2022
DOI: 10.1007/s40808-022-01519-y
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The potential of microgravity technique in subsurface cavities detection at Chan Sow Lin Site in Kuala Lumpur, Malaysia: a case study

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(1 citation statement)
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“…To overcome these challenges, geophysical methods and remote sensing techniques have emerged as valuable tools for the non-invasive location of underground cavities. Among geophysical methods, electrical resistivity tomography (ERT) (Cardarelli et al 2006;Park et al 2014), ground penetrating radar (GPR) (Ebraheem and Ibrahim 2019;Pilecki et al 2021), microgravity (Arisona et al 2023), and seismic methods (Liang et al 2018;Rao et al 2021) have been widely applied to investigate karst networks, sinkholes, and underground instabilities (Liu et al 2023). Satellite-based monitoring techniques, such as Interferometric Synthetic Aperture Radar (InSAR), have also succeeded in detecting cavities and monitoring the evolution of sinkholes and subsidence-prone areas, demonstrating the ability to provide wide spatial data coverage with adequate geometric and temporal resolution (Malinowska et al 2019;Esposito et al 2021).…”
Section: Introductionmentioning
confidence: 99%
“…To overcome these challenges, geophysical methods and remote sensing techniques have emerged as valuable tools for the non-invasive location of underground cavities. Among geophysical methods, electrical resistivity tomography (ERT) (Cardarelli et al 2006;Park et al 2014), ground penetrating radar (GPR) (Ebraheem and Ibrahim 2019;Pilecki et al 2021), microgravity (Arisona et al 2023), and seismic methods (Liang et al 2018;Rao et al 2021) have been widely applied to investigate karst networks, sinkholes, and underground instabilities (Liu et al 2023). Satellite-based monitoring techniques, such as Interferometric Synthetic Aperture Radar (InSAR), have also succeeded in detecting cavities and monitoring the evolution of sinkholes and subsidence-prone areas, demonstrating the ability to provide wide spatial data coverage with adequate geometric and temporal resolution (Malinowska et al 2019;Esposito et al 2021).…”
Section: Introductionmentioning
confidence: 99%