Here is the second of several animated models of bioturbation. It simulates the effect of mixing of a 5 cm thick layer by benthic organisms on a single 1 cm thick layer of yellow particles, followed by layers with 100% red particles. Continue reading “MATLAB-Based Simulation of Bioturbation, Part 4”
Here is the first of several animated models of bioturbation. It simulates the effect of mixing of a 5 cm thick layer by benthic organisms on a single 1 cm thick layer of red particles. Continue reading “MATLAB-Based Simulation of Bioturbation, Part 3”
Bioturbation (or benthic mixing) causes significant distortions in marine stable isotope signals and other palaeoceanographic records. In an earlier post I introduced a MATLAB-based model to study the effect of bioturbation on isotopic signals from stratigraphic carriers such as foraminifera. This post will demonstrate how to create a publishable figure showing the uppermost layers of a sediment sequence affected by bioturbation. The following posts will introduce MATLAB-based animations of the benthic mixing. Continue reading “MATLAB-Based Simulation of Bioturbation, Part 2”
The principal component analysis (PCA) can be used to decipher the statistically independent contribution of the source rocks to the sediment compositions in the Santa Maria Basin, NW Argentine Andes. Continue reading “PCA-Based Provenance Analysis of Varved Sediments from the NW Argentine Andes”
Principal component analysis (PCA) detects linear dependencies between variables and replaces groups of linear correlated variables with new, uncorrelated variables referred to as the principal components (PCs). The MATLAB function pca helps to perform such an linear unmixing experiment. Continue reading “Linear Unmixing Variables Using the PCA with MATLAB”
Lisa Kempler of MathWorks created a great compilation of geoscience teaching resources with MATLAB. These resources include published online curriculum materials designed for undergraduate course settings, but they are also applicable to graduate students learning computation for use in geoscience.
The Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) database is an amalgamation of two public domain databases by Paul Wessel (SOEST, University of Hawaii, Honolulu, HI) and Walter Smith (NOAA Laboratory for Satellite Altimetry, Silver Spring, MD) (Wessel and Smith 1996). The format of the GSHHG changed and therefore the recipes contained in MRES and MDRES have to be modified. Continue reading “A New MATLAB Script to Process the Global Geography Database (GSHHG)”
Thanks to the great support of MathWorks sponsoring the curriculum development of the new MATLAB/LEGO MINDSTORMS Environmental Remote Sensing Lab we just received eight more boxes of LEGO MINDSTORMS. Together with various devices to acquire typical multispectral, geophysical and geometric data, we cannot wait to use it to teach how geoscientific data is collected, processed and analyzed on the scale of a laboratory. The progress of course development as well as course materials will be provided on this blog.