Critical Infrastructure Systems must provide and guarantee a good basis for people, goods, services and information on which the health, safety, comfort and economic activity of a society depends. The security risks are increasing related with interdependencies between critical infrastructures that are not readily identifiable by traditional risk identification processes. It is necessary to have a way of systematic analysis of dependencies and interdependencies between critical infrastructures, using many dimensions (physical, cyber, geographic, logical and social). The relationships between Critical Infrastructure (CI) failure and their resilience are in function with interdependencies between subsystems of each CI. Critical infrastructures from the sector of energy are dependent from occurrence of geomagnetic storms and 3D distributions in underground of the electric conductivity. The external magnetic field penetration into the underground is direct dependent on the conductivity of the region. Also, magnetic field penetration is inverse dependent on his frequency. Deeper layers are more significant at long periods, and the shallow layers produce stronger influences at short periods. In this paper we show some considerations on: development of fast response algorithms, in real time, to highlighting some phenomena that precede of the geomagnetic storms and its beginning time; development of algorithms for determining the geomagnetic induced current (GIC); development of methodologies and complex analysis of multi parametric data related phenomenologically to develop from the conductivity maps to vulnerability maps.