This paper presents a methodology tuned to support the management of underground pipelines (sewer and aqueduct networks) in Rome, often threatened by the sudden formation of sinkholes related to the upward migration of existing underground cavities. The methodology integrates data coming from the assessment of susceptibility to sinkhole formation and the advanced processing of satellite-based SAR imagery. The former, performed through the multivariate logistic regression technique, relies on a detailed database of stratigraphic and other thematic (i.e. sinkhole inventory and density of underground cavities) information. A-DInSAR processing of satellite images, for which we developed on-purpose algorithms to filter only data relevant for the process under study, allowed us to provide density maps of subsiding reflectors whose likelihood to be precursors of sinkhole collapses is rated based on the integration with the susceptibility map. The procedure is addressed to point out potentially critical ‘hotspots’ that the company managing the underground networks should pay attention to by means of further detailed investigations. Recent (i.e. occurred after the finalization of the products shown in this paper) sinkholes validated the reliability of the procedure adopted, whose strength is the data fusion able to produce refined and focused information starting from independent and more generic datasets.

Integration of satellite-based A-DInSAR and geological modeling supporting the prevention from anthropogenic sinkholes. A case study in the urban area of Rome / Esposito, Carlo; Belcecchi, Niccolò; Bozzano, Francesca; Brunetti, Alessandro; Marmoni, Gian Marco; Mazzanti, Paolo; Romeo, Saverio; Cammillozzi, Flavio; Cecchini, Giancarlo; Spizzirri, Massimo. - In: GEOMATICS, NATURAL HAZARDS & RISK. - ISSN 1947-5705. - 12:1(2021), pp. 2835-2864. [10.1080/19475705.2021.1978562]

Integration of satellite-based A-DInSAR and geological modeling supporting the prevention from anthropogenic sinkholes. A case study in the urban area of Rome

Esposito, Carlo
Primo
;
Bozzano, Francesca;Marmoni, Gian Marco;Mazzanti, Paolo;Romeo, Saverio;
2021

Abstract

This paper presents a methodology tuned to support the management of underground pipelines (sewer and aqueduct networks) in Rome, often threatened by the sudden formation of sinkholes related to the upward migration of existing underground cavities. The methodology integrates data coming from the assessment of susceptibility to sinkhole formation and the advanced processing of satellite-based SAR imagery. The former, performed through the multivariate logistic regression technique, relies on a detailed database of stratigraphic and other thematic (i.e. sinkhole inventory and density of underground cavities) information. A-DInSAR processing of satellite images, for which we developed on-purpose algorithms to filter only data relevant for the process under study, allowed us to provide density maps of subsiding reflectors whose likelihood to be precursors of sinkhole collapses is rated based on the integration with the susceptibility map. The procedure is addressed to point out potentially critical ‘hotspots’ that the company managing the underground networks should pay attention to by means of further detailed investigations. Recent (i.e. occurred after the finalization of the products shown in this paper) sinkholes validated the reliability of the procedure adopted, whose strength is the data fusion able to produce refined and focused information starting from independent and more generic datasets.
2021
Sinkhole; susceptibility; A-DInSAR; data integration; spatial hazard zoning; Rome
01 Pubblicazione su rivista::01a Articolo in rivista
Integration of satellite-based A-DInSAR and geological modeling supporting the prevention from anthropogenic sinkholes. A case study in the urban area of Rome / Esposito, Carlo; Belcecchi, Niccolò; Bozzano, Francesca; Brunetti, Alessandro; Marmoni, Gian Marco; Mazzanti, Paolo; Romeo, Saverio; Cammillozzi, Flavio; Cecchini, Giancarlo; Spizzirri, Massimo. - In: GEOMATICS, NATURAL HAZARDS & RISK. - ISSN 1947-5705. - 12:1(2021), pp. 2835-2864. [10.1080/19475705.2021.1978562]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1571308
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