ALBERT

Description: Deep seismic sounding provides important information on the seismic structure of the crust. Seismic experiments make use of controlled sources (explosions, Vibroseis) or natural sources (earthquakes, ambient noise), or combinations of both types. Seismic velocities are derived from modelling or tomographic inversion of diving waves and refractions. Particularly the combined interpretation of compressional and shear velocities allows for insight into the lithological structure of the crust. Seismic reflectivity is derived from imaging of waves reflected at geological boundaries. Typical patterns of crustal reflectivity are observed for specific tectonic settings. Case studies are shown from two studies at plate boundary systems. (1) The old plate boundary at the Namibian margin was formed by Cretaceous continental rifting and its interplay with the activities of the Tristan da Cunha mantle plume. Traces of intensive magmatic overprinting of the crust at the landfall region of Walvis Ridge can be seen in the derived velocity model and also in the reflectivity image. (2) The Dead Sea transform marks the active boundary between the African and Arabian plates. The Dead Sea basin was formed by pull-apart in response to step-over of the fault system. Results from tomography reveal a deep asymmetric basin structure. An anomalous body was found under the basin, between 13 and 18 km depth, which is interpreted as pre-basin sediments. Our results are supported by the distribution of earthquakes. The results provide new constraints for the modeling of plate boundary processes.

Description: The provision of appropriate technologies is a major challenge in the ecological and sustainable use of geological resources. At the same time, there is an increasing awareness that innovative technologies are needed to allow for an environmentally sound and economically feasible exploitation of geo-reservoirs. This applies for both traditional and for more recent efforts of utilizing the geological underground, e.g. for geothermal energy production, storage of CO2 or other gases, the exploitation of gas shales and the storage of heat and chill. Most approaches with respect to lowering the environmental impact and improving the productivity of a reservoir and for monitoring physical, biological and chemical changes in such reservoirs currently under discussion can be looked upon as cross-cutting issues as they contribute to the various or even all areas mentioned above. The geoenergy concept focuses, in particular, on these cross-cutting issues and, at the same time, highlights the gaps in knowledge and respective research needs. Thus, a holistic approach is required that integrates exploration, exploitation and utilisation of potential reservoirs with innovative concepts for monitoring. Accordingly, the research fields of the cross-cutting topics described below focus on methodological development applicable in equal measure to the utilisation of geothermal energy and of shale gas as well as to the use and monitoring of CO2 storage. Complementary, new modelling approaches need to be developed that allow for the simulation of the involved processes to predict the occurrence and physical properties of potential reservoirs and the changes that may be induced by their utilisation. In addition, interactions between the different kinds of reservoir use need to be anticipated as well as related aspects of synergy or competition (CO2-Storage vs. Shale Gas vs. Geothermal).

Description: Neural networks are simplified mathematical models to simulate certain aspects of information processing in biological nervous systems. Some principles adopted from nature include parallel processing, learning by example, and abstraction of knowledge. Neuro-computing is a rapidly growing branch of research, where constantly new types of neural networks are developed for different applications. Pattern recognition and classification is the typical application of such approaches. Many tasks in geosciences, particularly in data interpretation, can be considered and treated as classification problems. In such a terminology, the studied objects (e.g. sub-regions of earth models) are classified as rock types based on characteristic features (e.g. physical properties). Numerical solutions have to address challenges such as incorrectness and incompleteness of data, and overlap in some properties for different lithologies. The self-organizing map is an intriguing concept, which allows to establish a certain classification behavior by unsupervised learning. We developed a work flow at GFZ, which includes preparation of data, application of learning rules, the segmentation of the trained map by adopting image processing techniques, and employment of knowledge. Applications are shown from geothermal exploration projects in the Northeast German basin and in Indonesia, where different geophysical models from the same study area were combined for a joint lithological interpretation.