Geochronological studies in northern Wedel Jarlsberg Land, southwestern Svalbard (Norway), showed that the Tonian (c. 950 Ma) igneous rocks were subjected to metamorphism during the Torellian (c. 640 Ma) and early Caledonian (470–460 Ma) events. Predominant augen gneisses, derived from a Tonian protolith, are intercalated in that area, with schists comprising two distinct metamorphic mineral assemblages. The M1 (Torellian) assemblage containing garnet-I + quartz + plagioclase-I + biotite-I + muscovite-I was formed under amphibolite-facies conditions at c. 550–600 °C and 5–8 kbar (1 kbar = 100 MPa). The M2 (Caledonian) assemblage comprising garnet-II + quartz + plagioclase-II + biotite-II + muscovite-II + zoisite + chlorite crystallized at c. 500–550 °C and 9–12 kbar, corresponding to epidote–amphibolite facies conditions. The M2 mineral assemblage constitutes the pervasive Caledonian fabric of the schists that was subsequently reactivated in a left-lateral strike-slip shear regime. The subsequent c. 70° clockwise rotation of the original structure to its present position was caused by a large-scale passive rotation during the Paleogene Eurekan orogeny. The new pressure–temperature estimates suggest that metamorphic basement in the study area was consolidated during the Torellian middle-grade event and then overprinted by Caledonian moderate- to high-pressure subduction-related metamorphism. A following sinistral shear zone assembled the present structure of basement units. Our results pose a question about the possible extent of Torellian precursor to the Caledonian basement across the High Arctic and the scale of its subsequent involvement in early Caledonian subduction. In conjunction with previous studies, the results suggest a possible correlation between southwestern Spitsbergen and the Pearya Terrane in Ellesmere Island.

Subsidence, especially in populated areas, is becoming a threat to human life and property. Monitoring and analyzing the effects of subsidence over large areas using in situ measurements is difficult and depends on the size of the subsidence area and its location. It is also time-consuming and costly. A far better solution that has been used in recent years is Differential Interferometry Synthetic Aperture Radar (DInSAR) monitoring. It allows the monitoring of land deformations in large areas with high accuracy and very good spatial and temporal resolution. However, the analysis of SAR images is time-consuming and involves an expert who can easily overlook certain details. Therefore, it is essential, especially in the case of early warning systems, to prepare tools capable of identifying and monitoring subsidence in interferograms. This article presents a study on automated detection and monitoring of subsidence troughs using deep-transfer learning. The area studied is the Upper Silesian Coal Basin (southern Poland). Marked by intensive coal mining, it is particularly prone to subsidence of various types. Additionally, the results of trough detection obtained with the use of convolutional neural networks were compared with the results obtained with the Hough transform and the circlet transform.

This article presents the results of automatic detection of subsidence troughs in synthetic aperture radar (SAR) interferograms. The detection of subsidence troughs is based on the circlet transform, which is able to detect features with circular shapes. Compared to other methods of detecting circles, the circular transform takes into account the finite data frequency. Moreover, the search shape is not limited to a circle but identified on the basis of a certain width. This is especially important in the case of detection of subsidence troughs whose shapes may not be similar to circles or ellipses but to their fragments. The transformation works directly on the image gradient; it does not require further binary segmentation or edge detection as in the case of other methods, e.g., the Hough transform. The entire processing process can be automated to save time and increase reliability compared to traditional methods. The proposed automatic detection method was tested on a differential interferogram that was generated based on Sentinel-1A SAR images of the Upper Silesian Coal Basin area. The test carried out showed that the proposed method is 20% more effective in detecting troughs that than the method using Hough transform.