About the lab

We work on the new methods of land subsidence monitoring and prediction to better understand the rock mass and surface movements caused by natural and anthropogenic phenomena. Furthermore, we analyze the influence of land subsidence and risk on buildings and infrastructure for preventing and avoiding damages and to maintain the safety for people living in those areas. For those purposes, we use some modern tools like fuzzy logic, neural networks, and mathematical modelling. For monitoring of subsidence and deformation, we use the geodetic methods and remote sensing (InSAR). Welcome to our website at www.land-subsidence.com

Featured projects (7)

The goal of the project is to develop a new method to predict drainage-related land subsidence. This method will be based on deep learning and Satellite Radar Interferometry (InSAR). Methods of artificial intelligence (AI) allow replacement of complex specifications required to establish traditional models. Moreover, they have not been thoroughly used to solve the discussed issues yet. Furthermore, InSAR offers a monitoring system for land subsidence. By combining these two methods, a novel algorithm will be developed to allow modelling and examining the characteristics of land subsidence in conditions involving drainage of the rock mass. This project is funded by PRELUDIUM grant from the National Science Center in Poland. Principal investigator: Artur Guzy Supervisor: Agnieszka Malinowska www.watersubsidence.com
Report 1 No. G2017001 is supported by China’s Belt and Road initiative. There are three main goals of the project: 1. To deepen the knowledge about research done on mining subsidence in China and Poland, 2. To develop a novel method for the damage risk assessment for the buildings/pipelines in China, 3. To apply InSAR for ground movements detection and to establish parameters of the models applied in order to predict ground deformation.
The research aims a better understanding of the generation processes of discontinuous deformations on the surface in active and abandoned mining areas. Consequently, the following research thesis has been formulated: a new innovative model for predicting discontinuous deformation can be defined, based on artificial intelligence methods, multi-criteria analysis method and elements of data mining. The planned research in realize this thesis will a fuller comprehension of the phenomena accompanying the generation of this type of deformation, clarifying the details of the physics of void propagation processes, and ultimately create base for evaluating the respective risk. http://sinkholerisk.org/aim/
The goal of the project is to develop a methodology for measuring and analyzing methods that would enable to separate the dynamic phenomena from the others in terms of surface movements. This adjustment will increase the reliability of geodetic measurements carried out on mining terrains. The results of the research may also clarify the impact of mining tremors on the surface structures. Within the project, continuous telemetric measurements, measurements of the surface structures tilt, periodic measurements with the use of GPS (high frequency), and remote sensing measurements (InSAR) are planned. The analytical part of the project will be based on statistical calculations and spatial analysis (GIS).
The aim of the project is to define and build a model that will allow for damage risk assessment of buildings and linear objects located in areas influenced by surface deformation. The model will based on artificial intelligence and geographic information systems (GIS).

Featured research (16)

The gas transport infrastructure is frequently localized in areas subjected to anthropogenic movements and strains. The potential impact of such deformations on the gas pipeline in the aspect of its damaging can be properly assessed by, e.g. by predicting strains, taking into account the causes of terrain movement. On the other hand the hazard is also related to technological factors like design of the pipeline. The presented method is based on artificial intelligence methods allowing for evaluation of probability of failure risk in gas supply pipeline sections. The Mamdani fuzzy inference was used in the study. Uncertainty of variables characterizing the resistance of the gas pipeline and predicted continuous deformations of ground surface were accounted for in the model by using triangular-shaped membership functions. Based on the surface deformations and gas pipeline resistance and the inference model one can make prediction when the gas pipeline is hazarded. The proposed model can contribute to the protection, cost optimization of the designed pipelines and to the repairs of the existing gas pipelines Trial registration number and date of registration: COAL-D-21-00255, 27.09.2021
Land subsidence due to mining is primarily caused by the removal of a deposit from a rock mass and the formation of a post-mining void. This type of land subsidence damages surface and underground infrastructure and adversely affects the safety of surface users. Underground mining, however, is also associated with the drainage of rock layers, for both technological and safety reasons. When compared to the direct impact of deposit exploitation, mining-induced aquifer drainage typically causes one order of magnitude less land subsidence. However, the spatial extent of the depression cone, and hence the area of land subsidence caused by aquifer drainage frequently extends beyond mining boundaries. Despite this, the environmental impact of the phenomenon remains underestimated. For research on phenomena related to groundwater head variations in aquifer systems, a multidisciplinary approach is necessary. Until far, the methodologies used have mostly relied on models, the application of which required a comprehensive understanding of the properties of the rock mass deformation mechanism. Because of the problem's complexity and scarcity of data, the parametrization of the models utilized thus far, as well as the simulation results, contributed to considerable uncertainty. As a result, the study presented attempted to develop a reliable model of land subsidence caused by mining-induced drainage using minimum input data. The study was carried out in the Bogdanka underground coal mine, Poland. First, land subsidence due to the direct and indirect influence of mining was assessed. The prior research work on the determination of the spatio-temporal drop in groundwater head as a result of mine drainage was then investigated. The outcomes of these efforts were used to develop the Geographically Weighted Regression model (GWR). Using field data and the Monte-Carlo approach, the model was calibrated and validated. This allowed us to statistically determine the quality of the model parameters on a local level. Finally, without requiring a thorough numerical model, the GWR model provided land subsidence patterns based on the relationship between groundwater head variations as well as the spatial distribution and thickness of the drained aquifer. According to the findings, the mining field has the highest land subsidence and decrease in the groundwater head. The calculated subsidence bowl closely resembles the observed depression cone. Its shape, however, is also linked to local geological, hydrogeological, and mining conditions. Furthermore, the greatest values of land surface subsidence caused by mine drainage were less than 0.5 m. Importantly, the spatial extent of mine-induced land subsidence extends several kilometres beyond the boundary of the mining area. The results indicate that the GWR model can be used to successfully estimate the value of land subsidence caused by mining drainage. Furthermore, by implementing the Monte Carlo method in the process of determining the model parameters, it is possible to assess the quality of the prediction performed not only globally, but also locally. Therefore, our findings can pave the way for a more reliable assessment of the environmental impact of the mining-induced drainage considering the spatial variability of the phenomenon and its driving parameters.
Horizontal strains related to mining-induced subsidence may endanger infrastructure and surface users' safety. While directional horizontal strains should be well determined, appropriate solutions for a complete assessment of the terrain surface deformation field are still required. As a result, the presented study examined a new method for calculating horizontal strain tensor based on the decomposition of satellite radar interferometry (InSAR) observations into vertical and azi-muth look direction (ALD) displacements. Based on a geometric integral model, we tested our method on experimental data before applying it to an underground copper ore mine in Poland. In the case study, the displacement field was determined using the Multi-Temporal InSAR method on Sentinel-1 data. The model data relative error did not exceed 0.02 at σ = ±0.003. For the case study, land subsidence of up to −167 mm and ALD displacements ranging from −110 mm to +62 mm was obtained, whereas the extreme values of horizontal strains ranged from −0.52 mm/m to +0.36 mm/m at σ = ±0.050 mm/m. Our results demonstrate the high accuracy of the method in determining the horizontal strain tensor. As a result, the approach can broaden the assessment of the environmental impact of land subsidence worldwide.
The marine-terminating glaciers are one of the biggest contributors to global sea-level rise. Research on this aspect of the effects of global climate change is developing nowadays in several directions. One of them is the monitoring of glacier movements, especially with satellite data. In addition to well-known analyzes based on radar data from available satellites, the possibility of studying glacier displacements from new sensors, the so-called microsatellites need to be studied. The main purpose of the research was an evaluation of the possibility of applying new high-resolution ICEYE radar data to observe glacier motion. Stripmap High mode was used to obtain velocities for the Jakobshavn glacier with an Offset-Tracking method. Obtained results were compared with displacements obtained from the Sentinel-1 data.

Lab head

Ryszard Hejmanowski
  • Department of Mining Areas Protection, Geoinformatics and Mining Surveying
About Ryszard Hejmanowski
  • Ryszard Hejmanowski currently works at the Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology in Krakow/Poland. He does research in Petroleum Engineering, Mining Engineering and Civil Engineering. He is particulary interested in the prediction and modeling of land subsidence due to water, gas, oil extraction, salt- and copper mining. His research group: www.land-subsidence.com

Members (4)

Agnieszka A. Malinowska
  • AGH University of Science and Technology in Kraków
Wojciech T Witkowski
  • AGH University of Science and Technology in Kraków
Artur Guzy
  • AGH University of Science and Technology in Kraków
Magdalena Łucka
  • AGH University of Science and Technology in Kraków