Unique Classification

This document details Datarock’s Unique Classification products.

Dependent Pipelines

The outputs of the following pipelines are used to determine any Unique Classification:

Pipeline Name

Pipeline Output Type

Image Preparation

Object Detection

Depth Registration

Semantic or Instance Segmentation

Unique Classification

Classification

 

Data Processing

The outputs of the Datarock Unique Classification model when applied over depth registered rows, allow classification labels including dominant label and class probability statistics of these predictions to be calculated. 

Depth-registered rows are cut into 5cm squares ('tiles') using customisable clipping and overlap parameters of the row imagery using the following settings.

  • Clip the top and bottom of each row image by 15%

    • This removes influences from the edge of the tray

  • Overlap the squares by 20%

    • This allows for capturing geological structure and texture at tile boundaries that could otherwise be unaccounted for.

An example of how the squares appear on depth-registered rows is shown below.

uc-1

Below is an image illustrating the difference between a classification model and its potential outputs against an object detection, semantic segmentation (single-class) and instance segmentation (multi-class) model.

uc-2

Detection of Unique Classification classes

Several classification classes can be detected by a Unique Classification model based on either manual labelling of 5cm resolution tile imagery during onboarding, sampling for tiles based on geological logs or via UMAP using the textural and colour differences of these tiles. The latter two can assist in further augmenting the training process.

These annotated tiles are split into training and evaluation datasets and the following image shows a compilation of training tiles for each class of a 10-class lithology classification model.

uc-3

Product Configuration Options

There are no configuration aspects to this product.

Output Intervals

Default interval lengths: raw data is produced at 0.05m scale as well as composited intervals of 0.5m, 1m, 2m, 3m and 5m.

Customisable interval is available: Yes, via uploading an assay or geology logging table to Datarock Customer Success Team.

User Data

Sampling Intervals

User data may be uploaded to the platform via CSV in the following format for customisable intervals (assay or geology logs):

  • HOLEID_sampling_intervals_lithology.csv
  • HOLEID_sampling_intervals_alteration.csv
  • HOLEID_sampling_intervals_mineral.csv

CSV file to contain the following headers:

File Header

Description 

depth_from

Start of interval

depth_to

End of interval

groundtruth

Class name (i.e. lithology class, alteration class, mineral class - depending on the model)

 

Data Output

Results from this class of model can be obtained using the Download Square Classification artefacts option from the Actions button in the Model Review tab of Datarock. The available CSV files include the following for the drill hole(s) selected:

  • ProjectID_classificationProduct_square.csv
  • ProjectID_classificationProduct_square_all_classes.csv*
  • ProjectID_classificationProduct_interval.csv
  • ProjectID_classificationProduct_composite_Xm.csv
  • ProjectID_classificationProduct_composite_segment_Xm.csv

  • ProjectID_classificationProduct_composite_user_intervals.csv**

* only available for download off the Platform and not via Public API
** only if sampling intervals have been uploaded to the Platform

These six CSV files contain the following headers:

Composite Data Files

ProjectID_classificationProduct_composite_Xm.csv

ProjectID_classificationProduct_composite_segment_Xm.csv

ProjectID_classificationProduct_composite_user_intervals.csv

Column Header

Description

hole_id

Customer’s Hole ID

depth_from_m

Start of interval (metres)

depth_to_m

End of interval (metres)

 groundtruth

 Logged class name as defined by the   ground_truth column in the uploaded   HOLEID_sampling_intervals_product.csv

dominant_class

The model predicted outcome of most likely/majority class within the selected interval

length_interval

Length of interval as defined by depth_from and depth_to

length_response

Length of core detected as defined by depth_from and depth_to

length_valid

Length of core detected as defined by depth_from and depth_to used for prediction

class name

A certain quantity of columns based on the name and number of trained classes. Each row shows a proportional prediction percentage of each class for that interval (adds up to 100%).

Raw Data Files

ProjectID_classificationProduct_square.csv

ProjectID_classificationProduct_square_all_classes.csv

ProjectID_classificationProduct_square_interval.csv

Column Header

Description

hole_id

Customer’s Hole ID

depth_from_m

Start of tile interval (metres)

depth_to_m

End of tile interval (metres)

 box_number

 Platform-assigned box number reference

class

Class with the highest probability predicted for square interval

probability

Statistical confidence of the model in selecting the most correct class for the square

inference_timestamp

The timestamp of when the model calculated the class

classification_source

What was used to derive the class prediction (model)

edited_by

Name of the author of any edits conducted on the class outcome (if applicable)

version

Model version identifier

class_probability (only available in square_all_classes.csv)

A certain quantity of columns based on the name and number of trained classes. Each row shows the statistical confidence of the model in selecting each class for the square 

 

Product Limitations

 

Limitations

Comments

Reliance of row detection and depth registration

The Unique Classification model is based on predicting geological features within a 5cm tile cut from row imagery. The dependency on the depth registered rows being identified means if a row is missed by the row model during Image Preparation, the subsequent tiles will not have the classification modelled applied.

Training is dependent on what can be seen within a tile image

Datarock’s Unique Classification model relies on classes being predicted using visually identifiable RGB features some of which are too subtle to predict from a photo, in particular if resolution is poor.

Tile imagery during the training process also does not provide geological context for individual squares when sampled at random. For example, two red-coloured tiles could both be predicted as “oxide” but one is from 1m depth in a surface hole (weathered rock and therefore true positive) and the other at 1,000m depth (hematite-oxidation in an IOCG and therefore a false positive). Site-based logging can be used to assist in-class training on these false positives, however, logging is not always at the same resolution as 5cm tiles.

Tiles which fall at the end of the rows may only contain a fraction of rock and thus can cause confusion for the model. To minimise any model confusion, these examples are generally trained as an “Other” class along with tiles that contain solely a core block or empty tray.

Training data must be representative of the whole area classification is to be applied

If new imagery or classification class is introduced to the model, the performance may decline as these examples were not trained during onboarding.

An initial model evaluation will need to be undertaken to see the suitability of the model in particular against any new imagery. Ideally, a new model version is trained to incorporate the new untrained tiles or class.

 

Document Version

 

Version

Date

Author

Rationale

1

11 August 2023

C Brown

Initial release.

2

03 January 2025

M Fracchia

User Data update

3

07 January 2025

N Pittaway

Data Output update