Quantifying and analysing rock trait distributions of rocky fault scarps using deep learning

Abstract

We apply a deep learning model to segment and identify rock characteristics based on a Structure‐from‐Motion orthomap and digital elevation model of a rocky fault scarp in the Volcanic Tablelands, Eastern California, USA. By post‐processing the deep learning results, we build a semantic rock map and analyse the rock trait distributions. The resulting semantic map contains nearly 230 000 rocks with effective diameters ranging from 2 to 250 cm. Rock trait distributions provide a new perspective on rocky fault scarp development and extend past research on scarp geometry including slope, height and length. Heatmaps indicate rock size spatial distributions on the fault scarp and surrounding topographic flats. Median grain size changes perpendicular to the fault scarp trace, with the largest rocks on the downslope proximal to the scarp footwall. Correlation analyses illustrate the relationship between rock trait statistics and fault scarp geomorphology. Local fault scarp height correlates with median grain size ( R2 = 0.6), the mean grain size of the largest rocks ( R2 = 0.8) and the ratio of the number of small to large rocks ( R2 = 0.4). The positive correlation ( R2 = 0.8) between local fault scarp height and standard deviation of grain size suggests that rocks on a higher fault scarp are less well sorted. The correlation analysis between fault scarp height and rock orientation statistics supports a particle transportation model in which locally higher fault scarps have relatively more rocks with long axes parallel to fault scarp trace because rocks have a larger distance to roll and orient the long axes. Our work demonstrates a data‐driven approach to geomorphology based on rock trait distributions, promising a greater understanding of fault scarp formation and tectonic activity, as well as many other applications for which granulometry is an indicator of process.

Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning

“…Finally, by obtaining precisely segmented grain masks, crucial data on particle sphericity, roundness and orientation (e.g., Steer et al, 2022) can be obtained. They can inform novel data‐driven study designs (e.g., Chen et al, 2023). Furthermore, grains from the prediction masks of our models might also form the basis for other machine learning applications, for example, the training of a classifier to identify the particles' petrography.…”

“…In addition, both tend to systematically over‐/underestimate the sizes of grains (e.g., Chardon et al, 2020; Chardon, Piasny, & Schmitt, 2022; Mair et al, 2022a). Therefore, and most recently, attention turned to deep neural networks, with the aim either to improve the segmentation in images (e.g., Chen et al, 2023; Chen, Hassan, & Fu, 2022; Mörtl et al, 2022; Soloy et al, 2020) or to directly predict percentile values of a grain size distribution (e.g., Buscombe, 2020; Lang et al, 2021). The main aims of all these works were to automate the measurements, improve the reproducibility and scalability, and to increase the number of observations.…”

“…The main aims of all these works were to automate the measurements, improve the reproducibility and scalability, and to increase the number of observations. Although deep learning did improve the segmentation for some data and settings, challenges have remained (e.g., Chen et al, 2023; Chen, Hassan, & Fu, 2022). In addition, current methods have limited ability to adapt to new, previously unseen data.…”

“…can be obtained. They can inform novel data-driven study designs (e.g.,Chen et al, 2023). Furthermore, grains from the prediction masks of our models might also form the basis for other machine learning applications, for example, the training of a classifier to identify the particles' petrography.…”

Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning

“…In addition, both tend to systematically over-/underestimate the sizes of grains (e.g., Chardon et al, 2020;Mair et al, 2022a). Therefore, and most recently, attention turned to deep neural networks, with the aim either to improve the segmentation in images (e.g., Chen et al, 2022a;Mörtl et al, 2022;Soloy et al, 2020;Chen et al, 2023) or to directly predict percentile values of a grain size distribution (e.g., Lang et al, 2021;Buscombe, 2020). The main aims of all these works were to automate the measurements, improve reproducibility and scalability, and increase the number of observations.…”