Geoscience

September 11, 2020 — July 19, 2023

Gaussian
Hilbert space
kernel tricks
spatial
statistics
stochastic processes
time series
Figure 1: Kircher’s model of the seismic systems of the earth

Tools for understanding dynamics of spatial/spatiotemporal processes where those processes are made of rocks.

1 Tools

geostack

…is a toolkit for high performance geospatial processing, modelling and analysis.

Some highlights of Geostack include:

  • Range of programmable geospatial operations based on OpenCL, including map algebra, distance mapping and rasterisation.
  • Data IO for common geospatial types such as geotiff and shapefiles with no dependencies.
  • Implicit handling geospatial alignment and projections, allowing easier coding of geospatial models.
  • Python bindings for interoperability with GDAL/RasterIO/xarray/NetCDF.
  • Built-in computational solvers including level set and network flow models.

More information and build guides are on our wiki.

Geostack can be installed for Python using conda.

gstat does certain R geostats.

landlab (Barnhart et al. 2020; Hobley et al. 2017; Hutton et al. 2020)

… is an open-source Python-language package for numerical modeling of Earth surface dynamics. It contains

  • A gridding engine which represents the model domain. Regular and irregular grids are supported.
  • A library of process components, each of which represents a physical process (e.g., generation of rain, erosion by flowing water). These components have a common interface and can be combined based on a user’s needs.
  • Utilities that support general numerical methods, file input/output, and visualization.

In addition Landlab contains a set of Jupyter notebook tutorials providing an introduction to core concepts and examples of use.

Landlab was designed for disciplines that quantify Earth surface dynamics such as geomorphology, hydrology, glaciology, and stratigraphy. It can also be used in related fields. Scientists who use this type of model often build their own unique model from the ground up, re-coding the basic building blocks of their landscape model rather than taking advantage of codes that have already been written. Landlab saves practitioners from the need for this kind of re-invention by providing standardized components that they can re-use.

Geocomputation with Python

This is the online home of Geocomputation with Python, a book on reproducible geographic data analysis with open source software.

Inspired by the Free and Open Source Software for Geospatial (FOSS4G) movement this is an open source book. Find the code underlying the geocompy project on GitHub, ensuring that the content is reproducible, transparent, and accessible. Making the book open source allows you or anyone else, to interact with the project by opening issues, making typo fixes and more, for the benefit of everyone.

2 Incoming

3 References

Baisch, and Bokelmann. 1999. “Spectral Analysis with Incomplete Time Series: An Example from Seismology.” Computers & Geosciences.
Bárdossy. 2006. “Copula-Based Geostatistical Models for Groundwater Quality Parameters.” Water Resources Research.
Barnhart, Hutton, Tucker, et al. 2020. “Landlab V2. 0: A Software Package for Earth Surface Dynamics.” Earth Surface Dynamics.
Basir, and Senocak. 2022. “Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-Fidelity Data Fusion.” Journal of Computational Physics.
Diggle, and Ribeiro. 2007. Model-Based Geostatistics. Springer Series in Statistics.
Gupta, ed. 2021. Encyclopedia of Solid Earth Geophysics. Encyclopedia of Earth Sciences Series.
Hobley, Adams, Nudurupati, et al. 2017. “Creative Computing with Landlab: An Open-Source Toolkit for Building, Coupling, and Exploring Two-Dimensional Numerical Models of Earth-Surface Dynamics.” Earth Surface Dynamics.
Hoffimann, Zortea, de Carvalho, et al. 2021. “Geostatistical Learning: Challenges and Opportunities.” Frontiers in Applied Mathematics and Statistics.
Hutton, Barnhart, Hobley, et al. 2020. “Landlab.”
Jessell, Guo, Li, et al. 2022. “Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications.” Earth System Science Data.
Lewis, Oliver, Lymburner, et al. 2017. “The Australian Geoscience Data Cube — Foundations and Lessons Learned.” Remote Sensing of Environment.
Matheron. 1963. “Principles of Geostatistics.” Economic Geology.
Peruzzi, Banerjee, and Finley. 2020. “Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains.” Journal of the American Statistical Association.
Pluch. 2007. “Some Theory for the Analysis of Random Fields - With Applications to Geostatistics.” arXiv:math/0701323.
Roberts, Mueller, and Mcintyre. 2017. “High-Dimensional Pixel Composites From Earth Observation Time Series.” IEEE Transactions on Geoscience and Remote Sensing.
Sambridge, Jackson, and Valentine. 2022. “Geophysical Inversion and Optimal Transport.” Geophysical Journal International.
Scalzo, Lindsay, Jessell, et al. 2022. “Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models.” Geoscientific Model Development.
Sun, Yoon, Shih, et al. 2021. “Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey.” arXiv:2104.04764 [Physics].
Valentine, Andrew P, and Sambridge. 2020a. “Gaussian Process Models—I. A Framework for Probabilistic Continuous Inverse Theory.” Geophysical Journal International.
———. 2020b. “Gaussian Process Models—II. Lessons for Discrete Inversion.” Geophysical Journal International.
Valentine, Andrew, and Sambridge. 2022. “Emerging Directions in Geophysical Inversion.”