We present an automated time series analysis of InSAR data for the characterization of ground subsidence induced by longwall mining at Metropolitan mine (New South Wales, Australia). The dataset derives from SqueeSAR™ processing of two Envisat radar data stacks of 44 images acquired from the ascending orbit and 43 from a descending orbit, acquired on a 35-day repeat interval in the period June 2006 to September 2010. Automated time series classification was carried out with PSTime, a specifically designed software that employs a sequence of statistical tests to classify the time series into the six different classes: uncorrelated, linear, quadratic, bilinear, discontinuous without constant velocity and discontinuous with change in velocity. Results highlight a cluster of bilinear trends with acceleration at the front of longwall panel progression and a cluster of bilinear trends with deceleration at the back. Linear trends are found at the centre of the subsidence bowl while outside most trends are uncorrelated. This picture is consistent with the evidence of acceleration in deformation trend when mining is approaching and a deceleration after extraction is completed and an essentially constant while mining takes place just underneath a specific point. Thus, time series analysis proved to be valuable for subsidence dynamics characterization, constraining in space and time the patterns of deformation trend in mining applications.

Characterization of Longwall Mining Induced Subsidence by Means of Automated Analysis of InSAR Time-Series

BERTI, MATTEO;
2014

Abstract

We present an automated time series analysis of InSAR data for the characterization of ground subsidence induced by longwall mining at Metropolitan mine (New South Wales, Australia). The dataset derives from SqueeSAR™ processing of two Envisat radar data stacks of 44 images acquired from the ascending orbit and 43 from a descending orbit, acquired on a 35-day repeat interval in the period June 2006 to September 2010. Automated time series classification was carried out with PSTime, a specifically designed software that employs a sequence of statistical tests to classify the time series into the six different classes: uncorrelated, linear, quadratic, bilinear, discontinuous without constant velocity and discontinuous with change in velocity. Results highlight a cluster of bilinear trends with acceleration at the front of longwall panel progression and a cluster of bilinear trends with deceleration at the back. Linear trends are found at the centre of the subsidence bowl while outside most trends are uncorrelated. This picture is consistent with the evidence of acceleration in deformation trend when mining is approaching and a deceleration after extraction is completed and an essentially constant while mining takes place just underneath a specific point. Thus, time series analysis proved to be valuable for subsidence dynamics characterization, constraining in space and time the patterns of deformation trend in mining applications.
Engineering Geology for Society and Territory
973
976
Iannacone J.P.; Corsini A.; Berti M.; Morgan J.; Falorni G.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/381655
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