Introduction: How to Extract Australian NVCL Borehole Data
1. Call the ‘param_builder’ function, fill it with parameters
from nvcl_kit.reader import NVCLReader
from nvcl_kit.param_builder import param_builder
from nvcl_kit.constants import Scalar
from nvcl_kit.generators import gen_scalar_by_depth
# Assemble parameters
# First parameter is state or territory name, one of: 'nsw', 'tas', 'vic', 'qld', 'nt', 'sa', 'wa'
# Other parameters are optional:
# bbox: 2D bounding box in EPSG:4326, only boreholes within box are retrieved
# default {"west": -180.0,"south": -90.0,"east": 180.0,"north": 0.0})
# polygon: 2D 'shapely.geometry.LinearRing' object, only boreholes within this ring are retrieved
# borehole_crs: CRS string, default "EPSG:4326"
# wfs_version: WFS version string, default "1.1.0"
# depths: Tuple of range of depths (min,max) [metres]
# wfs_url: URL of WFS service, GeoSciML V4.1 BoreholeView
# nvcl_url: URL of NVCL service
# max_boreholes: Maximum number of boreholes to retrieve. If < 1 then all boreholes are loaded
# default 0
param = param_builder('nsw', max_boreholes=20)
if not param:
print(f"Cannot build parameters: {param}")
2. Create ‘NVCLReader’ class, check if ‘wfs’ is not ‘None’ to see if this instance initialised correctly properly
# Instantiate class and search for boreholes
reader = NVCLReader(param)
if not reader.wfs:
print("ERROR!")
3. Call get_feature_list() to get list of WFS borehole data for NVCL boreholes or use ‘filter_feat_list() to narrow down the list
# Returns a list of python 'SimpleNamespace' objects (from 'types' module)
# Each object has fields from GeoSciML v4.1 BoreholeView
# accessed using '.' notation e.g. 'bh_list[0].name'
bh_list = reader.get_feature_list()
# Feature lists can be filtered by name and other attributes
feat_list = reader.filter_feat_list(name='BRD005')
print(feat_list)
4. Call get_nvcl_id_list() to get a list of NVCL borehole ids
nvcl_id_list = reader.get_nvcl_id_list()
5. Using an NVCL borehole id from previous step, call get_imagelog_data() to get the NVCL log ids
# Get list of NVCL log ids
nvcl_id_list = reader.get_nvcl_id_list()
# Get NVCL log id for first borehole in list
nvcl_id = nvcl_id_list[0]
# Get image log data for first borehole
imagelog_data_list = reader.get_imagelog_data(nvcl_id)
for ild in imagelog_data_list:
print(ild.log_id,
ild.log_name,
ild.log_type,
ild.algorithmout_id)
6. Using image log data, call gen_scalar_by_depth() to get borehole scalar data
# The names of NVCL scalar classes have 3 parts; first part is class grouping type,
# second is the TSA mineral matching technique, third part is wavelength:
# 1. Min1,2,3 = 1st, 2nd, 3rd most common mineral type
# OR Grp1,2,3 = 1st, 2nd, 3rd most common group of minerals
# 2. uTSA = user, dTSA = domaining, sTSA = system
# 3. V = visible light, S = shortwave IR, T = thermal IR
#
# These combine to give us a class name such as 'Grp1 uTSAS'
#
# Here we extract data for 'Grp1 uTSAS' using 'Scalar' class
#
# GEN_SCALAR_BY_DEPTH
for nvcl_id, log_id, sca_list in gen_scalar_by_depth(reader, scalar_class=Scalar.Grp1_uTSAS, log_type='1', top_n=4):
for depth in sca_list:
for meas in sca_list[depth]:
print(f"{nvcl_id} {log_id} @ {depth} metres: class={meas.className}, abundance={meas.classCount}, mineral={meas.classText}, colour={meas.colour}")
print()
7. Using the NVCL id from Step 5, you can also call get_spectrallog_data() and get_profilometer_data()
spectrallog_data_list = reader.get_spectrallog_data(nvcl_id)
for sld in spectrallog_data_list:
print(sld.log_id,
sld.log_name,
sld.wavelength_units,
sld.sample_count,
sld.script,
sld.script_raw,
sld.wavelengths)
profilometer_data_list = reader.get_profilometer_data(nvcl_id)
for pdl in profilometer_data_list:
print(pdl.log_id,
pdl.log_name,
pdl.max_val,
pdl.min_val,
pdl.floats_per_sample,
pdl.sample_count)
8. Option: get a list of dataset ids
datasetid_list = reader.get_datasetid_list(nvcl_id)
9. Option: Get a list of datasets
dataset_list = reader.get_dataset_list(nvcl_id)
for ds in dataset_list:
print(ds.dataset_id,
ds.dataset_name,
ds.borehole_uri,
ds.tray_id,
ds.section_id,
ds.domain_id)
10. Using an element from ‘datasetid_list’ in Step 8 or ‘ds.dataset_id’ from Step 9, can retrieve log data
# Scalar log data
log_list = reader.get_scalar_logs(ds.dataset_id)
for log in log_list:
print(log.log_id,
log.log_name,
log.is_public,
log.log_type,
log.algorithm_id)
# Different types of image log data
ilog_list = reader.get_all_imglogs(ds.dataset_id)
ilog_list = reader.get_mosaic_imglogs(ds.dataset_id)
ilog_list = reader.get_tray_thumb_imglogs(ds.dataset_id)
ilog_list = reader.get_tray_imglogs(ds.dataset_id)
ilog_list = reader.get_imagery_imglogs(ds.dataset_id)
for ilog in ilog_list:
print(ilog.log_id,
ilog.log_name,
ilog.sample_count)
11. Using the scalar log ids, can get scalar data and plots of scalar data
# Scalar data in CSV format
log_id_list = [l.log_id for l in log_list]
data = reader.get_scalar_data(log_id_list)
# Sampled scalar data in JSON (or CSV) format
samples = reader.get_sampled_scalar_data(log.log_id,
outputformat='json',
startdepth=0,
enddepth=2000,
interval=100)
# A data plot in PNG
plot_data = reader.plot_scalar_png(log_id)
# Data plots in HTML, only plots the first 6 log ids
plot_data = reader.plot_scalars_html(log_id_list)
12. Using the image log ids can produce images of NVCL cores
ilog_list = reader.get_mosaic_imglogs(ds.dataset_id)
for ilog in ilog_list:
img = reader.get_mosaic_image(ilog.log_id)
ilog_list = reader.get_tray_thumb_imglogs(ds.dataset_id)
for ilog in ilog_list:
# Either HTML or JPG
img = reader.get_tray_thumb_html(ds.dataset_id, ilog.log_id)
img = reader.get_tray_thumb_jpg(ilog.log_id)
# Use either 'get_tray_thumb_imglogs()' or 'get_tray_imglogs()'
ilog_list = reader.get_tray_thumb_imglogs(ds.dataset_id)
ilog_list = reader.get_tray_imglogs(ds.dataset_id)
for ilog in ilog_list:
depth_list = reader.get_tray_depths(ilog.log_id)
for depth in depth_list:
print(depth.sample_no,
depth.start_value,
depth.end_value)