The formation of cover collapse sinkholes in North of Hamedan, Iran

Heidari, Mojtaba; Khanlari, Gholamreza; Beydokhti, Alireza Taleb; Momeni, Aliakbar

doi:10.1016/j.geomorph.2011.04.025

Cited by 54 publications

(23 citation statements)

References 16 publications

(18 reference statements)

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“…The cover-collapse sinkholes of the Hamedan plains, Zagros Mountains, Iran, mainly induced by aquifer over-exploitation and the consequent water table decline, reach significantly larger dimensions. In this alluvial karst setting the cavernous limestone is overlain by thick and cohesive alluvium that reaches more than 100 m in thickness (Heidari et al, 2011;Taheri et al, 2015). In the Ambal salt pillow, Zagros…”

Section: Origin and Characteristics Of The Sinkholes In The Smcvmentioning

confidence: 99%

Sinkholes in hypogene versus epigene karst systems, illustrated with the hypogene gypsum karst of the Sant Miquel de Campmajor Valley, NE Spain

et al. 2019

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The Garrotxa-Banyoles artesian aquifer system in NE Spain includes zones with epigene and hypogene evaporite karst and sinkhole development. The sinkhole fields related to hypogene evaporite dissolution are associated with the discharge zones of the aquifer, where groundwater from a confined limestone rises traversing an overlying gypsum formation capped by thick marls. This work analyses the sinkhole field developed in the Sant Miquel de Campmajor Valley (SMCV), a discharge area of the aquifer, where a cartographic inventory including 94 sinkholes has been produced. Sinkholes in this hypogene gypsum karst are mainly large caprock-collapse sinkholes rooted in deep-seated cavities generated by interstratal hypogene karstification, as corroborated by an electrical resistivity imaging (ERI) survey. They show a nonclustered distribution within the sinkhole field and the variations in size and density across the area are mainly controlled by the thickness of the marl caprock. The magnitude and frequency relationships of the sinkholes can be modelled with a high goodness of fit with a log-normal function, like in other regions of the world. However, in this deep-seated hypogene system the depressions show much larger dimensions. ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P TPotential differences between the sinkholes developed in hypogene and epigene karst environments are discussed, including their distribution with respect to the groundwater flow system, their spatial patterns and dimensions.

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Section: Origin and Characteristics Of The Sinkholes In The Smcvmentioning

confidence: 99%

Sinkholes in hypogene versus epigene karst systems, illustrated with the hypogene gypsum karst of the Sant Miquel de Campmajor Valley, NE Spain

et al. 2019

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“…BL is a critical factor for the distribution of sinkholes, because a prerequisite for their formation is the presence of soluble bedrock. Nonetheless, most of the sinkholes occur in areas extensively covered by Quaternary alluvium, where there is significant uncertainty about the distribution of the different lithologies that form the rockhead (Heidari et al, ). Five lithotypes have been differentiated in the BL map: schist‐shale, marl, limestone, marly limestone, and conglomerate sandstone (Figure f).…”

Section: Methodsmentioning

confidence: 99%

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

Taheri¹,

Shahabi

Chapi

et al. 2019

Land Degrad Dev

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Land degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human‐induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances. Several susceptibility models were developed with a training sample of sinkholes, a number of conditioning factors, and four different statistical approaches: naïve Bayes, Bayes net (BN), logistic regression, and Bayesian logistic regression. Ten conditioning factors were initially considered. Factors with negligible contribution to the quality of predictions, according to the information gain ratio technique, were discarded for the development of the final models. The validation of susceptibility models, performed using different statistical indices and receiver operating characteristic curves, revealed that the BN model has the highest prediction capability in the study area. This model provides reliable predictions on the future distribution of sinkholes, which can be used by watershed and land use managers for designing hazard and land‐degradation mitigation plans.

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“…In contrast, cover-collapse sinkholes can develop rapidly and may not overlie a large cave. They may form during floods (Zhou and Lei, 2018) during which water flow and transport capacity in karstic voids may reach a maximum, or after groundwater extraction (Heidari et al, 2011;Khanlari et al, 2012). Water under pressure may liquify clay in overburden deposits above the karst contact, thereby undercutting the ground surface until roof failure occurs (Jia et al, 2018;Soliman et al 2018).…”

Section: Introductionmentioning

confidence: 99%

A reactivated cave system induces rapidly developing cover-collapse sinkholes in Tasmania, Australia

Burke¹,

Slee²,

McIntosh³

et al. 2020

JCKS

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The formation of cover collapse sinkholes in North of Hamedan, Iran

Cited by 54 publications

References 16 publications

Sinkholes in hypogene versus epigene karst systems, illustrated with the hypogene gypsum karst of the Sant Miquel de Campmajor Valley, NE Spain

Sinkholes in hypogene versus epigene karst systems, illustrated with the hypogene gypsum karst of the Sant Miquel de Campmajor Valley, NE Spain

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

A reactivated cave system induces rapidly developing cover-collapse sinkholes in Tasmania, Australia

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