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104 results for “Inverse Modelling”
Code and datasets for "Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes"
<p>Code and datasets for:</p> <p>Delaney I., M. A. Werder, D. Felix, I. Albayrak, R. M. Boes, D. Farinotti, 2024, Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes. Water Resources Research. </p> <p>For more information, contact Ian Delaney (ianarburua.delaney@unil.ch).</p>
Induced polarization and hydraulic tomography joint inversion results on a synthetic model
<p>These data are supplementary material for the following publication: Lukas Römhild, Gianluca Fiandaca, Peter Bayer (2024): Joint inversion of induced polarization and hydraulic tomography data for hydraulic conductivity imaging, <em>Geophysical Journal International</em>, Volume 238, Issue 2, August 2024, Pages 960–973, <a href="https://doi.org/10.1093/gji/ggae197">https://doi.org/10.1093/gji/ggae197</a></p> <p>The data set presents induced polarization (IP) and hydraulic tomography (HT) inversion results for a simple synthetic model. In addition, a joint inversion approach for both data types was implemented, so that individual and joint inversion results can be compared. We also show how the joint inversion procedure has the potential to correct a petrophysical bias within the IP inversion by incorporating the hydraulic data. The effect of different HT setups is also assessed by implementing different source and receiver positions.</p> <p>The file directory contains the original synthetic model, a subfolder for the synthetic data of IP and HT, and a subfolder for the individual HT and IP, as well as the joint inversion results. The HT results contain three different setups (1m, 2m-inside, 2m-outside). The IP results are shown for five different assumptions of petrophysical bias (-2, -1, 0, +1, +2). The joint inversion results contain all possible combinations of these test cases. For more information on the methodology, we refer to the paper (see above, currently under review).</p>
Line-Source Model based Rapid Inversion for Deriving Large Earthquake Rupture Characteristics using High-rate GNSS Observations
<p>The high-rate GNSS data and GNSS-derived velocity waveforms of six large earthquakes (the 2016 Mw 6.6 Norcia earthquake, the 2010 Mw 7.2 EI Mayor-Cucapah earthquake, the 2016 Mw 7.8 Kaikoura earthquake, the 2019 Mw 7.1 Ridgecrest earthquake, the 2014 Mw 8.2 Iquique earthquake, and the 2015 Mw 8.3 Illapel earthquake) are included in this repository.</p>
Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling."
<p>Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling." </p>
Analogue Models for comparing and testing the relationship between inverted normal faults and pure thrusting during the positive tectonic inversion
<p>This dataset contains a series of Analogue Models for comparing and testing positive tectonic inversion mechanisms and their newly formed structures . Furthermore, it includes 2-D seismic reflection profiles that can be compared with the models presented here. Finally, examples of natural cases that show tectonic inversion processes are included. Both, seismic lines and photos are located on a segment of Andean forearc, specifically, in the Domeyko Cordillera and the Preandean Basins, northern Chile.</p>
Data and models in Support of "Joint and Constrained Inversion as Hypothesis Testing Tools"
<p>The model and data files as well as the plotting and run scripts to reproduce the examples in "Joint and Constrained Inversion as Hypothesis Testing Tools".</p>
Supplementary Material for "Joint Inversion Based on Variation of Information - A Crustal Model of Wilkes Land, East Antarctica"
<p>Inversion_Results_Cluster.nc - contains geographical and vertical information of clusters</p> <p>Inversion_Results_RealData_Run1.nc - contains inverted susceptibilities and densities after the first inversion run with high coupling</p> <p>Inversion_Results_RealData_Run2.nc - contains inverted susceptibilities and densities after the second continuous inversion run with low coupling</p> <p>Plot.ipynb - python script for visualization</p>
Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2
<p>The files in this data repository provide the inputs required to run an inverse modeling case study. This case study will estimate CO<sub>2</sub> fluxes across North America for July 2015 using synthetic observations that have been created to resemble observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite.</p> <p>This data repository is specifically linked to a GitHub code repository (http://doi.org/10.5281/zenodo.3241524 or <a href="https://github.com/greenhousegaslab/geostatistical_inverse_modeling">https://github.com/greenhousegaslab/geostatistical_inverse_modeling</a>). That GitHub repository provides scripts for constructing a geostatistical inverse model that will estimate greenhouse gas fluxes or air pollution emissions using atmospheric observations. The GitHub repository includes a case study that can be run out-of-the-box; the case study provides users an opportunity to test out and explore the inverse modeling code. All of the input data files for that case study are provided for download here.</p> <p>Here is a brief explanation of the different files included in this data repository, but refer to the linked GitHub repository for greater details. All of these files are in a ".mat" file that can be read into Matlab using the <em>load</em> function or can be read into R using the <em>R.matlab</em> package.</p> <ul> <li><strong>H.tar.gz</strong>: This tar file contains the <strong>H</strong> matrices or sensitivity matrices required by the inverse model. These inputs were generated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model as part of NOAA's CarbonTracker-Lagrange program (<a href="https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/">https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/</a>). The <strong>H</strong> matrix is too large to store in a single file. We have therefore split up the matrix into 328 different files (all contained within H.tar.gz). Each file contains a vertical strip of the <strong>H</strong> matrix that corresponds to a different time period of fluxes to be estimated as part of the inverse model.</li> <li><strong>Z.mat</strong>: This file contains the synthetic OCO-2 observations used in the case study. </li> <li><strong>areas_us.mat</strong>: This file lists the area of each model grid box used in the case study in units of meters<sup>2</sup>. This file only includes grid box area for model grid boxes that fall within the continental United States. We estimate CO<sub>2</sub> fluxes across terrestrial North America on a 1 degree latitude by 1 degree longitude grid as part of the case study. Each of these model grid boxes will have a different area, depending upon the latitude of that model grid box. </li> <li><strong>distmat.mat</strong>: This file contains a matrix that lists the distance (in kilometers) between the center of each model grid box used in the case study. </li> <li><strong>land_mask.mat</strong>: We only estimate CO<sub>2</sub> fluxes for terrestrial regions of North America as part of the case study. This land mask is used to convert the fluxes estimated by the inverse model to a latitude-longitude grid that can then be plotted.</li> <li><strong>H_all_OCO2.mat</strong>: This file contains the H matrices summed across differnt time periods. I.e., this file is the sum of all the individual H files contained within H.tar.gz.</li> <li><strong>Xvar.tar.gz</strong>: This file contains different environmental variables from ERA5 meteorology that have been reformatted to match the H footprint matrices. These different variables can be used as predictors of CO2 fluxes in an inverse model. The different variables included in this file are as follows: <ul> <li>Xvar_e.mat Evaporation</li> <li>Xvar_msdwswrf.mat Mean surface downward short-wave radiation flux</li> <li>Xvar_q.mat Specific humidity</li> <li>Xvar_stl1.mat Soil temperature level 1</li> <li>Xvar_stl3.mat Soil temperature level 3</li> <li>Xvar_swvl1.mat Volumetric soil water layer 1</li> <li>Xvar_t2m.mat 2 metre temperature</li> <li>Xvar_tp.mat Total precipitation</li> <li>Xvar_mer.mat Mean evaporation rate</li> <li>Xvar_pev.mat Potential evaporation</li> <li>Xvar_r.mat Relative humidity</li> <li>Xvar_swvl3.mat Volumetric soil water layer 1</li> <li>Xvar_tcc.mat Total cloud cover</li> </ul> </li> </ul>
Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324
<p><strong>Dataset and Model Files for Full Waveform Inversion Seismic Earth Model WUS324</strong></p> <p> </p> <p>Arthur Rodgers</p> <p><em>Geophysical Monitoring Program, Lawrence Livermore National Laboratory, Livermore CA 94551, USA</em>; and</p> <p><em>Department of Earth Sciences, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland</em></p> <p> </p> <p>rodgers7@llnl.gov</p> <p> </p> <p>10.5281/zenodo.11619519</p> <p> </p> <p><strong>Summary</strong></p> <p>This data set includes the metadata and model for the three-dimensional (3D) seismic Earth model WUS324 (Rodgers et al., 2024). This model describes seismic wavespeeds, density and attenuation for the 3D volume spanning the surface to 400 km depth, latitudes from Mexico to Canada (28 to 52) and longitudes from the Pacific Ocean to the Great Plains (-132 to -100). The metadata tabulates the earthquakes and seismic networks and stations used in the creation and validation of 3D seismic Earth model WUS324.</p> <p> </p> <p>The WUS324 model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophys. J. Int., 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. The Visualization Handbook, 717(8). https://doi.org/10.1016/b978-012387582-2/50038-1</p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). https://doi.org/10.5334/jors.148</p> <p>Rodgers, A., C. Doody and A. Fichtner (2024). WUS324: Converged Full Waveform Inversion Improves Waveform Fits While Imaging Crustal and Upper Mantle Structure in the Western United States, manuscript in preparation.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was initiated under Laboratory Directed Research and Development project 20-ERD-008 at Lawrence Livermore National Laboratory (LLNL) and continued with support from the National Nuclear Security Administration Ground-based Nuclear Detonation Detection program. AR is grateful to the Eidgenössische Technische Hochschule, Zürich for support as an Academic Guest and to LLNL for Professional Research and Teaching Leave. This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. LLNL-MI-865269.</p> <p> </p> <p> </p> <p>Files contained in this data set.</p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Description </strong></p> </td> </tr> <tr> <td> <p>WUS324_all_events_project.csv</p> </td> <td> <p>Table of all 216 events considered during the creation of WUS324. This table includes the origin date and time, location and moment tensor parameters.</p> </td> </tr> <tr> <td> <p>WUS324_all_networks.txt</p> </td> <td> <p>Table of all seismic networks and that contributed to WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_events.txt</p> </td> <td> <p>Table of the 126 event names used in the inversions that created WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_inversion_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the creation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_events.txt</p> </td> <td> <p>Table of the 65 event names used in the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_validation_all_paths.csv</p> </td> <td> <p>Table of all event-station paths that contributed to the validation of WUS324.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.h5</p> </td> <td> <p>WUS324 model for simulating waveforms with minimum period of 16 seconds in Salvus HDF5 format.</p> </td> </tr> <tr> <td> <p>WUS324_16sec.xdmf</p> </td> <td> <pre>Auxiliary file for WUS324_16sec.h5, used to import model into Paraview.</pre> </td> </tr> <tr> <td> <p>WUS324.nc</p> </td> <td> <pre>WUS324 model in netCDF format following the metadata standards of the Incorporated Research Institutions for Seismology Earth Model Collaboratory</pre> </td> </tr> </tbody> </table>
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Reinforcement Learning
<p>Well-trained models and generation results for reinforcement learning part of <a href="https://github.com/ytl0410/Polymer-Generative-Models-Benchmark">ytl0410/Polymer-Generative-Models-Benchmark: Well-trained models and generative outcomes for the paper "Benchmarking Study of Deep Generative Models for Inverse Polymer Design" (github.com)</a></p>
Inverse Design of Metamaterials with Manufacturing-Aware Spectrum-to-Shape Diffusion Models
<p>The dataset includes detailed information on the MIM tri-layer metamaterial structures designed and used for training the DiffMeta framework. Specifically, it contains 60000 data:</p> <p>Structural Data: Detailed geometric patterns and composition parameters of the designed MIM tri-layer metamaterial structures.</p> <p>Spectral Data: Spectral measurements on MIM tri-layer metamaterial structures, including emissivity, reflectivity and transmittance spectra across a range of wavelengths.<br><br>The dataset is meticulously organized to facilitate the replication of our study and support further research in the field of metamaterial design. </p>
Data for: Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH Cardiovascular Magnetic Resonance
<p>Magnetic Resonance Imaging measurement data used in our paper about model-based myocardial T1 mapping with sparsity constraints. The data was obtained using a single-short inversion-recovery radial FLASH sequence and is provided in a file format used by the BART toolbox (<a href="http://doi.org/10.5281/zenodo.592960">DOI: 10.5281/zenodo.592960</a>).</p>
Saguaro recruitment data obtained by inverse-growth modelling
<p>Each year, an individual mature large saguaro cactus produces about one million seeds in attractive juicy fruits that lure seed predators and seed dispersers in a three-month feast. From the million seeds produced, however, only a few will persist into mature saguaros. A century of research on saguaro population dynamics has led to the conclusion that saguaro recruitment is an episodic event that depends on the convergence of suitable conditions for survival during the critical early stages. Because most data have been collected in Arizona, particularly in the surroundings of Tucson, most research has relied on a limited amount of environmental variation. In this study, we upscaled this knowledge on saguaro recruitment to a regional scale with a new method that used the inverse-growth modeling of 1,487 saguaros belonging to 13 populations in a latitudinal gradient ranging from arid desert to tropical thornscrub forest in Sonora, Mexico. Using generalized linear and additive mixed models, we created two 110-year-long saguaro recruitment curves: one driven only by previous size, and the second driven by size, drought, and soil structure. We found evidence that saguaro recruitment is indeed episodic with periodicities of 20–30 years possibly related to strong El Niño Southern Oscillation events. Our results suggest that saguaros rely on multidecadal periodic pulses of good beneficial years to incorporate new individuals into their populations. Inverse-growth modelling can be used in a wide variety of plant species to study their recruitment dynamics.</p>
GHG data from inverse models and UNFCCC national inventories v0.1
<p><strong>GHG (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O) data from inverse models and UNFCCC national inventories</strong></p> <p>This dataset contains 5 datasets, including GHG data from inverse models and UNFCCC national inventories in the top emitter countries:</p> <p>- <strong>CO2_inversion_1990-2019</strong>: annual CO<sub>2</sub> flux from from 6 inversion models in three sectors:</p> <ul> <li>'land flux (all land)' -> land flux from all land </li> <li>'land flux (managed land)' -> land flux from managed land</li> <li>'land flux (managed land + lateral adjustment)' -> land flux from managed land by adjusting the lateral flux</li> </ul> <p>- <strong>CH4_inversion_2000-2017</strong>: CH<sub>4</sub> flux from from 10 in-situ inversion (2000-2017) and 11 satellite inversion (2010-2017) models from four sectors:</p> <ul> <li>'anthropogenic (method x)' -> anthropogenic emissions from managed land. x could be 1, 2, 3.1 and 3.2, representing different methods to calculate the emissions in this sector:</li> <li>'fossil' -> emissions from the fossil sector</li> <li>'agriculture & waste' -> emissions from the agriculture and waste sector combined</li> <li>'biomass burning' -> emissions from biomass burning</li> </ul> <p>- <strong>N2O_inversion_1997-2016</strong>: anthropogenic N<sub>2</sub>O emissions from from 3 models.</p> <p>- <strong>Inventory_1990-2019</strong>: inventory data collecting from UNFCCC national inventories. The classification of sectors is corresponding with the inversion data files for each gas specie.</p> <p>- <strong>Inventory_1990-2019_IPCC</strong>: inventory data collecting from UNFCCC national inventories in IPCC category.</p> <ul> </ul>
Inverse modelling of carbonyl sulfide: implementation, evaluation and implications for the global budget
<p>The dataset is the boundary conditions of TM5-4DVAR inversions for COS tracer in years 2016-2019. The dataset is companion with publication: </p> <p>Ma, J., Kooijmans, L. M. J., Cho, A., Montzka, S. A., Glatthor, N., Worden, J. R., Kuai, L., Atlas, E. L., and Krol, M. C.: Inverse modelling of carbonyl sulfide: implementation, evaluation and implications for the global budget, Atmos. Chem. Phys., 21, 3507–3529, https://doi.org/10.5194/acp-21-3507-2021, 2021.</p>
Simulated CO2 time series data based on Jena CO2 inversion and TM3 transport model, and MIROC-ACTM
<p>Each file contains simulated CO2 time series at each surface station. The model, simulation type, and station are specified in the file name. These simulations are driven by either varying winds alone (e.g., Jena_W) or varying winds and fluxes (e.g., Jena_WF). The only MIROC-ACTM run is named ACTM_W_MLO.</p>
An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media
<p>This data includes the training and testing dataset for DNN design and the observation data of synthetic example for validation. </p> <p>The code of TNNA-AUS inversion method.</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file. </p>
Replication Data for: CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
<p>This dataset includes three files necessary for understanding CHEEREIO model output in the demo section of my initial submission to GMD for the paper: <em>CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model.</em> Detailed guides for how to handle these datasets are provided in the <a href="https://cheereio.readthedocs.io/en/latest/Postprocess-workflow.html">CHEEREIO documentation postprocessing page</a>.</p> <ul> <li>control_hemco_diagnostics.nc contains the source-separated prior methane emissions.</li> <li>combined_hemco_diagnostics.nc contains the source-separated and ensemble member separated posterior methane emissions.</li> <li>bigY.pkl contains a Python dictionary which aligns TROPOMI XCH4 with simulated prior and posterior GEOS-Chem XCH4.</li> </ul>
Inverse Model Results for Filchner-Ronne Catchment
<p>This page contains the results of the inversions for basal drag and drag coefficient in the Filchner-Ronne catchment presented in <a href="https://doi.org/10.5194/tc-17-5027-2023">Wolovick et al., (2023)</a>, along with the code used to perform the inversions and L-curves, analyze the results, and produce the figures presented in that paper. </p><blockquote><p>This all looks very complicated. There's so many files here. The description is so long. I just want to know the basal drag!</p></blockquote><p>If you don't want to get into the weeds of inverse modeling and L-curve analysis, or if you are uninterested in wading through our collection of model structures and scripts, then you should use the file <strong>BestCombinedDragEstimate.nc</strong>. That file contains our best weighted mean estimate of the ice sheet basal drag in our domain, along with the weighted standard deviation of the scatter of the different models about the mean. As discussed in the paper, this combined estimate is constructed from the weighted mean of 24 individual inversions, representing 8 separate L-curve experiments on our highest-resolution mesh, with three regularization values per L-curve (best estimate regularization, along with minimum and maximum acceptable regularization levels). Each inversion is weighted according to the inverse of its total variance ratio, which is a quality metric incorporating both observational misfit and inverted structure. For ease of use, these results have been interpolated from the unstructured model mesh onto a 250 m regular grid. <i><strong>If you only want to know the basal drag in the Filchner-Ronne region, that is the only file you should use</strong></i><strong>.</strong></p><p> </p><p>For users who want to go further, we will now explain the remaining files in this release. First we give a brief summary of all of the scripts included here and their functions, and then we will give an explanation of the matfiles that contain the actual inversion and L-curve results. Note that the scripts presented here are the matlab scripts used to organize and set up model runs for ISSM. The Ice-Sheet and Sea-level System Model (ISSM) is a highly versatile parallelized finite-element ice sheet model run in C but controlled using Matlab or Python front-ends. We do not include the underlying code for ISSM here; users who are interested in installing ISSM should go to <a href="https://issm.jpl.nasa.gov/">the ISSM home page</a>. We merely include the Matlab scripts we used to organize our ISSM front-end, set up model structures, and then analyze and visualize results.</p><p><strong>Main Matlab scripts:</strong></p><p>These are the main functional scripts used to set up and run the model.</p><ul><li>ISSMInversion_v3.m. This is the primary script we used to set up the inversions and perform L-curve analysis. It requires a model mesh as input along with some gridded data. It also produces an L-curve figure (figure 3) after performing the L-curve analysis. This script can be run in two modes: "setupandsend", which prepares model structures and sends them to the cluster to be solved, and "loadandanalyze", which loads the solutions from the cluster, saves them to matfiles, and performs analysis and visualization. In addition to L-curve analysis and the L-curve figure, this script can also produce a variety of additional figures of model output that we did not show in the paper.</li><li>MakeISSMMesh_v4.m. This is the script we used to make our model meshes. It requires a domain boundary as input along with some gridded data.</li><li>ModelBoundaryPicker_v1.m. This script opens a crude graphical interface for picking the domain outline.</li><li>OrganizeInversionsForRelease_v2.m. This script assembles L-curve and inverse model results and organizes them into the data release you see here. Note that it doesn't compute the combined drag estimate itself, (that is done by CombinedDragFigure_v1.m), but it does interpolate the combined drag estimate from the model mesh to the grid, and it produces the output netcdf file.</li></ul><p>Note that the gridded data files needed by some of the above scripts are not included in our release here. Users interested in using these scripts for their own projects will need to provide their own gridded inputs, for instance from BedMachine or Measures. </p><p><strong>Figure-making scripts:</strong></p><p>These scripts produced almost all of the figures we presented in the paper, and also computed the statistics we presented in the tables in the paper.</p><ul><li>CombinedDragFigure_v1.m. This script computes the combined drag estimate on the highest-resolution mesh, and makes a figure displaying it (Figure 12 in the paper).</li><li>InversionComparisonFigure_HOSSA_v1.m. This makes figure 11 in the paper and also computes the statistics shown in table 3.</li><li>InversionComparisonFigure_m_v1.m This makes figures 9 and 10, and also computes the statistics shown in table 2.</li><li>InversionComparisonFigure_N_v1.m. This makes figure 8, and also computes the statistics shown in table 1.</li><li>InversionComparisonFigure_v1a.m. This makes figure 4 in the paper.</li><li>InversionResConvergenceFigure_v2.m. This makes figure 6.</li><li>InversionResMisfitFigure_v1.m. This makes figure 7.</li><li>InversionSettingFigure_v1.m. This makes figure 1.</li><li>InversionSpectrumFigure_v1.m. This performs spectral analysis and makes figure 5.</li><li>InversionThermalSettingFigure_v1.m. This makes figure A1.</li><li>MeshSizeFigure_v1.m. This makes figure A2.</li><li>NComparisonFigure_v1.m. This makes figure 2.</li></ul><p><strong>Other utility Matlab functions:</strong></p><p>These miscellaneous function do various tasks. Many of them are called as subroutines of the scripts above. Additionally, many of them are generally useful in contexts beyond the inverse modeling presented here.</p><ul><li>FlattenModelStructure.m. ISSM has the unfortunate convention of saving every variable in 3D meshes on every single 3D mesh node, which is quire wasteful for variables that are actually 2D (ie, most of the model variables). This function flattens all uneccessarily 3D information, but unlike the built-in ISSM function flatten.m, this script preserves the 3D geometry of the mesh, along with 3D variables that actually are 3D (such as englacial temperature, for example). This function can also be run in reverse to expand variables back to full 3D before calling solve().</li><li>intuitive_lowpass.m. This function low-pass filters a 1D dataset using a gaussian filter. It has several options for handling boundary conditions at the end points.</li><li>LaplacianInterpolation.m. This function fills in missing data values for gridded data products by solving Poisson's equation (Laplacian=0).</li><li>LaplacianInterpolation_mesh.m. This function does the same thing but on an unstructured mesh.</li><li>loadnetcdf.m. This function loads variables from netcdf files into the Matlab workspace using a similar syntax as load() for matfiles.</li><li>MultiWavelengthInterpolator.m. This function interpolates gridded data onto an unstructured mesh using a multi-grid approach. The grid is smoothed at multiple wavelengths and each mesh element interpolates from the wavelength that is appropriate for its size. This functionality is useful for preventing aliasing in coarse-resolution areas when interpolating onto a mesh with variable mesh size. It also produces results that are approximately (but not precisely) conservative.</li><li>ThreeByThree.m. This function iteratively performs a 3x3 smoothing on gridded data.</li><li>unpack.m. This function takes a structure and "unpacks" it by making every field into a variable in the workspace.</li></ul><p> </p><p><strong>Matfiles with L-curve data and model structures.</strong></p><p>The results of our L-curve analyses and our actual inversion results are stored in matfiles. We performed 21 experiments shown in the paper; for each one we performed an independent L-curve analysis using 25 individual inversions, for a total of 525 inversions. However, for this data release we simplify matters by only presenting 3 inversions per experiment, corresponding to the best regularization value (LambdaBest) and the maximum and minimum acceptable regularization values (LambdaMax and LambdaMin). In addition, for each experiment we also provide an LCurveFile that summarizes the L-curve analysis but does not contain any actual model results. In total, we present 84 matfiles in this data release.</p><p><strong>Naming convention:</strong></p><p>All matfiles presented here have the following naming convention:</p><p>Mesh#_eqn_m#_Ntype_LambdaType.mat</p><ul><li>Mesh#: this represents the mesh on which the inversions were performed, ranging from Mesh1 (highest resolution) to Mesh10 (lowest resolution).</li><li>eqn: this represents the type of equations solved in the inversion. Values are "SSA" or "HO".</li><li>m#: exponent in the sliding law. Values are m1, m3, and m5.</li><li>Ntype: effective pressure source in the sliding law. Values are "noN" (ie, Weertman sliding), "Nop", "Nopc", and "Ncuas".</li><li>LambdaType: values of this string are "LCurveFile" (for the file summarizing the whole L-curve experiment), "LambdaMin", "LambdaBest", and "LambdaMax".</li></ul><p><strong>Variables in the model files:</strong></p><p>Every file ending with "LambdaMin", "LambdaBest", or "LambdaMax" is a model file containing the same set of variables. Those variables are:</p><ul><li>md. This is a a model structure variable usable by any ISSM installation. Note that if you do not have ISSM installed on your machine, Matlab will not recognize class "model" and you will not be able to load this variable. The results of the inversion are stored in md.results.StressbalanceSolution. Other important things for the inversion, such as cost functions, cost function coefficients, and velocity observations, are stored in md.inversion. Note that the process of normalizing cost function components described in the paper was performed in practice by manipulating values of md.inversion.cost_function_coefficients. Other important fields of the model structure that might be relevant are md.mesh (describing the numerical mesh), md.geometry (ice sheet geometry), md.mask (ice and ocean masks), md.friction (containing the basal sliding law, including the slip exponent and effective pressure field), md.materials (containing material properties, including rheology), md.stressbalance (controlling the stress balance solution), and md.solvers and md.toolkits (numerical solvers). In addition, users should note that md.cluster is class "ollie", which is a custom class made for AWI's old HPC setup. This will produce a warning that the class of md.cluster is unrecognized if you load md on another computer, and you will need to replace md.cluster with something appropriate to your own setup if you want to use md in the "solve()" command. Also note that md.miscellaneous.dummy contains a copy of the variable "GoodData_obs_vertices", a data quality metric produced by the script ISSMInversion_v3.m. Finally, users should note that md.miscellaneous.name is still set to the original file name we used internally, not the cleaner names we have used for this release.</li><li>MyInversionParameters. This is a structure containing the parameters used by ISSMInversion_v3.m to set up and L-curve and inverse model.</li><li>oldfilename. This is the original file name that we used when we generated these results internally. The cleaner file names you see here were created by the script OrganizeInversionsForRelease_v2.m</li><li>thislambda. The regularization lambda value used for this inversion.</li></ul><p><strong>Variables in the L-curve files:</strong></p><p>The files ending in "LCurveFiles" contain data summarizing the results of each L-curve experiment. Those variables are:</p><ul><li>Lambda. This variable contains the 25 values of the regularization parameter lambda tested in the L-curve. Size: [25,1].</li><li>DataCost, RegularizationCost. These two variables contain the values of J_obs and J_reg for each of the 25 inversions in the L-curve. Size: [25,1].</li><li>IsOutlier. This variable identifies whether any of the 25 inversions have been identified as an outlier and excluded from the curve-fitting process. Size: [25,1].</li><li>LambdaWavelengths. This variable contains all 50 of the smoothing wavelengths tested during the curve-fitting process. Note that these are wavelengths in log(lambda) space. Size: [1,50].</li><li>LogLambda_curves. This variable contains log(lambda), interpolated to fine spacing. All variables on the smooth curves are defined on a sampling defined by this variable. Size: [1000,1].</li><li>LogDataCost_curves, LogRegCost_curves. These variables contain all of the candidate smoothed curves of log(J_obs) and log(J_reg). Size: [1000,50].</li><li>CurvatureVariance_curves, ScatterVariance_curves. Variance measures (Eqs A1 and A2 in the paper) used to quantify how close the smooth curves are to fitting the 25 inversion data points (ScatterVariance) and how much variability the smooth curves have (CurvatureVariance). Size: [1,50].</li><li>TotalVariance_curves. Total variance metric used to select optimal smoothing wavelength. The optimal smoothing wavelength is defined by the minimum of this variable. Size: [1,50].</li><li>LogDataCost_bestcurve, LogRegCost_bestcurve. These variables contain the final best smoothed curve of log(J_obs) and log(J_reg). Size: [1000,1].</li><li>LogDataGradient_bestcurve, LogRegGradient_bestcurve. These variables contain the first derivatives, d(log(J))/d(log(lambda)), for both J_obs and J_reg, computed for the best smoothed curve. Note that their size has been reduced by 1 relative to the original curves (these values should be placed at the midpoints between adjacent samples in LogLambda_curves). Size: [999,1].</li><li>LogDataCurvature_bestcurve, LogRegCurvature_bestcurve. These variables contain the second derivatives, d^2(log(J))/d(log(lambda))^2, for both J_obs and J_reg, computed for the best smoothed curve. Note that their size has been reduced by 2 relative to the original curves (these values should be placed at the samples in LogLambda_curves, excluding the first and last samples). Size: [998,1].</li><li>TotalCurvature_bestcurve. This variable corresponds to the sum LogDataCurvature_bestcurve+LogRegCurvature_bestcurve. The best lambda value is the peak of this variable and the min/max acceptable values are defined where this variable drops to half of its maximum value. Note that, while the fine lambda sampling of the smoothed curve allows us to define these lambda values with some precision, we still need to select models from the original 25 lambda values, which have much coarser resolution in log(lambda) space. The "LambdaBest" model is selected to be the model that is closest to the fine-resolution lambdabest in log(lambda) space, while the LambdaMin and LambdaMax models are selected to be the first and last models inside those thresholds. Size: [998,1].</li><li>bestwavelength, bestwavelengthind. These variables describe the best smoothing wavelength (wrt log(lambda)) used to generate the best smooth curve. bestwavelengthind is the index of this wavelength within LambdaWavelengths. Size: [1,1].</li><li>bestlambda, bestlambda_minallowable, bestlambda_maxallowable. These three variables represent the continuously determined best, min, and max lambda values. Size: [1,1].</li><li>bestlambdaind, bestlambdaind_minallowable, bestlambdaind_maxallowable. As above, but these represent the index of the lambda values within LogLambda_curves. Size: [1,1].</li><li>bestmodelind, bestmodelind_minallowable, bestmodelind_maxallowable. The index of the models selected to represent the corresponding best lambda values. Note that for the bestlambda, this is the model with log(lambda) closest to the best value, but for the min/max acceptable, this is the first or last model within the relevant bound. Indices refer to the list of 25 models in each L-curve. Size: [1,1].</li><li>bestlambda_loguncertainty. The error bar around the best lambda value in log(lambda) space. This is equal to half of bestwavelength. The corresponding uncertainty ratio on a linear scale is exp(bestlambda_loguncertainty). Note that this same uncertainty value also applies to minimum and maximum acceptable lambda. Size: [1,1].</li><li>MyInversionParameters. A structure containing the parameters used by ISSMInversion_v3.m to set up and the L-curve and inversions.</li><li>LCurveFittingParameters. A structure containing the parameters used by ISSMInversion_v3.m to perform the curve-fitting analysis.</li><li>oldfilename. The original file name of this L-curve in our internal system before we gave the files neater names for public release.</li></ul><p> </p>
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OpenNeuro
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