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12.23 Parriaux A., Turberg P., Kalbermatten M., Golay F., Lance J.-M.: The geotype concept to develop GIS oriented analysis in engineering geology applications 12.24 Pera S. : Hydrogeology and geochemical characteristics of groundwater in a porous aquifer connected with two karst systems, in Southern Switzerland. 12.25 Sartori M., Ornstein P., Schreiber L. : GIS modelling: a first step toward 3D geology with the “Sion” map 12.26 Schreiber L., Ornstein P., Sartori M., Kühni A.: TOOLMAP – ‘SION’ method: development of a new GIS Framework for digital geological mapping 12.27 Skaloud J., Schaer P., Tomé P., Stebler Y.: Real-Time Mapping and Monitoring Capability of Geological Features by Airborne Laser Scanning 12.28 Villiger A., Heller O., Geiger A., Kahle H.-G., Brockmann E. : Estimation of local tectonic movements using continuous GNSS network 12.1 Detection of gross-errors into gravity data of IRAN by Kriging approach Abdollahzadeh Makan*, Najafi Alamdari Mehdi * *K.N.Toosi University of Technology, Faculty of Geodesy and Geomatics Engineering. 1364 Valiasr Street,Mirdaamaad Intersection,Tehran,Iran (m_abdollahzadeh@sina.kntu.ac.ir) The main aim of this research is to refine Iranian gravity data based on linear prediction method. Iranian gravity dataset has been observed during a long time by different gravimeters and methods. Various kinds of systematic errors have affected the observations due to uncertainty of reference frames and equipments. Hence a refinement process of gravity data seems to be necessary. Spatially correlated data such as gravity anomaly can be validated first by geographical techniques. It should be low pass filtered (trend removal) and then the map of the filtered data must be inspected. These are done by computing a high degree geopotential model, e.g. EIGEN04C, and RTM topography effect and then removing from the free air gravity anomaly. A gross error shows up as a chimney or volcano in the countered map. In This step about 1403 points were flagged as outliers. The size of current gravity database makes it virtually impossible to check and correct the remaining suspected errors. Hence an automatic, or semi-automatic computer based method is needed. Correlations of spatially distributed database are used to detect gross errors. If a value differs more than usual from its neighbouring values, it may be a gross error. This strategy based on simple Kriging approach was used with great success. As a result a number of 490 points were eliminated in the numerical process. The map of the outlier points is presented in figure1. As one can see, there are many blunders in Iranian gravity database. Most of them belong to NCC (Iranian National Carthogeraphic Center) observations, which have been measured along the precise leveling network. These gravity points should be investigated and may need to be remeasured. The new adjustment process seems to be necessary for NCC observed points. REFERENCES Bureau Gravimetrique Internation, 1989: Workshop on gravity data validation, Bulletin d‘ information, No.65, 89-142. Cressie, N. A. C. 1991: Statistics for Spatial Data, John Wiley and Sons, Inc., New York. Forsberg, R. 1984: A study of terrain reduction, Density anomalies and geophysical inversion methods in gravity field modelling, Rep. 355, Dept Geod Sci, Ohio state Univ, Columbus. Journel A.G. & Huijbrects Ch.J 1978: Mining Geostatistics, Academic Press, London, New York, San Francisco. Tscherning, C.C. 1990b : A strategy for gross-error detection in satellite altimeter data applied in the Baltic-Sea area for enhanced geoid and gravity determination. Proc. 11th General meeting Nordic Geodetic Commission, Copenhagen, 90- 106. 2 Symposium 12: Data acquisition, Geo-processing, GIS, digital mapping and 3D visualisation
2 8 Symposium 12: Data acquisition, Geo-processing, GIS, digital mapping and 3D visualisation Figure1. Detected outlier gravity points (The red triangles) 12.2 The Sensor Observation Service (SOS) Milan Antonovic SUPSI - Institute of Earth Sciences, CH-6952 Canobbio (milan.antonovic@supsi.ch) The SOS (Sensor Observation Service) aggregates readings from live, in-situ and remote sensors. The service provides an interface to make sensors and sensor data archives accessible via an interoperable web based interface. The SOS is part of the OGC’s SWE (Sensor Web Enablement) group of specifications. The SWE Working Group, is establishing the interfaces and protocols that will enable a “Sensor Web” through which applications and services will be able to access sensors of all types over the Web. This presentation introduce the main characteristic of all the SWE specifications and then explain extensively the SOS standard. The SWE group of specifications are: 1. Observations & Measurements (O&M) 2. Sensor Model Language (SensorML) 3. Transducer Markup Language (TML) 4. Sensor Observation Service (SOS) 5. Sensor Planning Service (SPS) 6. Sensor Alert Service (SAS) 7. Web Notification Service (WNS) REFERENCES Arthur Na (IRIS Corp.), Mark Priest (3eTI) 2007: Sensor Observation Service Mike Botts, 2003: Sensor Model Language (SensorML): XML-Based Language for In-situ and Remote Sensors Mike Botts, George Percivall, Carl Reed, John Davidson, 2006: OGC® Sensor Web Enablement: Overview And High Level Architecture. Stefan Falke, 2006: Exploring the Sensor Observation Service (SOS) for Multi-dimensional Atmospheric Science Data
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Abstract Volume 6 th Swiss Geoscien
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2 Plenary Session
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