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17 - Statistical Plots

1.0 Introduction

This tutorial demonstrates the use of statistical plots (e.g., Plot Histogram, Plot Scatter, Plot Cumulative) to fit distributions and perform linear regression on quantitative Grid Data.

Topics Covered in this Tutorial:

  • Grid Data
  • Histogram Plot and Best Fit Distribution
  • Scatter Plot and Regression Line
  • Cumulative Plot

Finished Product:

The finished product of this tutorial can be found in the Tutorial 17 Statistical Plots.dips8 file, located in the Examples > Tutorials folder in your Dips installation folder.

2.0 Model

If you have not already done so, run Dips by double-clicking on the Dips icon in your installation folder. Or from the Start menu, select Programs > Rocscience > Dips > Dips.

If the Dips application window is not already maximized, maximize it now, so that the full screen is available for viewing the model.

Dips comes with several example files installed with the program. These example files can be accessed by selecting File > Recent Folders > Examples Folder from the Dips main menu. This tutorial will use the Orthogonal Sets.dips8 file to demonstrate the basic plotting features of Dips.

  1. Select File > Recent Folders > Example Folder Open Folder Icon from the menu.
  2. Open the Orthogonal Sets.dips8 file. Since we will be using the Orthogonal Sets.dips8 file in other tutorials, save this example file with a new file name without overwriting the original file.
  3. Select File > Save As Save As Icon from the menu.
  4. Enter the file name Tutorial 17 Statistical Plots and Save the file.

You should see the Stereonet Plot View shown in the following figure.

If the example file has been previously opened and saved, the screen may show a different view or plot, since Dips saves the most recent view state when a file is saved
2D Stereonet View
2D Stereonet View showing poles, contours, and three orthogonal sets
  1. Switch to the Grid Data view of the file using the view tabs in the lower left of the window.
Grid Data View
Grid Data View showing 1683 measurements

The file contains 1683 measurements from 47 televiewer linear borehole cores.

The file uses the following columns:

    • The two mandatory Orientation columns
    • A Traverse column
    • A Set column
    • A Distance column
    • Three location X, Y, Z columns
    • A Persistence columns
    • 2 extra columns
    The X, Y, Z columns are computed using the Linear BH Televiewers' Trend, Plunge, and Collar Data (X, Y, Z), and the Distance column from the Grid Data. See the Compute Locations topic for more information on how the X, Y, Z locations of each measurement recorded along a borehole traverse is computed.

    3.0 Statistical Plots

    In Dips, statistical plots (e.g., histogram plot, scatter plot, cumulative plot) can be generated for any quantitative column in the Grid Data.

    3.1 Histogram Plot

    A histogram plot is a graphical representation that displays the distribution of a dataset. It consists of a series of bars, where the height of each bar corresponds to the frequency or relative frequency of data falling within a particular interval or bin. A statistical distribution can also be fitted to the dataset. The following distributions are available in Dips:

      To plot a histogram of Persistence:

      1. Select Statistics > Plot Histogram Plot histogram iconfrom the menu .
      2. In the Histogram Plot Parameters dialog:
        1. Set the Data Type = Persistence.
        2. Enter Number of Intervals = 30.
        3. Select Attach Markers to Distribution checkbox.
        4. Select Best Fit Distribution (Kolmogorov-Smirnov Test) checkbox to fit a distribution to the data.
          Histogram Plot Parameters Dialog
          Histogram Plot Parameters with Dataset set to Persistence
        5. Click OK to generate the histogram plot.

      The Histogram Plot view shows the Relative Frequency on the y-axis and Persistence on the x-axis. The Best Fit Distribution is fit with an Exponential Distribution through the data.

      Histogram Plot
      Histogram Plot of Persistence with Best Fit Distribution (Unfiltered Data)

      The SAMPLED and FIT distributions show the following:

      • Mean = 3.889
      • Standard Deviation = 2.467
      • Min = 1
      • Max = 10
      The Best Fit Distribution can be used to set up the distributions for Random Variables in other programs such as SWedge, UnWedge and RocSlope3 since we have the Distribution Type, Mean, Standard Deviation (if applicable), and (absolute) Minimum and Maximum values. Note that in the listed programs the Relative Minimum and Relative Maximum are used to define the distribution. The Relative Minimum = Mean - Minimum. The Relative Maximum = Maximum - Mean.

      3.2 Scatter Plot

      A scatter plot is a graphical representation that displays the relationship between two variables. A linear regression can be fitted through the dataset to see if there is a correlation between the two variables plotted.

      To plot a scatter plot of Z (Elevation) vs PHIR:

      1. Select Statistics > Plot Scatter plot scatter icon from the menu .
      2. In the Scatter Plot Parameters dialog:
        1. Set the X Axis Dataset = Z (Elevation).
        2. Set the Y Axis Dataset = PHIR.
        3. Select Show Regression Line checkbox to fit a linear regression through the data.
          Scatter Plot Parameters Dialog
          Scatter Plot Parameters dialog with X Axis Dataset set to Z (Elevation) and Y Axis Dataset set to PHIR
        4. Click OK to generate the scatter plot.

      The Scatter Plot view shows the Z (Elevation) values on the x-axis vs. PHIR values on the y-axis. The Regression Line is fit through the data.

      Scatter Plot View
      Scatter Plot View showing Z (Elevation) vs. PHIR (Unfiltered Data)

      The residual friction angle (i.e., PHIR) is loosely negatively correlated with the Z (Elevation) as indicated by the Correlation Coefficient of -0.511. The alpha and beta values represent the y-intercept and slope of regression line, respectively.

      3.3 Cumulative Plot

      A cumulative plot is a graphical representation that shows the cumulative distribution of a dataset.

      To plot a cumulative plot of Processed Dip:

      1. Select Statistics > Plot Cumulative plot cumulative icon from the menu.
      2. In the Cumulative Plot Parameters dialog:
        1. Set the Data Type = Processed Dip.
        2. NOTE: In this example, Orient 1 and Processed Dip are equivalent since all measurements are either Dip/Dip Direction according to the Global Orientation or recorded along a Linear BH Televiewer Traverse with Dip/Dip Direction
        3. Enter Number of Intervals = 30.
        4. Select the Attach Markers to Distribution checkbox.
        5. Cumulative Plot Parameters Dialog
          Cumulative Plot Parameters dialog with Data Type set to Processed Dip
        6. Click OK to generate the cumulative plot.

      The Cumulative Plot view shows the Cumulative Probability values on the y-axis and Processed Dip on the x-axis.

      Cumulative Plot View
      Cumulative Plot of Processed Dip (Unfiltered Data)

      To view a subset of the data, we can apply a filter to the Cumulative Plot View.

      1. From the Filter drop down in the toolbar, select the Bedding filter.

      Only data in the Bedding filter will be used to generate the cumulative plot.

      Cumulative Plot View
      Cumulative Plot of Processed Dip (Bedding)

      The footer at the bottom of the chart indicates that the Current Filter Applied to Data is the Bedding filter, which includes all poles in Set 1.

      The Histogram Plot View, Scatter Plot View, and Cumulative Plot View, like any other view type in Dips, can have filters applied to the Grid Data. See the Filter Data topic for more information about filters.

      This concludes the tutorial.

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