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Applied Sciences
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Land Subsidence: Monitoring, Prediction and Modeling - 2nd Edition
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Land Subsidence: Monitoring, Prediction and Modeling - 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 28 September 2024 | Viewed by 2103

Special Issue Editors

Prof. Dr. Ryszard Hejmanowski

E-Mail Website
Guest Editor
Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology, 30-059 Cracow, Poland
Interests: prediction (modeling) of land subsidence for the hard coal, copper ore, salt, gas and oil deposits; risk assessment and mitigation on transformed terrains; planning of the surveying systems to subsidence monitoring; IT systems developing (GIS-based) for building damage risk assessment and management on human transformed areas; mining seismicity and its influence on the terrain movements
Special Issues, Collections and Topics in MDPI journals
Dr. Pietro Teatini

E-Mail Website
Guest Editor
Department of Civil, Environmental and Architectural Engineering (DICEA), University of Padova, Padova, Italy
Interests: modelling geomechanical issues related to fluid withdrawal/injection from/into the subsurface; land subsidence due to aquifer overexploitation and production of hydrocarbon reservoirs; land uplift caused by fluid injection (aquifer recharge, underground gas storage, CO2 geologic sequestration); stress/strain analyses for safety evaluations (induced seismicity, aseismic earth fissuring accompanying land subsidence); modelling land subsidence due to peat oxidation and natural consolidation in deltas and wetlands; use of land subsidence measurements (integrating levelling, GPS, SAR interferometry), together with deformation at depth (borehole extensometers, well-logs) to characterize the geomechanical properties and calibrate the numerical models

Special Issue Information

Dear Colleagues,

Recently, land subsidence has become one of the important risk factors. Taking into consideration global warming and sea level rise, many regions of the world, large cities, and land users will be affected by the changes. In many of those areas, the land has subsided due to water pumping, gas and oil extraction, soft soils or peat compaction, and additional building load. On the other hand, there are terrains where the mining of raw materials is or was lately active. Mining is one of the most important factors of subsidence, sinkholes, and other related damage. It can affect buildings and infrastructure, threatening and decreasing quality of life. In any area transformed by human activity, the ground movements should also be considered. New ideas in modeling approach development, rock mechanics, and civil engineering have emerged in many countries. Novel measurement technics, sensors, and the expanding availability of remote sensing data have created new opportunities in relation to the monitoring of land subsidence.

This Special Issue of Applied Sciences is intended for specialists and an interdisciplinary audience, and covers recent advances in the following topics:

  • Land subsidence innovative monitoring technologies and untypical case studies;
  • Prediction of land subsidence: case studies for different kind of raw materials;
  • Modeling: new and improved approaches, parametrization, accuracy, and reliability.

Prof. Dr. Ryszard Hejmanowski
Dr. Pietro Teatini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land subsidence
  • modeling
  • uplift
  • sea-level rise
  • rock mechanics
  • monitoring
  • geodesy
  • remote sensing
  • GIS
  • risk assessment
  • prediction

Published Papers (2 papers)

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Research

16 pages, 4481 KiB  
Article
Monitoring Ground Displacement in Mining Areas with Time-Series Interferometric Synthetic Aperture Radar by Integrating Persistent Scatterer/Slowly Decoherent Filtering Phase/Distributed Scatterer Approaches Based on Signal-to-Noise Ratio
by Zhiwei Wang, Wenhui Li, Yue Zhao, Aihui Jiang, Tonglong Zhao, Qiuying Guo, Wanqiu Li, Yang Chen and Xiaofang Ren
Appl. Sci. 2023, 13(15), 8695; https://doi.org/10.3390/app13158695 - 27 Jul 2023
Cited by 1 | Viewed by 911
Abstract
During the interferometric synthetic aperture radar (InSAR)-based ground displacement monitoring in mining areas, the overlying land is mainly covered by low vegetation and arable land, which makes interferograms acquired by InSAR techniques easily susceptible to decorrelation, resulting in the quantity and density of [...] Read more.
During the interferometric synthetic aperture radar (InSAR)-based ground displacement monitoring in mining areas, the overlying land is mainly covered by low vegetation and arable land, which makes interferograms acquired by InSAR techniques easily susceptible to decorrelation, resulting in the quantity and density of highly coherent points (CPs) are not enough to reflect the spatial location and spatio-temporal evolution process of ground displacement, which is hardly meeting requirements of high-precision ground displacement monitoring. In this study, we developed an approach for monitoring ground displacement in mining areas by integrating Persistent Scatterer (PS), Slowly Decoherent Filtering Phase (SDF), and Distributed Scatterer (DS) based on signal-to-noise ratio (SNR) to increase the spatial density of CPs. A case study based on a mining area in Heze was carried out to verify the reliability and feasibility of the proposed method in practical applications. Results showed that there were four significant displacement areas in the study area and the quantity of CPs acquired by the proposed method was maximum 6.7 times that of conventional PS-InSAR technique and maximum 2.3 times that of SBAS-InSAR technique. The density of CPs acquired by the proposed method increased significantly. The acquired ground displacement information of the study area was presented in more detail. Moreover, the monitoring results were highly consistent with ground displacement results extracted by PS-InSAR and SBAS-InSAR methods in terms of displacement trends and magnitudes. Full article
(This article belongs to the Special Issue Land Subsidence: Monitoring, Prediction and Modeling - 2nd Edition)
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20 pages, 5775 KiB  
Article
Dynamic Prediction Model for Progressive Surface Subsidence Based on MMF Time Function
by Bang Zhou, Yueguan Yan and Jianrong Kang
Appl. Sci. 2023, 13(14), 8066; https://doi.org/10.3390/app13148066 - 10 Jul 2023
Cited by 1 | Viewed by 860
Abstract
It is imperative to timely and accurately predict the progressive surface subsidence caused by coal mining in the context of precision coal mining. However, the existing dynamic prediction methods that use time functions still have limitations, especially in the description of the moments [...] Read more.
It is imperative to timely and accurately predict the progressive surface subsidence caused by coal mining in the context of precision coal mining. However, the existing dynamic prediction methods that use time functions still have limitations, especially in the description of the moments of initiation and maximum subsidence velocity, which hinder their wide application. In this study, we proposed the MMF (Morgan–Mercer–Flodin) time function for predicting progressive surface subsidence based on the model assumptions and formula derivations. MMF time function can resolve the limitations in the description of the moments of initiation and maximum subsidence velocity perfectly. Afterward, we established the dynamic prediction model by combining the probability integral method with the MMF time function. Finally, using the measured subsidence data of working panel 22101 as an example, the accuracy and reliability of the dynamic prediction model was verified. The average RMSE and average relative RMSE (RRMSE) of prediction progressive subsidence using MMF time function are 46.65 mm and 4.63%, respectively. The accuracy is optimal compared with other time functions (for the average RMSE, Logistic time function is 80.57 mm, Gompertz time function is 79.77 mm, and Weibull time function is 90.61 mm; for the average RRMSE, Logistic time function is 7.66%, Gompertz time function is 7.73%, and Weibull time function is 8.62%). The results show that the method proposed in this paper can fully meet the requirements of practical engineering applications, achieve accurate dynamic prediction during the coal mining process, and provide good guidance for surface deformation and building protection. Full article
(This article belongs to the Special Issue Land Subsidence: Monitoring, Prediction and Modeling - 2nd Edition)
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