Reading data#

GeoST offer various functions to read and parse data to GeoST objects. Generally speaking, data can either be loaded from (local) files or is requested from a service like the BRO REST-API. The raw data is then parsed to a GeoST data object such as a BoreholeCollection or CptCollection.

Supported subsurface data#

GeoST supports data coming from several data sources and file formats. The tables below list the currently supported data sources, associated reader functions and resulting GeoST objects.

Subsurface data#

By subsurface point data we mean layered data (e.g. boreholes) or discrete/measurement data (e.g. CPT, well logs) at a single x,y- or lat,lon-location (see Data structures for a more detailed description).

File format/data service	Read function	Returned GeoST object	Description
BHR-G	`read_bhrg`	`BoreholeCollection`	(BRO) Geological boreholes from xml
BHR-GT	`read_bhrgt`	`BoreholeCollection`	(BRO) Geotechnical boreholes from xml
BHR-P	`read_bhrp`	`BoreholeCollection`	(BRO) Pedological boreholes from xml
CPT	`read_cpt` `read_gef_cpts`	`CptCollection`	(BRO) Cone Penetration Tests from xml or gef
SFR	`read_sfr`	`BoreholeCollection`	(BRO) Pedological soilprofile descriptions from xml
BRO REST-API	`bro_api_read`	`BoreholeCollection` or `CptCollection`	BRO BHR-G, BHR-GT, BHR-P, CPT or SFR objects
GeoST borehole parquet or csv	`read_borehole_table`	`BoreholeCollection` or `DataFrame`	GeoST native format. Result of to_parquet or to_csv exports
GeoST CPT parquet or csv	`read_cpt_table`	`CptCollection` or `DataFrame`	GeoST native format. Result of to_parquet or to_csv exports
NLOG excel export	`read_nlog_cores`	`BoreholeCollection` or `DataFrame`	Reader for NLOG deep cores, see here
UU LLG cores	`read_uullg_tables`	`BoreholeCollection` or `DataFrame`	Reader for csv distribution of Utrecht University student boreholes
BORIS XML	`read_xml_boris`	`BoreholeCollection` or `DataFrame`	Reader for XML exports of the BORIS borehole description software

Subsurface model data#

By subsurface model data we mean voxel models (such as GeoTOP) or layer models (such as REGIS).

File format/data service	Read function	Returned GeoST object	Description
Generic Voxelmodel	`VoxelModel`	`VoxelModel`	Generic reader for a voxelmodel presented in NetCDF format (readable as xarray dataset)
GeoTOP NetCDF	`GeoTop.from_netcdf`	`VoxelModel`	Reader for GeoTOP NetCDF distribution
GeoTOP OpenDAP	`GeoTop.from_opendap`	`VoxelModel`	Reader for GeoTOP OpenDAP distribution

Data reading examples#

import geost

# Read a few BRO soil cores in a small area
boreholes = geost.bro_api_read("BHR-P", bbox=(141_470, 455_000, 141_700, 455_300))

print(type(boreholes))
boreholes.header

<class 'geost.base.BoreholeCollection'>

	nr	crs	surface	vertical_datum	begin_depth	end	ghg	glg	landuse	x	y	geometry
0	BHR000000085497	urn:ogc:def:crs:EPSG::28992	1.99	NAP	0.0	0.49	0.5	None	graslandBlijvend	141671.0	455122.0	POINT (141671 455122)
1	BHR000000114014	urn:ogc:def:crs:EPSG::28992	2.05	NAP	0.0	0.55	0.5	None	graslandBlijvend	141523.0	455073.0	POINT (141523 455073)
2	BHR000000120513	urn:ogc:def:crs:EPSG::28992	2.01	NAP	0.0	0.51	0.5	None	graslandBlijvend	141580.0	455167.0	POINT (141580 455167)
3	BHR000000160549	urn:ogc:def:crs:EPSG::28992	2.03	NAP	0.0	0.53	0.6	None	graslandBlijvend	141622.0	455034.0	POINT (141622 455034)
4	BHR000000206176	urn:ogc:def:crs:EPSG::28992	1.89	NAP	0.0	0.39	0.3	None	graslandBlijvend	141522.0	455275.0	POINT (141522 455275)
5	BHR000000247842	urn:ogc:def:crs:EPSG::28992	2.12	NAP	0.0	0.62	0.4	None	graslandBlijvend	141642.0	455278.0	POINT (141642 455278)

from geost.bro import GeoTop

# Get corresponding voxels of the GeoTOP model
geotop = GeoTop.from_opendap(bbox=(141_470, 455_000, 141_700, 455_300))

geotop

GeoTop
Data variables:
    strat    (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    lithok   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_1   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_2   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_3   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_4   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_5   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_6   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_7   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_8   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    kans_9   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    onz_lk   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
    onz_ls   (y, x, z) float32 25kB dask.array<chunksize=(5, 4, 76), meta=np.ndarray>
Dimensions: {'y': 5, 'x': 4, 'z': 313}
Resolution (y, x, z): (100.0, 100.0, 0.5)