Sedimentary rocks in Bequerel crater: origin as polar layered deposits during high obliquity

Abstract not available

Rapid methods of landslide hazard mapping : Papua New Guinea case study

A landslide hazard probability map can help planners (1) prepare for, and/or mitigate against, the effects of landsliding on communities and infrastructure, and (2) avoid or minimise the risks associated with new developments. The aims of the project were to establish, by means of studies in a few test areas, a generic method by which remote sensing and data analysis using a geographic information system (GIS) could provide a provisional landslide hazard zonation map. The provision of basic hazard information is an underpinning theme of the United Nations International Decade for Natural Disaster Reduction (IDNDR). It is an essential requirement for disaster preparedness and mitigation planning. This report forms part of BGS project 92/7 (R5554) ‘Rapid assessment of landslip hazards’ carried out under the ODA/BGS Technology Development and Research Programme as part of the British Government’s provision of aid to developing countries. It provides a detailed technical account of work undertaken in a test area in the highlands of Papua New Guinea (PNG) in collaboration with the Geological Survey Division. The study represents a demonstration of a methodology that is applicable to many developing countries. The underlying principle is that relationships between past landsliding events, interpreted from remote sensing, and factors such as the geology, relief, soils etc. provide the basis for modelling where future landslides are most likely to occur. This is achieved using a GIS by ‘weighting’ each class of each variable (e.g. each lithology ‘class’ of the variable ‘geology’) according to the proportion of landslides occurring within it compared to the regional average. Combinations of variables, produced by summing the weights in individual classes, provide ‘models’ of landslide probability. The approach is empirical but has the advantage of potentially being able to provide regional scale hazard maps over large areas quickly and cheaply; this cannot be achieved using conventional ground-based geotechnical methods. In PNG, landslides are usually triggered by earthquakes or intense rain storms. Tectonic instability and the extreme ruggedness of the terrain make the highlands very susceptible to landsliding, but the extent to which regional factors influence the distribution and severity of landsliding is uncertain. The report discusses the remote sensing and GIS methodology, and describes the results of the pilot study over an area of approximately 4 500 km2 in the Kaiapit/Saidor districts of the Finisterre mountain range. The landslide model uses geology, elevation, slope angle, lineaments and catchments as inputs. The resulting provisional landslide hazard zonation map, divided into 5 zones of landslide hazard probability, suggests that regional controls on landslide occurrence do exist and are significant. It is recommended that consideration be given in PNG to implementing the techniques as part of a national strategic plan for landslide hazard zonation mapping

Validation and intercomparison of persistent scatterers interferometry : PSIC4 project results

This article presents the main results of the Persistent Scatterer Interferometry Codes Cross Comparison and Certification for long term differential interferometry (PSIC4) project. The project was based on the validation of the PSI (Persistent Scatterer Interferometry) data with respect to levelling data on a subsiding mining area near Gardanne, in the South of France. Eight PSI participant teams processed the SAR data without any a priori information, as a blind test. Intercomparison of the different teams' results was then carried out in order to assess any similarities and discrepancies. The subsidence velocity intercomparison results obtained from the PSI data showed a standard deviation between 0.6 and 1.9 mm/year between the teams. The velocity validation against rates measured on the ground showed a standard deviation between 5 and 7 mm/year. A comparison of the PSI time series and levelling time series shows that if the displacement is larger than about 2 cm in between two consecutive SAR-images, PS-InSAR starts to seriously deviate from the levelling time series. Non-linear deformation rates up to several cm/year appear to be the main reason for these reduced performances, as no prior information was used to adjust the processing parameters. Under such testing conditions and without good ground-truth information, the phase-unwrapping errors for this type of work are a major issue. This point illustrates the importance of having ground truth information and a strong interaction with the end-user of the data, in order to properly understand the type and speed of the deformation that is to be measured, and thus determine the applicability of the technique