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Estimating picking errors in near-surface seismic data to enable their time-lapse interpretation on hydrosystems

Abstract : Time‐lapse applications of seismic methods have been recently suggested at the near‐surface scale to track hydrological properties variations due to climate, water level changes or permafrost thaw for instance. But when it comes to traveltime tomography or surface‐wave dispersion inversion, a careful estimation of the data variability associated to the picking process must be considered prior to any time‐lapse interpretation. In this study, we propose to estimate picking errors that are due to the inherent subjectivity of human operators using statistical analysis based on picking repeatability. Two seismic datasets were collected along the same profile under distinct hydrological conditions, across a granite‐micaschist contact at the Ploemeur hydrological observatory (France). Both datasets were recorded using identical equipment and acquisition parameters. A thorough statistical analysis is conducted to estimate picking uncertainties, at the 99 % confidence level, for both Pressure (P) wave first arrival time and surface‐wave phase velocity. With the suggested workflow, we are able to identify 33 % of the P‐wave traveltimes and 16 % of the surface‐wave dispersion data that can be considered significant enough for time‐lapse interpretations. In this selected portion of the data, point‐by‐point differences are highlighting important variations linked to different hydrogeological properties of the subsurface. These variations show strong contrasts with a non‐monotonous behaviour along the line, offering new insights to better constrain the dynamics of this hydrosystem.
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Submitted on : Wednesday, November 7, 2018 - 8:54:02 AM
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Marine Dangeard, L. Bodet, S. Pasquet, J. Thiesson, R. Guerin, et al.. Estimating picking errors in near-surface seismic data to enable their time-lapse interpretation on hydrosystems. Near Surface Geophysics, European Association of Geoscientists and Engineers (EAGE), 2018, 16 (6), pp.613-625. ⟨10.1002/nsg.12019⟩. ⟨insu-01914586⟩



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