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      Weighted Joint Density

      This document details Datarock’s product Weighted Joint Density (wJd).

      WJd was developed by Palmström et al with the aim of better characterising the degree of jointing and the block size than other common methods. The method is based on the understanding that joints perpendicular to a bore hole will be more frequently intersected than other joints.

      Literature

      This product is based on the following literature:

       

      Title

      Author

      Year

      The weighted joint density method leads to improved characterization of jointing

      A Palmström et al

      1996
       

      Dependent Models

      The outputs of the following models are used:

       

      Model Name

      Model Type

      Fracture Detection and Classification

      Object Detection

      Drillers Break

      Object Detection

      Fracture Mask

      Instance Segmentation
       

      Data Processing

      The following steps are taken to determine the Weighted Joint Density of a given interval:

      1. Take the output of the fracture detection model, “simple” classes only

      2. Run a segmentation model to extract the fracture profile

      wjs-1

      3. Measure the angle of each fracture with respect to the core axis

      wjs-2

      4. For a given interval, calculate the Weighted Joint Density

      wjs-3

      where

      L = length of the measured section along the core

      δ = the intersection angle, the angle between the drill core and the individual joint

      This method provides a more nuanced understanding of joint density, leading to potentially more accurate characterisation of the rock mass. The image below shows an example of measured Weighted Joint Density along side row imagery.

      wjs-4

      Further Information

      The Weighted Joint Density quantifies the joint density in rock masses, adjusted for joint orientation:

      • Variability: It shows the distribution of joints, indicating rock mass strength and stability.

      • Engineering Impact: High wJd values may signal potential weak zones, influencing design strategies.

      • Hydrogeology: Similar to engineering impact, wJd can indicate permeability and fluid flow, important for groundwater management and resource extraction.

      In summary, wJd can be used as a key factor in geotechnical and engineering analyses, affecting project safety and effectiveness.

      Product Configuration Options

      There are no configuration aspects to this product.

      Output Data

      Default interval length: 1.0m

      Customisable interval available: Yes.

      User Data

      User data may be provided to the Datarock team via csv in the following format:

      ·       HoleID_sampling_intervals_WJD.csv

      CSV file to contain the following headers:

       

      File Header

      Description

      depth_from

      Start of interval

      depth_to

      End of interval
       

      Data Output

      Results from this product is delivered in a batch nature.

      Integration of the required technologies into Datarock production is ongoing.

      The available CSV files include the following:

      • ProjectID_HoleID_WJD_3m.csv

      • ProjectID_HoleID_WJD_by_user_intervals.csv

      Both CSVs contain the following headers:

       

      File Header

      Description

      hole_id

      Customer’s Hole ID

      depth_from_m

      Start of interval (metres)

      depth_to_m

      End of interval (metres)

      depth_from_ft*

      Start of interval (feet)

      depth_to_ft*

      End of interval (feet)

      wjd

      Weighted joint density

      wjd_measurable

      Weighted joint density using only “measurable” fractures
       

      *Only included if project depths are in feet.

      Product Limitations

       

      Limitations

      Comments

      Dimensional Limitation

      The wJd method relies on measurements taken from drill cores or scanlines, which are essentially one-dimensional. This can limit the accuracy of joint characterization because it may not capture the true three-dimensional spatial distribution of joints within the rock mass.

      Sampling Bias

      The data derived from wJd is dependent on the orientation and location of the borehole or scanline relative to the joint sets. If the sampling is not representative of the entire rock mass, it could lead to skewed results.

      Impact of Unseen Joints

      There could be joints that are not intersecting the core or scanline but could be critical for the stability of the rock mass. The wJd may underestimate the density and impact of such joints.

      Technological Limitations

      The accuracy of wJd is dependent on the technology used for measuring and analyzing the rock joints. Imperfections in imaging or data processing can lead to errors in the final analysis.
       

      Document Version

       

      Version

      Date

      Author

      Rationale

      1.0

      19 Dec 2023

      L Yanez

      Initial release