Afloraments fracturats digitalitzats. Avaluació de tècniques remotes en models DFN i aplicació de machine learning
- Autores: Laura Blanco Núñez
- Directores de la Tesis: Josep Anton Muñoz i de la Fuente (dir. tes.), Òscar Gratacós Torrà (codir. tes.)
- Lectura: En la Universitat de Barcelona ( España ) en 2023
- Idioma: catalán
- Tribunal Calificador de la Tesis: Eduard Roca Abella (presid.), Oriol Falivene Aldea (secret.), Roger Ruiz Carulla (voc.)
- Programa de doctorado: Programa de Doctorado en Ciencias de la Tierra por la Universidad de Barcelona
- Materias:
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- Resumen
The ongoing digital transition results in millions of data points gathered every day. Within the context of geosciences, the use of digital remote sensing techniques is increasing. Machine learning techniques are interesting alternative to support the processing and interpretation of large amounts of collected data. The present industrial PhD focuses on the analysis of fractured outcrops using remote sensing techniques and machine learning with two specific objectives; (1) the evaluation of how representative are remote sensing techniques to identify fractures and subsequently represent them in discrete fracture network (DFN) models; and (2) the use of classification machine learning models in processes associated with fractured environments, such as block falls.
Methodology The first case study, evaluates remote techniques for data gathering (Terrestrial Laser Scanner (TLS) and Photogrammetry (SfM)). The experiment was carried out in active La Fou quarry in Barcelona. Fractures surfaces were reconstructed from a large number of remotely captured data (Santana et al., 2012; Geyer et al., 2015), the fractures network is characterized by multiple families (García-Sellés et al., 2018). With these results, and those collected with manual scanline techniques, three-dimensional (DFN) models are constructed. One model is built using data from each capture technique (DFNTLS, DFNSfM and DFNscan-line), where the main geometric properties of the fractures will be represented. Finally, the fracture trace maps are generated to calculate the porosity and permeability using aperture data for each of the scenarios.
The second case study is aimed at automating the classification of block falls. The study area is located on the historically unstable Degotalls escarpments, close to the access road of the Monastery of Montserrat. Monitoring of these escarpments using periodic TLS scanning began in 2007, as a result of multiple falling block episodes starting back in 2001, which affected the Monastery car park (limiting access to the Sanctuary on several occasions). The methodology consists of aligning two scans or point clouds of the same outcrop, captured at two different times, aiming to detect clusters of differences between the scans. Subsequently, each cluster of differences is visually reviewed to identify and classify block falls. The methodology developed in this Thesis, incorporates machine learning algorithms to automate this last stage of identification and classification of fallen blocks.
Results In the first case study, the main geometric properties for each fracture captured and processed are position, orientation, height, and length. These can be used to characterize fracture statistics such as spacing, intensity, P10, P20, P21 and P32, among others. For individual families are identified Family I WNW-ESE, Family II NNW/SSE, Family III, N-S and Family IV NE-SW. The technique with the highest number of characterised fractures was TLS with more than 52,000, followed by photogrammetry with 1,094 and finally scan-lines with 398 fractures measured. Fracture height and length are very variable, average values range from a few centimetres to 18m. TLS average lengths are less than one metre. The spacings Facultat de Ciències de la Terra Página 4 de 4 between fractures in Family I have centimetric sizes and the rest of the families have more variable spacing from centimetric to 6m. Photogrammetry obtains values that sometimes double or triple the rest of the techniques. Furthermore, with the input of the fracture openings into the model, trace maps have been generated and porosity and permeability value of the fracture network has been obtained. In the three DFN models, the average porosity value obtained is between 5.4-5.7% and the average permeability values between 4x105 mD and 6.2x105mD.
In the second case study, the results generated from the combination of 11 classifier algorithms and 15 resampling methods, allow us to identify the best predictive combination for the classification of fallen blocks, according to the temporal period and steepness. In Degotalls-E, from approximately 5,800 clusters identified by each monitoring, a 98% reduction is achieved in the number of clusters to be validated, between False Positives (FP) and True Positives (VP), understanding (VP) as the real block falls. In Degotalls-N the reduction would be 80.16% for a complete identification, while for identifying 96% of the real block drops, the reduction would be 90%, from a population of about 3,700 initial clusters. It is also important to note that the validation of the task by comparing high-resolution images has significantly increased the reliability of the results obtained.
Conclusions and the way forward In the evaluation of the representativeness of using remote sensing techniques for the construction of DFN models, the results suggest that data captured and processed with remote techniques are represented in DFN models with very acceptable results. The remote sensing technique that more accurately approximates the outcrop depends on the geometric property that need to be captured and represented. The layout of the outcrop and fracture orientations also influence data capture. When analysing the results for permeability and porosity of the fracture network, similar values are obtained for all three DFN models. Consequently, the geometrical differences of the models by each technique do not greatly influence the estimation of these values, showing equivalent fracture models in the case of the Fou. The DFN models, which are currently obtained using remote sensors allow the uncertainties generated in the three-dimensional regional models to be characterized measured and reduced. This will be the obhect of further research both academic and industrial.
Finally, the work carried out at Montserrat shows that the application of remote techniques in the field of digitisation and the identification of differences during the monitoring of outcrops is precise and of high resolution. This new methodology integrating machine learning algorithms for the identification and classification of fallen blocks demonstrates, especially in Degotalls-E, that applying this system generates acceptable results in terms of True Positive (VP), False Positive (FP) and False Negative (FN). This automation will eventually allow to implement a block drop alert system, with an integrated TLS device, which allows and facilitates the detection of precursors, involving an application of the technique used during the development of this thesis in the industrial world
