Supplements for Inferring Long-Term Tectonic Uplift Patterns from Bayesian Inversion of Fluvially-Incised Landscapes paper

<p><strong>Data and File Organization:</strong></p> <ol> <li><strong>Natural Landscapes (DEM):</strong> <ul> <li>Look for <code>.tif</code> files containing DEMs of natural landscapes. These files are in latitude-longitude coordinates; convert them to UTM if needed.</li> </ul> </li> <li><strong>Synthetic Landscapes (DEM):</strong> <ul> <li>DEM files for synthetic landscapes, ready for use in inversion schemes, are labeled with a <code>syn_</code> prefix.</li> </ul> </li> <li><strong>Climatic Data:</strong> <ul> <li>Climatic data for the Himalayas is available in <code>climate_data_him.zip</code>.</li> </ul> </li> </ol> <p><strong>Running the Code:</strong></p> <ol> <li> <p><strong>Loading DEMs:</strong></p> <ul> <li>Use the <code>loadDEM</code> package to load your DEM file.</li> <li>Specify <code>Z0</code> and <code>A0</code> values, then plot the landscape and <code>basinID</code> for reference.</li> </ul> </li> <li> <p><strong>Identifying Basins of Interest:</strong></p> <ul> <li>Determine which <code>basinID</code>s are of interest, then save them as forward objects. The functions for this process are available within the relevant packages.</li> </ul> </li> <li> <p><strong>Loading the Forward Model:</strong></p> <ul> <li>Load the forward model from the saved file using the appropriate function in the <code>frd</code> package.</li> <li>Choose the number of knots and specify if you prefer a 1D or 2D inversion.</li> </ul> </li> <li> <p><strong>Running and Plotting Inversion Results:</strong></p> <ul> <li>After running the inversion, view results in <code>inversion.step</code>.</li> <li>Plot these results using the plotting functions in the <code>frdplotting</code> package.</li> </ul> </li> </ol> <p>&nbsp;</p> <p>&nbsp;</p> <p><strong>Setup and Installation:</strong></p> <ul> <li>Install the package <code>scabbard</code> with: <div> <div>&nbsp;</div> <div><code>pip install pyscabbard </code></div> </div> </li> <li>All other Python dependencies are standard and can be installed via <code>pip</code> or <code>conda</code> as needed.</li> </ul>