Terracing and cluster analysis – new insights in potential field data prior to modelling

• Main Authors: Strehlau, Ronja, Götze, Hans-Jürgen, Ebbing, Jörg, Holzrichter, Nils
• Format: Conference Object
• Language: English
• Published: 2022
• Subjects: ddc:550; Clustering; terracing; gravity field; Liguro-Provencial Basin; Norway; Karasjok; Northern Norway
• Online Access: https://doi.org/10.23689/fidgeo-5315; http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9659

Geological maps are complex to produce through intensive and expensive field studies. Comparisons of geophysical data with geological conditions are difficult and often only qualitatively possible. The following work therefore examines an automated procedure to better reconcile this information. For this purpose, the terracing method, and a cluster analysis of potential field (gravity and magnetic field) and petrophysical data from the Karasjok and Ligurian Sea regions are used to interpret this geophysical measurements in a geological way. Two different tectonic regions were selected: (1) The Karasjok region is located in Northern Norway, where the Karasjok Greenstone Belt (KGB) dominates geological settings, consisting of abundant ultramafic intrusions, komatiites, gabbroic intrusions, amphibolites and migmatites. (2) The Ligurian-Provençal basin, part of the Western Mediterranean Sea, which is located between the French-Italian coastline and the island of Corsica. Geologically the area is characterised by the spreading zone in the Western Mediterranean. The high-resolution Airborne Gravity Gradient Survey and aeromagnetic datasets of the Karasjok region cover an area of 20 km x 30 km with a data resolution of 50 m. The dataset of the Ligurian basin cover a much larger area with the resolution of 5 km. Data constraints come from former LOBSTER and LISA campaigns and a study in the research group at CAU Kiel, new compilation of the AlpArray Gravity Research Group (AAGRG), besides data of the ICGEM Potsdam (disturbance) and the GOCE mission. By aid of the terracing algorithm, the boundaries of the anomalies are to be sharpened and regions with constant field amplitude were generated. For this purpose, a shape index-based algorithm was applied, which uses the shape index calculated at each field point to grade the function. Through an iterative process and the variation of parameters, the terracing result is refined. The resulting data sets are then further processed using a cluster analysis method. Here, the ...