We introduce the 3D-segmentation and -visualization software YaDiV to the mineralogical application of rock texture analysis. YaDiV has been originally designed to process medical DICOM datasets. But due to software advancements and additional plugins, this open-source software can now be easily used for the fast quantitative morphological characterization of geological objects from tomographic...

The traditional advancing front technique algorithm encounters many problems due to the complex geometric characteristics of edge shape. These include poor quality, a slow algorithm, low robustness, and inability mesh unit converge. To address these problems, an optimized adaptive triangular dynamic generation called R-TIN is proposed applied 3D engineering geological modeling in this study. Fi...

Throughout this dissertation, solutions for rapid digitalization of ideas will be defined. More precisely, the focus is on interactive scientific sketching and communication of geology, where the result is a digital illustrative 3D model. Results are achieved through a sketch-based modelling approach which gives the user a more natural and intuitive modelling process, hence leading to a quicker...

The three-dimensional laser scanning technique has recently become common in diverse working environments. Even in geology, where further development is needed, this technique is increasingly useful in tackling various problems such as stability investigations or geological and geotechnical monitoring. Three-dimensional laser scanning supplies detailed and complete geometrical information in sh...

Accurate salt interpretation involves an integration of geology and geophysics. In the practice of salt interpretation and model building in depth imaging, we see two problems that could happen: exclusive geophysical interpretation without geological input and insufficient geological interpretation without understanding how the geophysical data are processed and without evaluating the limit and...

Data assimilation in subsurface flow systems is challenging due to the large number of simulations often required, and by need preserve geological realism calibrated (posterior) models. In this work we present a deep-learning-based surrogate model for two-phase 3D formations. This model, recurrent residual U-Net (referred as R-U-Net), consists convolutional (convLSTM) neural networks, designed ...