Subsurface data types and utilities
Project description
subsurface
DataHub for geoscientific data in Python. Two main purposes:
-
Unify geometric data into data objects (using numpy arrays as memory representation) that all the packages of the stack understand
-
Basic interactions with those data objects:
- Write/Read
- Categorized/Meta data
- Visualization
Data Levels:
The difference between data levels is not which data they stored but which data they parse and understand. The rationale for this is to be able to pass along any object along while keeping the I/O in subsurface.
Human
\=================================/'
\===============================/ ' \
\==========geo_format=========/ ' \ -> Additional context/meta information about the data
\===========================/' ' \
\=======geo_object========/ ' ' \ -> Elements that represent some
\=======================/ ' ' / geological concept. E.g: faults, seismic
\=====================/' ' ' ' /
\======element======/' ' ' ' / -> type of geometric object: PointSet,
\=================/' ' ' ' / TriSurf, LineSet, Tetramesh
\=primary_struct/ '' / - > Set of arrays that define a geometric object:
\=============/ ' ' / e.g. *StructuredData* **UnstructuredData**
\============/'' /
\DF/Xarray/ ' '/ -> Label numpy.arrays
\=======/'' /
\array/' / -> Memory allocation
\===/ /
\=//
'
Computer
Installation
The project is in pre-alpha and is not yet ready to be used.
Changes Log
Update 13.06.2020
We are changing things. Help us figure it out!
Original statement
The goal of this project is to support other subsurface geoscience and
engineering projects with a set of classes for common subsurface data entities,
such as seismic and GPR datasets, log curves, and so on. The current plan is to
use xarray
under the hood, with pint
for units support and cartopy
for CRS and map support.
It's early days, everything might change. Help us figure it out!
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