Formation) on the calculated gravitational response.</p> <p><strong><em>Yerrida_Basin_3D_Noddy.zip </em></strong>is a set of three dimensional 'Noddy' geological models.</p> <p>1. <strong>Yerrida_gravity_response_noKillara.his </strong>A model with no Killara Formation.</p> <p>2. <strong>Yerrida_gravity_response-500mKillara.his</strong> A model with 500 m thick Killara Formation.</p> <p>3. <strong>Yerrida_gravity_response-2000mKillara.his</strong> A model with 2000 m thick Killara Formation.</p> <p>Corresponding gravity responses (*.grv) files are supplied.</p> <p>A Noddy executable installation file in Windows format is supplied in this archive or can be downloaded from <a href="http://tectonique.net/noddy/">http://tectonique.net/noddy/</a></p> <p>This is a companion dataset for the paper submitted to the scientific journal Solid Earth: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin.<em> </em>Mark D Lindsay, Sandra Occhipinti, Crystal LaFlamme, Alan Aitken, Lara Ramos.</p>
Absolute model ages of three craters in the vicinity of the Chang'E-5 landing site and their geologic implications
<p>We used the Crater Size Frequency distribution (CSFD) method to yield the formation ages of Pythagora, Sharp B, and Harpalus crater, and the crater density values (N(1)). Here we provide the crater counting data.</p>
Text-fig. 3. Correlation model of the studied sections of Late Miocene deposits. a. Tuapse highway bridge. b. Gaverdovsky. c. Volchaya Balka. 1. reversed polarity; 2. normal polarity; 3. unstudied interval. in Late Miocene (Early Turolian) Vertebrate Faunas And Associated Biotic Record Of The Northern Caucasus: Geology, Taxonomy, Palaeoenvironment, Biochronology
Text-fig. 3. Correlation model of the studied sections of Late Miocene deposits. a. Tuapse highway bridge. b. Gaverdovsky. c. Volchaya Balka. 1. reversed polarity; 2. normal polarity; 3. unstudied interval.
3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)
<p>3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)</p> <p>M. Jessell<sup>1,2</sup>, J. Giraud<sup>1,2</sup>, M. Lindsay<sup>1,2 </sup></p> <p><sup>1</sup>Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia</p> <p><sup>2</sup>Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia</p> <p>Contact author: Jeremie Giraud (jeremie.giraud@uwa.edu.au)</p> <p>Companion dataset to the paper:</p> <p>Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x open-source code, J. Giraud, V. Ogarko, R. Martin, M. Lindsay, M. Jessell, Geoscientific Model Development Discussions.</p> <p>This dataset contains models and data shown in the paper, in both 2D and 3D:</p> <p>1. Geological model</p> <ul> <li>Reference lithology voxet:</li> </ul> <p>The reference geological model was obtained using public data from the Geological Survey of Western Australia and modified subsequently (stretched vertically and flattened at surface level) for the purpose of this study.</p> <ul> <li>Probability voxet<br> The lithology probability voxet was derived using Monte Carlo simulations for uncertainty estimation as mentioned in the paper.</li> </ul> <p>2. True and inverted models for density and magnetic susceptibility</p> <p>Derivation is detailed in the paper; it uses fictitious density and magnetic susceptibility values.</p> <p>3. Bouguer and total magnetic field anomaly</p> <p>Calculation is detailed in the paper.</p> <p>The authors are supported, in part, by Loop – Enabling Stochastic 3D Geological Modelling (LP170100985) and the Mineral Exploration Cooperative Research Centre (MinEx CRC) whose activities are funded by the Australian Government's Cooperative Research Centre Program. This is MinEx CRC Document 2021/3. Mark Lindsay acknowledges funding from the ARC and DECRA DE190100431.</p> <p>It is a companion dataset to: <br> Vitaliy Ogarko, Jeremie Giraud, & Roland. (2021, February 5). Tomofast-x v1.0 source code (Version 1.0). Zenodo. <a href="http://doi.org/10.5281/zenodo.4452620">http://doi.org/10.5281/zenodo.4452620</a></p>
Data for: Modeling the distribution of the endangered Jemez Mountains salamander (Plethodon neomexicanus) in relation to geology, topography, and climate
<p>The Jemez Mountains salamander (<em>Plethodon neomexicanus</em>; hereafter JMS) is an endangered salamander restricted to the Jemez Mountains in north-central New Mexico, United States. This strictly terrestrial species requires moist surface conditions for mating and foraging. Threats to its current habitat include fire suppression and ensuing severe fires, changes in forest composition, habitat fragmentation, and climate change. Forest composition changes resulting from reduced fire frequency and increased tree density suggest that its current aboveground habitat does not mirror its historically successful habitat regime. We hypothesized that geology and topography might play a significant role in the current distribution of the salamander. We modeled the distribution of the JMS using a machine learning algorithm to assess how geology, topography, and climate variables influence its distribution. Our habitat suitability map reveals low uncertainty in model predictions, and we found slight discrepancies between the designated critical habitat and the most suitable areas for the JMS. Because geological features are important to its distribution, we recommend that geological and topographical data are considered, both during survey design and in the description of localities of JMS records once detected.</p>
Truth Tables for consistency-checking 3D geological models
<p>The Microsoft Excel and PDF representations of the Truth Tables and geo-feature relation exampels are provided as supplementary information to the GMD publication: </p> <p><strong>Consistency-Checking 3D Geological Models</strong></p> <p><strong>Marion N. Parquer, Eric A. de Kemp, Boyan Brodaric and Michael J. Hillier</strong></p> <p> </p>
Data for: Modeling the distribution of the endangered Jemez Mountains salamander (Plethodon neomexicanus) in relation to geology, topography, and climate
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Data from: Interaction of sequence data and paleogeographic priors in biogeographic dating: How could biological data inform time-constrained geological models?
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Geological model of Long Valley Caldera
<p>Borehole lithostratigraphic data (Table S1) and boundary surfaces (surface_01-05) of Long Valley Caldera 3D geological model. This geological model is used in Gola G., Barone A., Castaldo R., Chiodini G., D'Auria L., Garcia-Hernandez R., Pepe S., Solaro G. & Tizzani P. "A novel multidisciplinary approach for the thermo-rheological study of volcanic areas: The case study of Long Valley Caldera", which has been submitted for possible publication in Journal of Geophysical Research - Solid Earth.</p> <p>Coordinates: North America NAD27 UTM Zone 11N</p> <p>Easting: 311000 - 360000 m.; Northing: 4155000 - 4187000 m; XY resolution: 500 m.</p>
Data from: Postglacial recolonization of North America by spadefoot toads: integrating niche and corridor modeling to study species' range dynamics over geologic time
<p>Understanding the factors that shape species' distributions is a key topic in biogeography. As climates change, species can either cope with these changes through evolution, plasticity or by shifting their ranges to track the optimal climatic conditions. Ecological niche modeling (ENM) is a widespread technique in biogeography that estimates the niche of the organism by using occurrences and environmental data to estimate species' potential distributions. ENMs are often criticized for failing to take species' dispersal abilities into consideration. Here, we attempt to fill this gap by combining ENMs with dispersal and corridor modeling to study the range dynamics of North American spadefoot toads (Scaphiopodidae) over the Holocene. We first estimated the current and past distributions of spadefoot toads and then estimated their past distributions from the Last Glacial Maximum (LGM) to the present day. Then, we estimated how each taxon recolonized North American by using dispersal and corridor modeling. By combining these two modeling approaches we were able to 1) estimate the LGM refugia used by the North American spadefoot toads, 2) further refine these projections by estimating which of the putative LGM refugia contributed to the recolonization of North America via dispersal, and 3) estimate the relative influence of each LGM refugium to the current species' distributions. The models were tested using previously published phylogeographic data, revealing a high degree of congruence between our models and the genetic data. These results suggest that combining ENMs and dispersal modeling over time is a promising approach to investigate both historical and future species' range dynamics.</p>
Pore-Scale Modeling of Carbon Dioxide and Hydrogen Transport during Geologic Gas Storage
<p>This file contains the data discussed in association with our journal submission. We discuss pore-scale simulations to compare CO2 and H2 invasion at subsurface conditions in the context of geologic storage.</p>
Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine
<p>This is the relevant data of the article "Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine"</p>
Trapping, Remobilization, Ostwald ripening, and Hysteresis in Geological CO2 Storage: Pore-Scale Imaging and Modelling
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Figures and Data for "CO2 rock physics modeling for reliable monitoring of geologic carbon storage"
<p>The following data includes all data used to generate figures in this study. We will update the link to the paper once it is published. It has been accepted in Nature Comm Earth and Environment. LANL has approved this release: LA-UR-24-25434.</p>
Data from: Postglacial recolonization of North America by spadefoot toads: integrating niche and corridor modeling to study species’ range dynamics over geologic time
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