Inherited structural controls on fault geometry, architecture and hydrothermal activity: an example from Grimsel Pass, Switzerland

Exhumed faults hosting hydrothermal systems provide direct insight into relationships between faulting and fluid flow, which in turn are valuable for making hydrogeological predictions in blind settings. The Grimsel Breccia Fault (Aar massif, Central Swiss Alps) is a late Neogene, exhumed dextral strike-slip fault with a maximum displacement of 25–45 m, and is associated with both fossil and active hydrothermal circulation. We mapped the fault system and modelled it in three dimensions, using the distinctive hydrothermal mineralisation as well as active thermal fluid discharge (the highest elevation documented in the Alps) to reveal the structural controls on fluid pathway extent and morphology. With progressive uplift and cooling, brittle deformation inherited the mylonitic shear zone network at Grimsel Pass; preconditioning fault geometry into segmented brittle reactivations of ductile shear zones and brittle inter-shear zone linkages. We describe ‘pipe’-like, vertically oriented fluid pathways: (1) within brittle fault linkage zones and (2) through alongstrike- restricted segments of formerly ductile shear zones reactivated by brittle deformation. In both cases, low-permeability mylonitic shear zones that escaped brittle reactivation provide important hydraulic seals. These observations show that fluid flow along brittle fault planes is not planar, but rather highly channelised into sub-vertical flow domains, with important implications for the exploration and exploitation of geothermal energy.

Self-learning of fault diagnosis identification

A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.

Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO

Many of the emerging telecom services make use of Outer Edge Networks, in particular Home Area Networks. The configuration and maintenance of such services may not be under full control of the telecom operator which still needs to guarantee the service quality experienced by the consumer. Diagnosing service faults in these scenarios becomes especially difficult since there may be not full visibility between different domains. This paper describes the fault diagnosis solution developed in the MAGNETO project, based on the application of Bayesian Inference to deal with the uncertainty. It also takes advantage of a distributed framework to deploy diagnosis components in the different domains and network elements involved, spanning both the telecom operator and the Outer Edge networks. In addition, MAGNETO features self-learning capabilities to automatically improve diagnosis knowledge over time and a partition mechanism that allows breaking down the overall diagnosis knowledge into smaller subsets. The MAGNETO solution has been prototyped and adapted to a particular outer edge scenario, and has been further validated on a real testbed. Evaluation of the results shows the potential of our approach to deal with fault management of outer edge networks.