Ghana airborne geophysics project in the Volta and Keta Basin : BGS final report

This report describes the work undertaken by BGS between November 2006 and March 2009 in collaboration with Fugro Airborne Surveys Pty Ltd on an airborne geophysical survey and ground reconnaissance mapping of the Volta River and Keta Basins, Ghana. The project was supported by the EU as part of the Mining Sector Support Programme, Project Number 8ACP GH 027/13. The initial contract duration was three years, but this was extended by five months to account for acquisition of gravity data by another project. Some parts of Ghana have been airborne surveyed as part of the Mining Sector Development and Environmental Project, co-funded by the World Bank and the Nordic Development Fund, but no work was carried out on the Volta River and Keta basins, which together form a major portion of the Ghanaian territory. The approximate areas covered by the surveys are estimated at 98,000 km² for the satellite imagery and the airborne geophysics, except for the Time Domain Electromagnetic (TDEM) survey which was limited to 60,000 km². The main beneficiary of this project is the Geological Survey Department, GSD. The work enhanced its geological infrastructure and its personnel received hands-on training on modern geological mapping technology. Indirect beneficiaries were the mining and exploration companies that can follow up the reconnaissance work with detailed exploration work. The project was conducted in five phases, and this document reports on the BGS input to Phase 1, 4 and 5, with no inputs required in Phases 2 and 3: • Phase1: geological outline through Radar and optical satellite imageries. • Phase 2: airborne geophysical survey over the two basins for magnetics and Gamma Ray spectrometry (Fugro survey). • Phase 3: airborne electromagnetic and magnetic geophysical survey of specific areas, following the completion and interpretation of phase 2, using fixed wing time domain technology (Fugro survey). • Phase 4: interpretation of the combined geology and geophysics. • Phase 5: production of factual and interpretation maps. The full list of BGS products is outlined in Table 1 below, while Jordan et al. (2006) describe the products delivered on schedule in Phase 1

Earthquake source parameters of the 2009 Mw 7.8 Fiordland (New Zealand) earthquake from L-band InSAR observations

The 2009 MW7.8 Fiordland (New Zealand) earthquake is the largest to have occurred in New Zealand since the 1931 Mw 7.8 Hawke’s Bay earthquake, 1 000 km to the northwest. In this paper two tracks of ALOS PALSAR interferograms (one ascending and one descending) are used to determine fault geometry and slip distribution of this large earthquake. Modeling the event as dislocation in an elastic half-space suggests that the earthquake resulted from slip on a SSW-NNE orientated thrust fault that is associated with the subduction between the Pacific and Australian Plates, with oblique displacement of up to 6.3 m. This finding is consistent with the preliminary studies undertaken by the USGS using seismic data

Stability margins and model-free control: A first look

We show that the open-loop transfer functions and the stability margins may be defined within the recent model-free control setting. Several convincing computer experiments are presented including one which studies the robustness with respect to delays.Comment: 13th European Control Conference, Strasbourg : France (2014

Cross-layer system reliability assessment framework for hardware faults

System reliability estimation during early design phases facilitates informed decisions for the integration of effective protection mechanisms against different classes of hardware faults. When not all system abstraction layers (technology, circuit, microarchitecture, software) are factored in such an estimation model, the delivered reliability reports must be excessively pessimistic and thus lead to unacceptably expensive, over-designed systems. We propose a scalable, cross-layer methodology and supporting suite of tools for accurate but fast estimations of computing systems reliability. The backbone of the methodology is a component-based Bayesian model, which effectively calculates system reliability based on the masking probabilities of individual hardware and software components considering their complex interactions. Our detailed experimental evaluation for different technologies, microarchitectures, and benchmarks demonstrates that the proposed model delivers very accurate reliability estimations (FIT rates) compared to statistically significant but slow fault injection campaigns at the microarchitecture level.Peer ReviewedPostprint (author's final draft