Audrey Goulet

RMDA app: new geotechnical domains delimiter app and more!

A new app has appeared in the Rock Mass Data Analyser suite: the Geotechnical Domains Delimiter. This app allows you to create your own geotechnical domains; either from boundaries (e.g. lithological contacts or fault planes) or volumes (e.g. lithologies, domains, selection boxes). The app uses the HW/FW filter to classify the space in relation to each survey imported (inside a survey = ‘ore’; hanging wall = hang; footwall = foot). Each unique fingerprint combination is represented by a point (see pink dots in Figure 2). To identify key areas, these points need to be assigned as a ‘reference point’ by giving it a name (Figure 2). Afterwards, geotechnical domains names can be assigned to the defined reference points (Figure 3). A single geotechnical domain may have multiple fingerprints, thus it needs to be defined by more than one reference point. In this example, most of the geotechnical domains were associated with two fingerprints each. The geotechnical domains created automatically classify all the data in the Rock Mass Data Analyser: the rock mass quality data (e.g. RQD, G, RMR, GSI), the structures, the geology observed in borehole segments, stress measurements etc. Figure 4 shows the lab tests, the rock mass quality intervals along boreholes and stress measurements in Region2 with ticks in the Filter panel. For more information, view the five training videos detailing the Geotechnical Domains Delimiter app. Borehole ID: more flexibility to show text in 3D For all borehole sources, you now have the ability to show the borehole ID along the dip of the hole and at the top or bottom. To do so, the controls of the text series related to the borehole IDs must be adjusted (see Figure 5). If more than 1,000 boreholes need to be displayed, do not forget to increase the ‘Max # to plot’ field! Detailed data errors There is now a detailed error panel of the data imported and saved for all data sources (rock mass quality, structures, lab tests etc.). These panels detail the boreholes or sample for which the data is outside the expected range, the concerned parameter, and its value. A table summarising all the rows containing ‘bad quality data’ is available in Rock Mass Data Analyser. The values outside expected ranges are clearly highlighted in red. The segments containing ‘bad quality’ data can also be seen in 3D space (Figure 6). The ‘good data’ table only shows the data used for calculations; if a sample/borehole interval contained a ‘bad value’ for a parameter, only that parameter is ignored in the calculations, not the entire data for the segment/sample. Rock mass quality data: using RQD from another source New features in the Rock Mass Data Importer/Analyser allow you to import RQD values with intervals other than the one defined with your rock mass quality data csv. The first step is to import the csv containing the RQD data along the borehole, in the following column order: Borehole ID, From, To, RQD value (see Figure 7). Afterwards, the newly imported RQD values can be seen alongside the RQD values from your rock mass quality data imported, if it exists. You can choose which RQD values to use for the further analysis (Tables, Charts & 3D View) in ‘Select RQD sources’ (see Figure 8). The same panel can be found in both the Rock Mass Data Importer app and the Rock Mass Data Analyser app. Do not hesitate to contact the mXrap support team for an app upgrade to get all these features!

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Sensitivity Analysis in Hazard Assessment app

A new window has been added to the Hazard Assessment app (Figure 1). The sensitivity analysis aims to quantify the effect of the variation of the input parameters on the hazard.  These analyses can also help the user to determine the optimal settings to use for many of the parameters. This window allows the user to vary only one parameter at the time. The steps to realize such a sensitivity analysis are quite simple (Figure 2). All the input parameters that can be studied are: Examples presented are from the Tasmania root and expressed a sensitivity analysis for the grid spacing. Annualised probability The following hazard calculations all refer to the annualized hazard and apply to the entire volume of the chosen grid: The overall variation for the three main hazard assessment are shown in Figure 3. Grid-based Hazard (Hazard Iso’s window) The Iso View describes the hazard at all locations within the mine but when you are considering the seismic risk for a particular work area, large events and strong ground motions may come from multiple sources. Details on hazard Iso’s can be found in the blog post:   For the grid-based hazard, the distribution of parameters listed in Table 1 can be investigated. The distribution refers to the value for each grid point composing the volume studied, for which the grid extent and spacing are set. Hazard Parameters Other Parameters ML Rating:   Seismic Rateb-value MminKS Frequency-Magnitude relation Table 1 – Parameters for which the impact of parameter input variation are studied The ML Rating is the design magnitude that would have a probability of exceedance of 15%. The ML Rating Isossurface can be visualised in the 3D view for each ML Rating Level ticked for each step (Figure 4). Hazard on excavations The Excavation View estimates the seismic hazard associated with working areas (minode locations) in a few different ways. More details can be found on blog post : The effect of the input parameters variations can be visualised on the hazard on excavation, expressed by the P [ML within R] and P [PPV > PPVDESIGN]. The distribution of both parameters for each minode can be seen for each step (Figure 5).

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HW–FW filter

For a few months now, a new tool has appeared in the General Analysis app; the hanging wall (HW) and footwall (FW) filter. The HW–FW filter allows you to filter your events based on where they are in relation to ticked survey/s. If more than one survey or plane is used for the HW–FW filter, they need to be somewhat parallel in order to make sense. Below is an example of the classification when one survey (FIG 1) and two surveys (FIG 2) are used. The events are categorised into four categories, which can be visualised one at a time or simultaneously:  If mXrap does not use the terms hanging wall and footwall correctly, there is the option to ‘flip’ it. By default, the events are classified using a plane orientation that is automatically determined by averaging the orientation of the triangles in the input survey/s. It is also possible for you to specify the overall plane orientation (dip and dip direction) used in calculations, which may be useful if the automatically determined orientation does not match your expectations. The change of the overall plane orientation will affect the event’s classification (FIG 3). The events are unclassified if they are outside the boundaries of the survey, or if there is a ‘hole’ in the survey. There is the possibility to classify the unclassified events under HW, FW and ore by ticking ‘using nearest vertex instead’. Examples of how the events are classified for the earlier example using the ‘classify outer events by nearest vertex’ way is shown (FIG 4). The volumes created by the HW–FW filter can be saved. A volume will be created for each classification (HW, FW, ore, unclassified) with the defined name, which could be the name of the survey/s used for the classification. These volumes will automatically appear in the VSA table (FIG 5). If your surveys have dense mesh, consider using the ‘simplify mesh’ option as it will speed up the calculations process for the exported filter volumes. For now, these filters are used for events. However, the same tool can be applied to other data. That classification tool can be used with multiple surveys simultaneously. It can further be applied to different data types, such as structures, rock mass classification or intact rock tests. Stay tuned for further tips and training!

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