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) along the vertical axis (depth with respect to datum) at a single point (geographic lat/lon).

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 `LayeredData`	GeoST native format. Result of to_parquet or to_csv exports
GeoST CPT parquet or csv	`read_cpt_table`	`CptCollection` or `DiscreteData`	GeoST native format. Result of to_parquet or to_csv exports
NLOG excel export	`read_nlog_cores`	`BoreholeCollection` or `LayeredData`	Reader for NLOG deep cores, see here
UU LLG cores	`read_uullg_tables`	`BoreholeCollection` or `LayeredData`	Reader for csv distribution of Utrecht University student boreholes
BORIS XML	`read_xml_boris`	`BoreholeCollection` or `LayeredData`	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'>

PointHeader instance containing 6 objects
                nr                          crs  surface vertical_datum  \
BHR000000085497  urn:ogc:def:crs:EPSG::28992     1.99            NAP   
BHR000000114014  urn:ogc:def:crs:EPSG::28992     2.05            NAP   
BHR000000120513  urn:ogc:def:crs:EPSG::28992     2.01            NAP   
BHR000000160549  urn:ogc:def:crs:EPSG::28992     2.03            NAP   
BHR000000206176  urn:ogc:def:crs:EPSG::28992     1.89            NAP   
BHR000000247842  urn:ogc:def:crs:EPSG::28992     2.12            NAP   

   begin_depth   end  ghg   glg           landuse         x         y  \
        0.0  0.49  0.5  None  graslandBlijvend  141671.0  455122.0   
        0.0  0.55  0.5  None  graslandBlijvend  141523.0  455073.0   
        0.0  0.51  0.5  None  graslandBlijvend  141580.0  455167.0   
        0.0  0.53  0.6  None  graslandBlijvend  141622.0  455034.0   
        0.0  0.39  0.3  None  graslandBlijvend  141522.0  455275.0   
        0.0  0.62  0.4  None  graslandBlijvend  141642.0  455278.0   

                geometry  
POINT (141671 455122)  
POINT (141523 455073)  
POINT (141580 455167)  
POINT (141622 455034)  
POINT (141522 455275)  
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)