Obtaining reliable estimates of the subsurface state distributions is essential to determine the location of e.g. potential nuclear waste disposal sites. However, providing these is challenging since it requires solving the problem numerous times yielding high computational cost. To overcome this, we use a physics-based machine learning method to construct surrogate models. We demonstrate how it produces physics-preserving predictions, which differentiates it from purely data-driven approaches.

The rotation of the principal stress axes in a fault structure because of a rock stiffness contrast has been investigated for the impact of the ratio of principal stresses, the angle between principal stress axes and fault strike, and the ratio of the rock stiffness contrast. A generic 2D geomechanical model is employed for the systematic investigation of the parameter space.

It is generally assumed that faults have an influence on the stress state of the Earth’s crust. It is questionable whether this influence is still present far away from a fault. Simple numerical models were used to investigate the extent of the influence of faults on the stress state. Several models with different fault representations were investigated. The stress fluctuations further away from the fault (> 1 km) are very small.

When stresses yield a critical value, rock breaks and generate pathways for fluid migration. Thus, the contemporary undisturbed stress state is a key parameter for assessing the stability of deep geological repositories. In this workshop you can ask everything you always wanted to know about stress (but were afraid to ask), and this is divided into three parts. 1) How do we formally describe the stress field? 2) How do we to actually measure stress? 3) How do we go from points to 3D description?

The subsurface is subject to constant stress. With increasing depth, more rock overlies an area, thereby increasing the stress. There is also constant stress from the sides. Knowledge of this stress is fundamental to build lasting and safe underground structures. Very few data on the stress state are available; thus, computer models are used to predict this parameter. We present a method to improve the quality of the computer models, even if no direct data on the stress state are available.