Roberto Miele

University of Lausanne | UNIL · Institute of Earth Sciences (ISTE)

Lake Garda, the largest lake in Italy at the southern front of the central Alps, provides a unique opportunity to reconstruct Quaternary geological processes related to the interplay between deglaciation driven sedimentation and tectonic activity. In fact, the topographic depression presently occupied by the lake was site of a major glacial tongue...

Understanding the thermal behavior of nonsteady state subsurface geosystems, when temperature changes over time, requires knowledge on the speed of heat propagation and, thus, of the rock's thermal diffusivity as essential thermo‐physical parameter. Mixing models are commonly used to describe thermo‐physical properties of polymineralic rocks. A the...

Accurate predictions of the spatial distribution of permeability in the subsurface is fundamental in reservoir characterization for several tasks (e.g., CO2 injection and storage monitoring or natural resources characterization). Nonetheless, modelling permeability is particularly challenging, due to its strong variability, spatial anisotropy and d...

Geostatistical seismic rock physics amplitude-versus-angle (AVA) inversion allows the joint prediction of rock and fluid properties from seismic reflection data. In these seismic inversion methods, the model perturbation and update occur iteratively in the petrophysical domain. A facies-dependent pre-calibrated rock physics model is applied to the...

Predicting the subsurface spatial distribution of geological facies from fullstack geophysical data is a main step in the geo-modeling workflow for energy exploration and environmental tasks and requires solving an inverse problem. Generative adversarial networks (GANs) have shown great potential for geologically accurate probabilistic inverse mode...

The simultaneous prediction of the subsurface distribution of facies and acoustic impedance (IP) from fullstack seismic data requires solving an inverse problem and is fundamental in natural resources exploration, carbon capture and storage, and environmental risk management. In recent years, deep generative models (DGM), such as variational autoen...

Predicting the spatial distribution of geological facies in the subsurface from fullstack geophysical data is a main step in the geo-modeling workflow for energy exploration and environmental tasks and requires solving an inverse problem. Generative adversarial networks (GAN) have shown great potential for geologically accurate inverse modeling, al...

Accurate prediction of the spatial distribution of subsurface permeability is a fundamental task in reservoir characterization and monitoring studies for hydrocarbon production and CO2 geological storage. Predicting permeability over large areas is challenging, due to its high variability and spatial anisotropy. Common approaches for modelling perm...

The Danish subsurface provides a large potential for the use of low-enthalpy geothermal heat and thereby to change the national district heating structure by providing a base load to the system. In the past decade, new exploration and research campaigns have been performed to remove geological, technical and commercial obstacles for a significant u...