Demonstrate the combining of two relatively large time-dependent datasets. The analysis answers the question: What is the association between mineralisations and the depositional environment? This example shows that complex sub-analyses, such as investigating this as a function of the commodity type is trivial when undertaken in a quantitative fashion.
Load GPlates from the command line as follows:
Load features and set properties as follows:
The following depicts the loaded data:
Configure the animation to start at (no earlier than) 540 Ma, and end at present day, with steps of 10 Ma.
In this analysis, we wish to investigate the sedimentological environment in which an ore-deposit has formed. This is to be determined by computing the environment in which an ore deposit occurs for all times, and then subsequently determining the property at the "birth-age". Later we discuss how we can look at relationships as a function of the commodity type. Performing this analysis entails investigating two data properties, namely the "Environ" property of the Palaeogeography dataset, and the "name" parameter of the OZMIN dataset, representing the commodity name. Two steps are involved, namely defining the data association, and analysing the data via the data mining tool (next step).
Data coregistration is performed via the Layers dialog in GPlates (show via the "Window" menu item if absent). The following steps define the required data association:
Select the new layer, and expose inner parameters by selecting the triangle button on the left side of the layer. The following depicts exposed parameters:
First a coregistration seed channel is defined, which is essentially the independent variable in the analysis. Coregistration inputs are then dependent variables, depicting relations and associations with respect to the seeds. In the layer tool, select the OZMIN dataset as the seed. Both the OZMIN and palaeogeography datasets are then to be selected as coregistration input channels; the OZMIN dataset is included here so that the commodity type can be included in the analysis. The layer parameters should look as follows:
In the next step, the data association is configured by selecting the "Co-registration Configuration" on the layer dialog. First the commodity name is added by selecting the commodity dataset, selecting the "Coregistration" option (as opposed to "Relational"), and then selecting the property name. Selecting the "Add" button will then add this to the configuration table. The palaeogeography dataset should then be selected similarly, and the ENVIRONMEN attribute selected, following by adding to the configuration table. The coregistration configuration should look as follows:
Now that the coregistration has been configured, we can visually inspect how the defined associations change as we vary the GPlates time slider. The coregistration values can be viewed by choosing a desired time, and selecting "View Result" on the layer dialog corresponding to the coregistration layer. (The first three attributes are always automatically set to be the GPlates-ID and begin and end time of the seed, respectively.) At a time of 150 Ma, the following results are obtained:
Similarly, the following depicts results at 50Ma:
Note that seed points not yet defined at a particular time instant do not appear on the output (thus different rows for different times). Additionally, due to GPlates' method of determining the associations for all times, the result table can look different anyway.
The final coregistration step is to export the coregistration results to an output directory. This is an interim step while GPlates is coherently integrated with the data mining suite. The coregistration export will output comma-separated-value (.csv) files for each time instant defined by the "Configure animation" option in the GPlates menu. For this analysis, the following steps should be followed:
The Add data button should be selected, followed by selection of the "Co-registration data" export type, and highlighting of the CSV file output format option. Please leave other parameters at their default values, and ensure that the sub-string "co_registration" forms part of the filename (this is expected by the data mining tools). The following should result:
Once the coregistration analysis results have been exported via GPlates, the next step is to perform data mining and analysis of the time-dependent results. The Orange data mining tool is being used for this, with GPlates specific-plugins underway. A visual-programming environment is being used to abstract analysis complexity and improve flexibility. Current developments will also see Orange directly integrated with GPlates, but at this preliminary stage Orange has to be started as a separate program.
Once Orange has been started, various data mining and analysis tools, called Widgets can be selected from collections. A GPlatesPalaeoAssociations collection has been developed for the analyses required here. The following steps should be undertaken for this analysis:
In a first step, the "TimeSeries" widget from the GPlatesPalaeoAssociations collection should be dragged onto an empty canvas. This widget analyses all coregistration outputs across time, and returns a homogenous table for the chosen coregistration attribute corresponding to each seed geometry for all times. Thus this widget forms a time-series data structure, ready for subsequent analysis. After double-clicking on the widget to expose parameters, the directory which the coregistration results were exported to should be chosen. The "attribute" parameter then allows for selection of the coregistration analysis of interest (scrolling through attribute indices will result in the associated attribute names of the cregistration being shown). For this exercise, the fourth attribute index should be chosen, pertaining to the ENVIRONMEN attribute. (As already mentioned above indices 0, 1 and 2 are always reserved for the GPlates-ID and begin and end time of the seed in question.) The following depicts the first step (it is a good habit to rename widgets appropriately for better illustration):
Dragging a "Data Table" widget from the Data collection, and connecting it to the output of the "TimeSeries" widget allows the data structure to be studied in time-series format (tables denoted with "Check" are just for checking intermediate results):
Next a second "TimeSeries" widget should be dragged onto the canvas, and the name attribute selected. Since this attribute does not change over time, the present day attribute represents the commodity type over all times, extracted by attaching the "AttributeAtTime" widget to the output, set to extract results at present day. A single vector of results is obtained, as depicted below:
Next the "BirthAttribute" widget from the GPlatesPalaeoAssociations collection should be connected to the output of the first "TimeSeries" widget. This widget is useful for establishing palaeo-relationships at the time of formation. This is achieved by detecting the point in time at which the seed becomes valid, and then recording the selected attribute at that time:
Note that many of the output results are shown as "NaN", i.e. invalid. This is because many of the seed features formed before 540 Ma, and relevant palaeo-geographic information is not available. The "Attribute Statistics" widget from the Visualise collection is useful to look at the overall statistics of the results:
To complete the analysis, the "Select Data" widget should be selected from the Data plugin, followed by the "Attribute Statistics" attribute from the Visualise plugin. The "Select Data" widget can be used interactively to filter results according to either the environment or commodity types. The following schematic completes the exercise:
Three analyses are shown to demonstrate how the resultant Orange schematic can be used for investigating spatio-temporal associations. The "select data" widget allows for the required analysis interactivity.
Commodities that formed in Land environment erosional environments:
Commodities that formed in Marine shallow environments:
Environments in which Gold formed: