HELP

 

Structure of the MPM Online Tool

 

-          The MPM online tool is a geographic information systems (GIS) modelling tool for modelling of mineral prospectivity in northern Finland.

-          The MPM online tool is composed of GTK open source geospatial datasets and fuzzy logic modelling tools.

-          The input data i.e. raster layers and other GIS dataset helping to localize and assess modelling results are located on the left hand side of the tool under the 'Layers' (Fig. 1). 'Derivatives' are generated from the geological GIS data such as distance to structures and density of structures. The airborne geophysical data consists of magnetic and electromagnetic interpolated measurement data. The geochemical data is interpolated from glacial till assay data (see Data for specifications). Unfortunately, in the current version of the MPM online tool the histogram stretching of the colour scale cannot be performed by the user.

-          The spatial representation of the input and output layers, and other exploration related data layers, can be viewed in the 'Map' (Fig. 1).

-          Fuzzy tools i.e. 'Fuzzy Membership' and 'Fuzzy Overlay' tools are located on the top right corner of the MPM online tool (Fig. 1).

 

Fig. 1. The user interphase of the Mineral Prospectivity Modeler Online Tool.

 

 

-          To produce a prospectivity model, the input data and fuzzy modelling tools are arranged into a geoprocessing model to the 'Model Builder' located on the right hand side of the MPM online tool (Fig. 2).

 

Figure 2. An example 

 

-          'Bookmarks' at the bottom left of the MPM online tool can help to zoom into a focus area.

 

 

Construction of a mineral prospect model in the Model Builder

 

-          The input data and fuzzy tools can be dragged with the left hand mouse button and dropped one dataset or a tool at a time to the Model Builder (Fig. 2). Copy-pasting of the data ellipses and tool rectangles is not possible in the current version of the MPM tool.

-          A raster input and output can be removed by clicking on to the raster ellipse (outlines turn green) and pressing the delete button on the keyboard.

-          An input raster dataset can be connected to a Fuzzy Membership tool by bringing the cursor on top of an ellipse representing the input raster (see Fig. 3). When the cursor turns into a hand icon click onto ellipse with the right mouse button. Keep the right mouse button down and move the cursor onto the Fuzzy Membership tool. Release the mouse button and the input raster layer and the fuzzy tool should be connected with an arrow.

Figure 3. Connect an input layer (ap_resistivity) to the Fuzzy Membership tool by drawing an arrow in the Model Builder. The output of the Fuzzy Membership, in this example case, is raster(15).

 

 

-          For the modelling result to be meaningful each input dataset has to be transformed with Fuzzy Membership tool before Fuzzy Overlay. The fuzzy logic as an expert driven machine learning technique is described in 'Fuzzy logic' and the details of the fuzzy tools at http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/an-overview-of-the-overlay-tools.htm

-          The membership is always scaled between 0 and 1. Thus, if the real minimum membership is more than 0 or the real maximum less than 1, the memberships have to be transformed to the correct range using other tools which are not yet available in the MPM Online Tool.

-          The Fuzzy Membership tool parameters are shortly described in table 1. For more detailed information go to http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/fuzzy-membership.htm and click to see the specifications for each function.

Fuzzy membership tool parameter

Option

Explanation

Fuzzy membership type

Large

The fuzzy membership transformation function 'Large' defines the shape of the fuzzy membership function as an S-shape increasing function. The small values of input data will be close to 0 and large values close to 1.

 

Gaussian

Defines the shape of the fuzzy membership function as an Gaussian bell shaped function. The small and large values of input data will be close to 0 and values close the mid point close to 1.

 

Near

Near function is similar to the Gaussian fuzzy membership function except the Near function has a more narrow spread.

 

Small

Defines the shape of the fuzzy membership function as an S-shape decreasing function. The small values of input data will be close to 1 and large values close to 0.

Mid point

 

Defines the value of the input data with a fuzzy membership of 0.5. The default value of the mid point is mean of the dataset if the mid point field in the tool is left empty. The processing extent is considered when the mean value is calculated.

Spread

 

Defines the spread of a function. For the Large and Small functions the spread values range from 1 to 10, for Gaussian from 0.01 to 1 and for Near from 0.001 to 1. Larger values result in a steeper distribution from the midpoint.

Hedge

None

Defining a hedge increases or decreases the fuzzy membership values which modify the meaning of a fuzzy set. Hedges are useful to help in controlling the criteria or important attributes.

NONE -No hedge is applied. This is the default.

 

Somewhat

SOMEWHAT 'Known as dilation, defined as the square root of the fuzzy membership function. This hedge increases the fuzzy membership functions.

 

Very

VERY -Also known as concentration, defined as the fuzzy membership function squared. This hedge decreases the fuzzy membership functions.

Table 1. Parameters of the Fuzzy Membership functions and their specifications.

 

-          To specify the parameters a Fuzzy Membership tool can be opened by double clicking the Fuzzy Membership rectangle. An additional window opens up to specify the parameters (Fig. 4).

 

Figure 4. The Fuzzy Membership tool. Specify the Fuzzy Membership type, Mid point, Spread and Hedge.

 

 

- Determination of the midpoint is critical for the success of the modelling. In the current version of the MPM online tool, there is no tool to study the distribution of the data e.g. as a histogram or descriptive statistics besides the mean. If the midpoint field in the Fuzzy Membership tool is left empty the tool uses mean of the input data as a mid point. In this case, the chosen extent is considered. It is an advisable practice to run a Fuzzy Membership tool first with an empty mid point field and keep notes of the used mid point which is reported in the 'Running model...'-window and updated back to tool if it was originally left empty. This way the user will know the mean value of the data and can then start to increase or decrease it manually for the following runs of the model.

- Connect the Fuzzy Membership output rasters to each other with Fuzzy Overlay tool. The assumption is that the user has scaled the inputs between 0 and 1 prior to combining them with a Fuzzy Overlay function.To specify the parameters the Fuzzy Membership tool can be opened by double clicking the Fuzzy Overlay rectangle. An additional window opens up to specify the parameters (Fig. 5).

-          The Fuzzy Overlay types and their short definition is given in table 2.

-          To view active parameters of the tool, hover the mouse over tool and the tooltip opens.

 

Overlay type

 

AND

Returns the minimum value of all of the input evidence rasters for each cell.

OR

Returns the maximum value of all of the input evidence rasters for each cell.

SUM

Calculates the product of unfavourabilities of all the input rasters and subtracts this from unity for each cell. Tends towards large values if even one of the inputs has a large value or if many inputs have intermediate values.

PRODUCT

Calculates the product of values of all the input rasters for each cell. Tends towards small values, if even one of the inputs has a small value or if many inputs have intermediate values.

GAMMA

The GAMMA type is typically used to combine more basic data. When gamma is 1, the result is the same as fuzzy SUM. When it is 0, the result is the same as fuzzy PRODUCT. Values between 0 and 1 allow you to combine evidence to produce results between the two extremes established by fuzzy AND or Fuzzy OR.

Table 2. Parameters of the Fuzzy Overlay functions with a short explanation. Edited from http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/fuzzy-overlay.htm.

 

 

Figure 4. The Fuzzy Membership tool. Specify the Fuzzy Membership type, Mid point, Spread and Hedge.

 

 

- Determination of the midpoint is critical for the success of the modelling. In the current version of the MPM online tool, there is no tool to study the distribution of the data e.g. as a histogram or descriptive statistics besides the mean. If the midpoint field in the Fuzzy Membership tool is left empty the tool uses mean of the input data as a mid point. In this case, the chosen extent is considered. It is an advisable practice to run a Fuzzy Membership tool first with an empty mid point field and keep notes of the used mid point which is reported in the 'Running model...'-window and updated back to tool if it was originally left empty. This way the user will know the mean value of the data and can then start to increase or decrease it manually for the following runs of the model.

- Connect the Fuzzy Membership output rasters to each other with Fuzzy Overlay tool. The assumption is that the user has scaled the inputs between 0 and 1 prior to combining them with a Fuzzy Overlay function.To specify the parameters the Fuzzy Membership tool can be opened by double clicking the Fuzzy Overlay rectangle. An additional window opens up to specify the parameters (Fig. 5).

-          The Fuzzy Overlay types and their short definition is given in table 2.

-          To view active parameters of the tool, hover the mouse over tool and the tooltip opens.

 

Overlay type

 

AND

Returns the minimum value of all of the input evidence rasters for each cell.

OR

Returns the maximum value of all of the input evidence rasters for each cell.

SUM

Calculates the product of unfavourabilities of all the input rasters and subtracts this from unity for each cell. Tends towards large values if even one of the inputs has a large value or if many inputs have intermediate values.

PRODUCT

Calculates the product of values of all the input rasters for each cell. Tends towards small values, if even one of the inputs has a small value or if many inputs have intermediate values.

GAMMA

The GAMMA type is typically used to combine more basic data. When gamma is 1, the result is the same as fuzzy SUM. When it is 0, the result is the same as fuzzy PRODUCT. Values between 0 and 1 allow you to combine evidence to produce results between the two extremes established by fuzzy AND or Fuzzy OR.

Table 2. Parameters of the Fuzzy Overlay functions with a short explanation. Edited from http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/fuzzy-overlay.htm.

 

Figure 5. The Fuzzy Overlay tool. Specify the Fuzzy Overlay type and the Gamma value (default 0.9).

 

Specifying the processing extent

 

-          The default processing extent of the MPM online tool is Northern Finland.

-          A smaller rectangle for the processing extent can be drawn on the map. In case of complex models this may be preferable to limit the processing time. The extent rectangle can be drawn using a rectangle tool (Fig. 5) under "ModelBuilder" located on the left hand side of the MPM online tool (Fig. 1). After selecting the tool, draw an area on the map by clicking in one corner of the extent with the left mouse button, keeping the mouse down and releasing it in the opposite corner of the extent.

 

Figure 6. The Model Builder Tool. Specify the processing extent with the rectangle tool and the known deposits and the modelled deposit type under "Select a layer for sample points".

 

-          Select the modelled deposit type under "Select a layer for sample points". The selection of the deposit type is only made for model validation with receiver operating characteristics (ROC) curves and their under the area curve (AUC) value (see FUZZY LOGIC for explanation). The data is in vector point file format and is derived from the GTK "Mineral deposit database". Choose None if you do not want to validate your model with the ROC tool. Naturally, the accuracy of the model is validated based on this selection regardless of what raster inputs you have selected to the model.

 

 

Running the model

 

- When the geoprocessing model in the Model Builder is ready, the processing extent is drawn on the map and the selected deposit type is selected, the model can the executed pressing the Run model button located in the MPM online tool (Fig. 6).

- The model is validated and the running order is determined by performing topological sort over tool nodes in the graph. This ensures the model is run in the correct order.

- When the model is running a "Running model..." information window is automatically opened on top of the browser.

- All input rasters in the model must be connected to a tool otherwise a warning will appear in the Running model... window and model will stop running.

- When running the model the Fuzzy tool flashes red. When the calculation is completed it turns green and plots the AUC box next to the Fuzzy Membership or Fuzzy Overlay rectangles.

- Raster analysis output layers will appear in the Model Builder when each step of the model is completed. A layer is generated for Fuzzy Memberships and Fuzzy Overlay outputs for viewing.

- When the model is running the model quality is being assessed for each input separately with ROC curves. The AUC of the ROC curve is reported in a box after each Fuzzy membership and Fuzzy Overlay step of the model. The ROC curve can be viewed by clicking at the AUC box appearing next to the Fuzzy tools in the Model Builder (Fig. 7).

- AUC box will appear green when AUC >0.5 and red when AUC <0.5 (Fig. 7).

- The Running model... information window will inform you when the model processing is completed. Press "Close" to exit the window.

Figure 7. An example fuzzy logic model made for orogenic gold deposits.

 

-          A output layers will appear under a group layer in the ModelBuilder.

-          You can remove the created model output layers from the ModelBuilder and map with the button Clear geoprocessing rasters located in the ModelBuilder. The model has to be run again in order to recreate the outputs.

-          The rainbow colour palette (red-yellow-green-blue) with Percent Clip (1%) histogram stretching is used as a default. Unfortunately, in the current version of the MPM online tool the histogram stretching or classification of the colours cannot be performed by the user.

-          When you rerun the model again the new output layers will appear under a new group layer. The new output layers in the Raster analysis layers have with the same layer names as in the previous runs of the model. In case you want to compare models you must be careful and keep your own notes how the versions of the model inputs and parameters differ in different model runs.

-          New model button removes the current model from the MPM online tool and opens up a new empty Model builder window. The old model will not be saved. Do not press the button unless you want to create a completely new model from scratch.

 

 

Visual assessment of the model outputs

 

-          The final outputs of the model can be viewed in the map and overlain by background data from GTK and other sources.

-          The minimum and maximum values of the outputs can be seen under Layers> layer name>Selite.