Investigation of Mining Subsidence Prediction Under Tectonic Influences

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@phdthesis{abb92a123bb441478a59cb79fea12eca,
title = "Investigation of Mining Subsidence Prediction Under Tectonic Influences",
abstract = "This dissertation addresses the challenge of predicting human-induced subsidence in tectonic settings. The study focuses on the non-symmetric and shape-defying nature of subsidence troughs in tectonic regions, which deviates from conventional symmetric models. The aim of the dissertation is to improve the accuracy of subsidence prediction by incorporating horizontal stress effects into empirical methods. Through a combination of numerical investigations and empirical modelling, the research reveals stress-induced patterns in subsidence profiles. The developed model, based on various concepts, successfully incorporates asymmetry and shape deviation, resulting in significantly improved prediction accuracy. Application of the model to a real subsidence case in a salt cavern shows a 30% improvement in prediction (based on mean squared error comparison with classical solution). This new solution covers subsidence profile patterns not previously considered by empirical models. ",
keywords = "Senkung unter tektonischen Bedingungen, Neue Methode zur Subsidenzvorhersage, Asymmetrie, Numerische Modellierung, Subsidence under tectonic conditions, New subsidence prediction method, Asymmetry, Shape deviation, Numerical modeling, Subsidence engineering",
author = "Aleksandra Babaryka",
year = "2023",
month = dec,
day = "12",
language = "English",

}

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TY - BOOK

T1 - Investigation of Mining Subsidence Prediction Under Tectonic Influences

AU - Babaryka, Aleksandra

PY - 2023/12/12

Y1 - 2023/12/12

N2 - This dissertation addresses the challenge of predicting human-induced subsidence in tectonic settings. The study focuses on the non-symmetric and shape-defying nature of subsidence troughs in tectonic regions, which deviates from conventional symmetric models. The aim of the dissertation is to improve the accuracy of subsidence prediction by incorporating horizontal stress effects into empirical methods. Through a combination of numerical investigations and empirical modelling, the research reveals stress-induced patterns in subsidence profiles. The developed model, based on various concepts, successfully incorporates asymmetry and shape deviation, resulting in significantly improved prediction accuracy. Application of the model to a real subsidence case in a salt cavern shows a 30% improvement in prediction (based on mean squared error comparison with classical solution). This new solution covers subsidence profile patterns not previously considered by empirical models.

AB - This dissertation addresses the challenge of predicting human-induced subsidence in tectonic settings. The study focuses on the non-symmetric and shape-defying nature of subsidence troughs in tectonic regions, which deviates from conventional symmetric models. The aim of the dissertation is to improve the accuracy of subsidence prediction by incorporating horizontal stress effects into empirical methods. Through a combination of numerical investigations and empirical modelling, the research reveals stress-induced patterns in subsidence profiles. The developed model, based on various concepts, successfully incorporates asymmetry and shape deviation, resulting in significantly improved prediction accuracy. Application of the model to a real subsidence case in a salt cavern shows a 30% improvement in prediction (based on mean squared error comparison with classical solution). This new solution covers subsidence profile patterns not previously considered by empirical models.

KW - Senkung unter tektonischen Bedingungen

KW - Neue Methode zur Subsidenzvorhersage

KW - Asymmetrie

KW - Numerische Modellierung

KW - Subsidence under tectonic conditions

KW - New subsidence prediction method

KW - Asymmetry

KW - Shape deviation

KW - Numerical modeling

KW - Subsidence engineering

M3 - Doctoral Thesis

ER -