Hi Jens,

Thanks for the response, I appreciate it and I apologise for not giving a clear description of what I intended with the question. Let me give that a second go in terms of what the two data sets are. Data Set 1 (field mapping discontinuity data): this data set includes the recorded orientation of 2500 joints across the project site. A single entry of this data set includes the XYZ coordinates and dip, dip direction and strike measurements for the respective joint. Data Set 2 (lineament analysis): this data set is the discontinuity data that was extracted from the drone imagery thus it also provides XYZ, dip and azimuth for discontinuities. However, the data comes from drone images which cover the entire project site and not only the areas accessible by foot thus the data have >10 000 data entries.

That being said, my task is to determine whether the dip and azimuth (strike) of discontinuities from the lineament analysis (Data Set 2) correlate with the dip and strike for the discontinuities measured in the field (Data Set 1). I can visually do this on a stereonet plot but I would like to determine a correlation coefficient between the two data sets. This would indicate how relatable the lineman analyses are and whether we can use the data going forward.

I looked at the Pearson Correlation Coefficient but that works with only one parameter, for example comparing azimuth with azimuth which then ignores the dip of the discontinuities. I get good results but the dip of discontinuity is a critical factor which I can’t ignore for stability purposes of the project. So I need a method where I can compare azimuth and dip (Data Set 1) with azimuth and dip (Data Set 2). I hope this explains a bit more about my initial question and what I am trying to establish. Kind Regards, Marthinus

Thanks, Jens, that is not a bad idea, I will look into the physical comparison when we get into the field again. It could be an interesting test to determine the accuracy, bias and level of error between the two methods.

In the meantime, I will continue my search for an appropriate methodology for a comparative study, and if I have success I will share it and the outcome.

Appreciate your input.

Regards,

Los datos x,y,z y t,"in situ" de campo son indispensables, porque el nivel de precisión es óptimo y la presencia del investigador es invaluable.

Comparing drone-captured lineament data to dip and strike measurements from field mapping involves integrating and analyzing data from different sources to identify relationships, patterns, and potential correlations. Here's a step-by-step guide on how you can approach this comparison:

**1. Data Collection:**

- Drone-Captured Lineament Data: Process the drone-captured imagery to identify and extract lineament features, which are linear features like faults, fractures, or other geological structures.

- Dip and Strike Measurements: Collect dip and strike measurements from field mapping using tools like a compass-clinometer or geological compass. These measurements provide information about the orientation of rock layers or geological structures.

**2. Data Preparation:**

- Organize and format both sets of data for analysis. Ensure that the data types and units are consistent.

- Convert dip and strike measurements into Cartesian coordinates (x, y, z) to match the format of the drone-captured lineament data.

**3. Geographic Referencing:**

- Ensure that both datasets are properly georeferenced to a common coordinate system (e.g., UTM).

- Use ground control points or GPS to accurately align the drone imagery with the field mapping data.

**4. Data Integration:**

- Overlay the drone-captured lineament data on top of the dip and strike measurements. GIS software can help you visualize and analyze the integrated dataset.

**5. Visual Comparison:**

- Create maps or 3D models to visually compare the lineaments with the dip and strike measurements. This can help identify potential correlations or alignments.

**6. Statistical Analysis:**

- Calculate statistical measures to quantify the relationship between lineament orientations and dip/strike measurements. Measures like circular statistics (e.g., mean vector, concentration parameter) can provide insights into alignment trends.

**7. Correlation Analysis:**

- Use correlation analysis to determine if there is a statistically significant relationship between specific lineament orientations and dip/strike measurements.

- Pearson correlation coefficient or Spearman's rank correlation can be used, depending on the nature of the data.

**8. Geologic Interpretation:**

- Compare the results of the analysis with known geological concepts and theories. Interpret the potential implications of the observed correlations or alignments.

**9. Field Validation:**

- Return to the field to validate any interesting correlations or patterns discovered through data analysis. This helps ensure that the relationships observed in the data hold true on the ground.

**10. Documentation and Reporting:**

- Document your analysis process, methodologies, results, and interpretations in a clear and concise manner. This documentation is crucial for sharing your findings with the geoscience community.

**11. Uncertainty Consideration:**

- Keep in mind the uncertainties associated with both datasets. Drone imagery might have limitations in capturing small-scale features, and field measurements can have inherent measurement errors.

Remember that the comparison should be approached with a critical mindset, considering geological context and potential confounding factors. It's also essential to collaborate with geologists and experts in the field to ensure the accuracy and validity of your analysis.