Building a drone data pipeline for structural geologists

Written By Guest User

Based on work from Prabhakaran et al. 2019, structural geologists have successfully used ASDC pipelines to analyse drone gathered imagery. This work delivered a fully automated workflow for the analysis of large photogrammetry datasets into fracture network maps.

Figure 1: Example fracture network map from Prabhakaran et al. 2019

Structural Geology Use Case

Geological outcrops and features aren’t always in a convenient nor safe location for study. For this reason, drones are now a common tool within the geoscience research community for the observation of geological outcrops. 

Whilst point clouds and 3D models are an effective way of digitising geological features at scale, high resolution, drone data of this kind comes with its challenges when researchers come to quantifying and interrogating the large datasets collected.

Currently methods of interpreting involves manually interpreting fractures and mapping them to the digitised surface of rock features. It is time-consuming and forms a bottleneck in the workflow

Based on Prabhakaran et al., researchers are now working to automate the data interrogation process within the ASDC Platform by building tools for the user community. These tools will enable automated creation of outcrop 3D models, integrated with algorithms for the automatic mapping of discrete fracture networks (DFN). DFNs are the mapped properties of a rock fractures and find applications in modelling geothermal heat recovery, hydrocarbon exploitation, and groundwater flow.

The structural geology research community is set to benefit from this work by accelerating: 

  • the generation of large photogrammetric datasets.

  • the interpretation of that data at scale.

  • Lowering interpreter bias.

  • the development of fully automated workflows.

  • visualisation, sharing, and publishing of results to the community.

We’d like to hear from you

If you would like to know more about how the ASDC platform might accelerate your next drone data capture project, please email us at info@asdc.science, or help us learn more about what you would like to achieve by filing in this form.

References

Prabhakaran, R., Bruna, P.-O., Bertotti, G., & Smeulders, D. (2019). An automated fracture trace detection technique using the complex shearlet transform. Solid Earth, 10(6), 2137–2166. https://doi.org/10.5194/se-10-2137-2019 

Reisenhofer, R., & King, E. J. (2019). Edge, Ridge, and Blob Detection with Symmetric Molecules. SIAM Journal on Imaging Sciences, 12(4), 1585–1626. https://doi.org/10.1137/19M1240861

Research Group Computational Data Analysis. (2018). PyCoShREM. Python, Bremen, Germany: Research Group Computational Data Analysis, University Bremen. Retrieved from https://github.com/rgcda/PyCoShREM (Original work published January 3, 2018)

Weismüller, C., Prabhakaran, R., Passchier, M., Urai, J. L., Bertotti, G., & Reicherter, K. (2020). Mapping the fracture network in the Lilstock pavement, Bristol Channel, UK: manual versus automatic. Solid Earth Discussions, 1–39. https://doi.org/10.5194/se-2020-67 

Weismüller, C., Reicherter, K., Passchier, M., & Urai, J. (2020). The fracture network in the Lilstock pavement, Bristol Channel, UK: digital elevation models and orthorectified mosaics created from unmanned aerial vehicle imagery (No. RWTH-2020-06903). Lehr- und Forschungsgebiet Neotektonik und Georisiken. https://doi.org/10.18154/RWTH-2020-06903 


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