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Synthetic geology data set
For drill hole data
MINERAL RESOURCES FLAGSHIP
Presentation title | Presenter name2 |2 |
UNKNOWN
real geology
Rocks are made up of collections of minerals
(with specific composition and texture).
Rocks are affected by structures.
SAMPLE & MEASURE
chemical and physical
properties
Measurement values depend on geology,
measurement type, errors, and sampling
strategy. e.g. XRF, XRD, hyperspectral, images
MODEL
geology model
Algorithms performed on the data provide
higher level geological information from
which we can create the geological model.
e.g. primary rock type, alteration, structures
PREDICT
new information for
decision making
Algorithms predict useful information for
minerals exploration and mining. e.g. predict
mineralisation and rock properties
Real World Scenario
WORKFLOW How good are our
intepretations, models
and predictions?
Presentation title | Presenter name3 |
Presentation title | Presenter name
3 |
UNKNOWN
real geology
Rocks are made up of collections of minerals
(with specific composition and texture).
Rocks are affected by structures.
SAMPLE & MEASURE
chemical and physical
properties
Measurement values depend on geology,
measurement type, errors, and sampling
strategy. e.g. XRF, XRD, hyperspectral, images
MODEL
geology model
Algorithms performed on the data provide
higher level geological information from
which we can create the geological model.
e.g. primary rock type, alteration, structures
PREDICT
new information for
decision making
Algorithms predict useful information for
minerals exploration and mining. e.g. predict
mineralisation and rock properties
KNOWN
synthetic geology
SAMPLE &
MEASURE
INTEPRET
and
MODEL
PREDICT
simulated
Real World Synthetic
WORKFLOW
TESTMODELS&PREDICTIONS
How good are
our models and
predictions?
“GeoLena”
reactive transport
modelling
geostatistical
simulation
Real World
How to synthesize?
geologist’s interpretation +
interpolation
deformation and fluid
flow modelling
How do we collaborate? A web platform?
End product:
• library of synthetic data models
• simulated
drilling/sampling/measuring
coarse scale
(mesh?)
fine scale
(continuum?)
3D
geological
domains
process
modelling
geostatistical
parameters
3D static
model
conditioning
data
mineralogy
chemical &
physical
properties
more
parameters
synthetic
drill hole
measurements
sensor
parameters +
noise
3D geology,
geological process
simulation
geochemistry,
rock physics
sensor physics
and analytics
mineralogy,
geochemistry,
geostatistics
Expertise
Consistency:
• across scales
• across measurement methods

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Synthetic geology dataset - June Hill CSIRO

  • 1. Synthetic geology data set For drill hole data MINERAL RESOURCES FLAGSHIP
  • 2. Presentation title | Presenter name2 |2 | UNKNOWN real geology Rocks are made up of collections of minerals (with specific composition and texture). Rocks are affected by structures. SAMPLE & MEASURE chemical and physical properties Measurement values depend on geology, measurement type, errors, and sampling strategy. e.g. XRF, XRD, hyperspectral, images MODEL geology model Algorithms performed on the data provide higher level geological information from which we can create the geological model. e.g. primary rock type, alteration, structures PREDICT new information for decision making Algorithms predict useful information for minerals exploration and mining. e.g. predict mineralisation and rock properties Real World Scenario WORKFLOW How good are our intepretations, models and predictions?
  • 3. Presentation title | Presenter name3 | Presentation title | Presenter name 3 | UNKNOWN real geology Rocks are made up of collections of minerals (with specific composition and texture). Rocks are affected by structures. SAMPLE & MEASURE chemical and physical properties Measurement values depend on geology, measurement type, errors, and sampling strategy. e.g. XRF, XRD, hyperspectral, images MODEL geology model Algorithms performed on the data provide higher level geological information from which we can create the geological model. e.g. primary rock type, alteration, structures PREDICT new information for decision making Algorithms predict useful information for minerals exploration and mining. e.g. predict mineralisation and rock properties KNOWN synthetic geology SAMPLE & MEASURE INTEPRET and MODEL PREDICT simulated Real World Synthetic WORKFLOW TESTMODELS&PREDICTIONS How good are our models and predictions? “GeoLena”
  • 4. reactive transport modelling geostatistical simulation Real World How to synthesize? geologist’s interpretation + interpolation deformation and fluid flow modelling
  • 5. How do we collaborate? A web platform? End product: • library of synthetic data models • simulated drilling/sampling/measuring
  • 6. coarse scale (mesh?) fine scale (continuum?) 3D geological domains process modelling geostatistical parameters 3D static model conditioning data mineralogy chemical & physical properties more parameters synthetic drill hole measurements sensor parameters + noise 3D geology, geological process simulation geochemistry, rock physics sensor physics and analytics mineralogy, geochemistry, geostatistics Expertise Consistency: • across scales • across measurement methods