Verde v1.0.0: Processing and gridding spatial data using Green's functions

2018-09-13T00:00:00Z (GMT) by Uieda, Leonardo Hoese, David
<p>Verde is a Python library for processing spatial data (bathymetry, geophysics surveys, etc) and interpolating it on regular grids (i.e., <em>gridding</em>).</p> <p>Most gridding methods in Verde use a Green's functions approach. A linear model is estimated based on the input data and then used to predict data on a regular grid (or in a scatter, a profile, as derivatives). The models are Green's functions from (mostly) elastic deformation theory. This approach is very similar to <em>machine learning</em> so we implement gridder classes that are similar to <a href="">scikit-learn</a> regression classes. The API is not 100% compatible but it should look familiar to those with some scikit-learn experience.</p> <p>Advantages of using Green's functions include:</p> <ul> <li>Easily apply <strong>weights</strong> to data points. This is a linear least-squares problem.</li> <li>Perform <strong>model selection</strong> using established machine learning techniques, like k-fold or holdout cross-validation.</li> <li>The estimated model can be <strong>easily stored</strong> for later use, like spherical-harmonic coefficients are used in gravimetry.</li> </ul> <p>The main disadvantage is the heavy memory and processing time requirement (it's a linear regression problem). So it's not recommended for gridding large datasets (> 10,000 points), though it will depend on how much RAM you have available.</p>