Geoscience

Tools
Incoming
References

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.

Tools

Pangeo: A community platform for Big Data geoscience
csiro-dcfp/pangeo_hpc: Scripts and advice for running Pangeo with dask-jobqueue on (some) Australian HPC systems
Spatio-Temporal Statistics with R

…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.

Incoming

softwareunderground/awesome-open-geoscience: Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome
Noddyverse 1.0.1 (Jessell et al. 2022)
Source code in C to build random 3D geological models, together with example Jupyter Notebook and equivalent mybinder link to perform a filtered sampling of 1 million existring 3D models

References

Baisch, Stefan, and Götz H. R. Bokelmann. 1999. “Spectral Analysis with Incomplete Time Series: An Example from Seismology.” Computers & Geosciences 25 (7): 739–50.

Bárdossy, András. 2006. “Copula-Based Geostatistical Models for Groundwater Quality Parameters.” Water Resources Research 42 (11).

Barnhart, Katherine R, Eric WH Hutton, Gregory E Tucker, Nicole M Gasparini, Erkan Istanbulluoglu, Daniel EJ Hobley, Nathan J Lyons, et al. 2020. “Landlab V2. 0: A Software Package for Earth Surface Dynamics.” Earth Surface Dynamics 8 (2): 379–97.

Basir, Shamsulhaq, and Inanc Senocak. 2022. “Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-Fidelity Data Fusion.” Journal of Computational Physics 463 (August): 111301.

Diggle, Peter, and Paulo J. Ribeiro. 2007. Model-Based Geostatistics. Springer Series in Statistics. New York, NY: Springer.

Gupta, Harsh K., ed. 2021. Encyclopedia of Solid Earth Geophysics. Encyclopedia of Earth Sciences Series. Cham: Springer International Publishing.

Hobley, Daniel EJ, Jordan M Adams, Sai Siddhartha Nudurupati, Eric WH Hutton, Nicole M Gasparini, Erkan Istanbulluoglu, and Gregory E Tucker. 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 5 (1): 21–46.

Hoffimann, Júlio, Maciel Zortea, Breno de Carvalho, and Bianca Zadrozny. 2021. “Geostatistical Learning: Challenges and Opportunities.” Frontiers in Applied Mathematics and Statistics 7.

Hutton, Eric, Katy Barnhart, Dan Hobley, Greg Tucker, Sai Nudurupati, Jordan Adams, Nicole Gasparini, et al. 2020. “Landlab.”

Jessell, Mark, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko. 2022. “Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications.” Earth System Science Data 14 (1): 381–92.

Lewis, Adam, Simon Oliver, Leo Lymburner, Ben Evans, Lesley Wyborn, Norman Mueller, Gregory Raevksi, et al. 2017. “The Australian Geoscience Data Cube — Foundations and Lessons Learned.” Remote Sensing of Environment 202 (December): 276–92.

Matheron, Georges. 1963. “Principles of Geostatistics.” Economic Geology 58 (8): 1246–66.

Peruzzi, Michele, Sudipto Banerjee, and Andrew O. Finley. 2020. “Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains.” Journal of the American Statistical Association 0 (0): 1–14.

Pluch, Philipp. 2007. “Some Theory for the Analysis of Random Fields - With Applications to Geostatistics.” arXiv:math/0701323, January.

Roberts, Dale, Norman Mueller, and Alexis Mcintyre. 2017. “High-Dimensional Pixel Composites From Earth Observation Time Series.” IEEE Transactions on Geoscience and Remote Sensing 55 (11): 6254–64.

Sambridge, Malcolm, Andrew Jackson, and Andrew P Valentine. 2022. “Geophysical Inversion and Optimal Transport.” Geophysical Journal International 231 (1): 172–98.

Scalzo, Richard, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps. 2022. “Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models.” Geoscientific Model Development 15 (9): 3641–62.

Sun, Alexander Y., Hongkyu Yoon, Chung-Yan Shih, and Zhi Zhong. 2021. “Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey.” arXiv:2104.04764 [Physics], April.

Valentine, Andrew P, and Malcolm Sambridge. 2020a. “Gaussian Process Models—I. A Framework for Probabilistic Continuous Inverse Theory.” Geophysical Journal International 220 (3): 1632–47.

———. 2020b. “Gaussian Process Models—II. Lessons for Discrete Inversion.” Geophysical Journal International 220 (3): 1648–56.

Valentine, Andrew, and Malcolm Sambridge. 2022. “Emerging Directions in Geophysical Inversion.” arXiv.

Tools

Incoming

References

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