How biased are our models? - a case study of the alpine region

Geophysical process simulations play a crucial role in the understanding of the subsurface. This understanding is required to provide, for instance, clean energy sources such as geothermal energy. However, the calibration and validation of the physical models heavily rely on state measurements such as temperature. In this work, we demonstrate that focusing analyses purely on measurements introduces a high bias. This is illustrated through global sensitivity studies. The extensive exploration of the parameter space becomes feasible through the construction of suitable surrogate models via the reduced basis method, where the bias is found to result from very unequal data distribution. We propose schemes to compensate for parts of this bias. However, the bias cannot be entirely compensated. Therefore, we demonstrate the consequences of this bias with the example of a model calibration.

Automated summary

Geophysical process simulations are crucial for understanding the subsurface and providing clean energy sources, but calibration and validation of physical models heavily rely on state measurements, leading to bias issues that can only be partially compensated for through suitable surrogate models and compensation schemes.