A novel methodological approach for land subsidence prediction through data assimilation techniques

Gazzola, Laura; Ferronato, Massimiliano; Frigo, Matteo; Janna, Carlo; Teatini, Pietro; Zoccarato, Claudia; Antonelli, Massimo; Corradi, Anna; Dacome, Maria Carolina; Mantica, S.

doi:10.1007/s10596-021-10062-1

Cited by 10 publications

(8 citation statements)

References 55 publications

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“…However, land subsidence due to the compression of alluvial deposits has been monitored, documented and widely acknowledged for many years. As a result, remediation plans that allow for the long-term management of land subsidence in such prone areas have been developed [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Guzy

Witkowski

2021

Energies

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Land subsidence caused by groundwater withdrawal induced by mining is a relatively unknown phenomenon. This is primarily due to the small scale of such movements compared to the land subsidence caused by deposit extraction. Nonetheless, the environmental impact of drainage-related land subsidence remains underestimated. The research was carried out in the “Bogdanka” coal mine in Poland. First, the historical impact of mining on land subsidence and groundwater head changes was investigated. The outcomes of these studies were used to construct the influence method model. With field data, our model was successfully calibrated and validated. Finally, it was used for land subsidence estimation for 2030. As per the findings, the field of mining exploitation has the greatest land subsidence. In 2014, the maximum value of the phenomenon was 0.313 cm. However, this value will reach 0.364 m by 2030. The spatial extent of land subsidence caused by mining-induced drainage extends up to 20 km beyond the mining area’s boundaries. The presented model provided land subsidence patterns without the need for a complex numerical subsidence model. As a result, the method presented can be effectively used for land subsidence regulation plans considering the impact of mining on the aquifer system.

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Section: Introductionmentioning

confidence: 99%

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Guzy

Witkowski

2021

Energies

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“…Subsidence can either coincide with mining activities or be delayed in response to the time-dependent deformation of rocks. Several studies determined the rate of expected subsidence employing physical or numerical modeling and analysis (Whittaker 1989;Singh et al 1998;Alheib et al 2001;Ye et al 2016;Xie et al 2020;Gazzola et al 2021). In any case, to avoid possible environmental harm it is important to monitor areas subjected to underground mining activity.…”

Section: Introductionmentioning

confidence: 99%

Semi-real time systems for subsidence monitoring in areas affected by underground mining: the example of the Nuraxi-Figus coal district (Sardinia, Italy)

Ammirati

Martire

Bordicchia³

et al. 2022

Int J Coal Sci Technol

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Underground mining can produce subsidence, which can be coincident with mining activities or delayed in response to the time-dependent deformation of the rocks. Therefore, in these cases, it is essential to effectively monitor the soil deformations at different times during and after mining activity. In the present work, an integrated approach based on geotechnical numerical modeling and Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) method has been applied to detect, study and monitor the subsidence related to mining activity in the Nuraxi Figus coal district (Sardinia, Italy). Two datasets of high-resolution COSMO-Skymed images were acquired, respectively in two covering periods: from 2011 and 2014, and from 2013 to 2020. The A-DInSAR results show that the predominant displacement rates are located in correspondence with the panels. The cumulated satellite-based LoS displacements vary in the first period between − 130 and + 28 mm and − 293 and + 28.4 mm, while, during the second period between − 6.9 and + 1.6 mm and − 8.72 and + 4.33 mm in ascending and descending geometries, respectively. The geotechnical numerical model allowed to obtain a value for the maximum expected. By using the vertical and horizontal components it was possible to reconstruct the kinematics of the deformation considering three phases: pre-mining, syn-mining, and post-mining activity. The temporal evolution of displacements started during the mining extraction in 2011, achieved the major values in correspondence of post-mining operations, during the period from 2013 to 2014 and continued slowly until 2020. The near real-time monitoring system applied in this study proved to be very useful for detecting subsidence during the mining activity and the post-mining period.

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“…One of the methods used in data assimilation is the particle filter [1] or particle method [2] for static problems. Filtering and ensemble based methods have been applied to subsidence estimation [3,4] and [5] used ensemble smoother and ensemble smoother multiple data assimilation method to estimate geomechanical parameters of the subsurface. An important question in these applications is whether the ensemble spread is sufficiently large to ensure applicability of the method given the system complexity.…”

Section: Introductionmentioning

confidence: 99%

On spatially correlated observations in a particle method for subsidence estimation

Kim¹,

Vossepoel²

2022

Preprint

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The particle method is an ensemble-based data assimilation method for state- and parameter estimation in a quasi-static problem. We apply the particle method in two different experiments with models of increasing complexity. The first model, which calculates subsidence for a single observation point due to a single source of strain, considers uncorrelated parameters and observations. In the second model subsidence can be seen as a summation of subsidence contributions from multiple sources. A single source in the second model causes deformation over a region and the observations within this region will be correlated. Assimilating these correlated observations may trigger weight collapse. With synthetic tests we show in a model of subsidence with 50 independent parameters and spatially correlated observations a minimum of 10¹³ particles is required to have information in the posterior distribution identical to that in a model with 50 independent and spatially uncorrelated observations.Spatial correlations cause information loss which can be quantified with mutual information. With synthetic experiments we illustrate how stronger spatial correlation results in a lower information content in the posterior. This quantification underpins our finding that a larger ensemble size is required for the particle method to remain effective in the case of spatial correlation. We furthermore illustrate how this loss of information is reflected in the log likelihood, and how this depends on the number of parameters of the model. Based on the results of these experiments, we propose criteria to evaluate the required ensemble size for data assimilation of spatially correlated fields.

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A novel methodological approach for land subsidence prediction through data assimilation techniques

Cited by 10 publications

References 55 publications

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Semi-real time systems for subsidence monitoring in areas affected by underground mining: the example of the Nuraxi-Figus coal district (Sardinia, Italy)

On spatially correlated observations in a particle method for subsidence estimation

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