The Sentinel-1 constellation provides an effective new radar instrument with a short revisit time of six days for the monitoring of intensive mining surface deformations. Our goal is to investigate in detail and to bring new comprehension of the mine life cycle. The dynamics of mining, especially in the case of horizontally evolving longwall technology, exhibit rapid surface changes. We use the classical approach of differential radar interferometry (DInSAR) with short temporal baselines (six days), which results in deformation maps with a low decorrelation between the satellite images. For the same time intervals, we compare the radar results with prediction models based on the Knothe–Budryk theory for mining subsidence. The validation of the results with ground levelling measurements reveals a high level of resemblance of the DInSAR subsidence maps (−0.04 m bias with respect to the levelling). On the other hand, aside from the explicable exaggeration, the location of the subsidence trough needs improvement in the forecasted deformations (0.2 km shift in location, a deformation velocity four times higher than in DInSAR). In addition, a time lag between DInSAR (compatible with extraction) and prediction is revealed. The model improvement can be achieved by including the DInSAR results in the elaboration of the model parameters.

The 2008 M_w = 6.4 Movri earthquake ruptured a NNE right lateral strike-slip fault about 30 km south of the city of Patras. Although some strike-slip activity on minor faults was known, there was no tectonic evidence of large scale NS striking fault and such a large event was not anticipated. Following the event, a network of six stations was installed for 4 months in the epicentral area in order to monitor aftershocks and in particular the northern part of the rupture area closest to the city of Patras. We combine these new aftershock observations with GPS measurements of an already existing geodetic network in the area performed just after the earthquake, as well as with SAR interferograms, together with already published source studies, in order to refine already proposed models of this event. The combined data set allows defining much more accurately the lateral and vertical limits of the rupture. Its length inferred from geodesy is ∼15 km and its modelled upper edge ∼17 km. The seismic moment then constrains the lower edge to coincide, within a few kilometres, with the Moho interface. The absence of seismicity in the shallow crust above the co-seismic fault is interpreted as a result of the decoupling effect of possible presence of salt layers above the rupture area, near 14 to 16 km in depth, which favours our interpretation of an immature strike-slip fault system, compatible with the absence of surface ruptures. The immature character of this large crustal fault is further suggested by the high variability of focal mechanisms and of fault geometries deduced from aftershock clusters, in the strike direction. Its geometry and mechanism is consistent with the crustal shear, striking NNE, revealed by GPS in this region. This shear and faulting activity might be generated by the differential slip rate on the subduction interface, 50 km to the south, leading to a north-northeastward propagating strike-slip fault zone. The wide extension of the aftershock distribution forming a NNE alignment, beyond the rupture area towards the north, suggests a localization process of the shear strain, which could be the preliminary stage of fault propagation further to the NNE. An alternative speculative model for this regional stress could be the existence of a well-developed NNE striking shear zone within the uppermost mantle, marking at depth the southward propagation of the northern branch of the North Anatolian fault. Both models may not be exclusive of each other, and in fact their sources may be mechanically interdependent.

On 29 January 2019, the collapse of a mine roof resulted in a significant surface deformation and generated a tremor with a magnitude of 4.6 in Rudna Mine, Poland. This study combines the seismological and geodetic monitoring of the event. Data from local and regional seismological networks were used to estimate the mechanism of the source and the ground motion caused by the earthquake. Global Navigation Satellite System data, collected at 10 Hz, and processed as a long-term time-series of daily coordinates solutions and short-term high frequency oscillations, are in good agreement with the seismological outputs, having detected several more tremors. The range and dynamics of the deformed surface area were monitored using satellite radar techniques for slow and fast motion detection. The radar data revealed that a 2-km² area was affected in the six days after the collapse and that there was an increase in the post-event rate of subsidence.

The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition.