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2016-01-26 - Colloque/Présentation - communication orale - Anglais - 1 page(s)

Kaufmann Olivier , Watlet Arnaud , "Electrical Resistivity Tomography Monitoring to Assess Water Distribution in the Vadose Zone" in 5th International Geologica Belgica Meeting, Mons, Belgique, 2016

  • Codes CREF : Sciences de l'ingénieur (DI2000)
  • Unités de recherche UMONS : Géologie fondamentale et appliquée (F401)
  • Instituts UMONS : Institut des Sciences et du Management des Risques (Risques)

Abstract(s) :

(Anglais) Electrical Resistivity Tomography Monitoring to Assess Water Distribution in the Vadose Zone Olivier KAUFMANN1,*, Arnaud WATLET1,2 1 University of Mons Faculty of Engineering – Geology and Applied Geology Unit Rue de Houdain, 9 7000, Mons, BELGIUM 2 Royal Observatory of Belgium Seismology-Gravimetry Section Avenue Circulaire, 3 1180, Brussels, BELGIUM *Corresponding author: olivier.kaufmann@umons.ac.be, +32 65 37 46 21 Keywords: ERT, monitoring, vadose zone. Abstract Water distribution in the subsurface is subject to variations not only in space but also in time. These variations result from the combined effect of different phenomena such as precipitation, run-off, evapotranspiration, water storage in soils or weathered rocks and infiltration to deeper horizons leading to groundwater recharge. Imaging those variations might reveal specific behaviours in the storage and infiltration process linked to local patterns in the subsoil hydraulic properties. However direct in-situ observation of the water distribution is not possible. As soil and rock electrical properties are strongly dependent on the presence (and salinity) of water in their porosity, DC electrical resistivity geophysical methods has long been applied to groundwater exploration. Electrical Resistivity Tomography (ERT) is a geophysical inversion technique allowing to reconstruct a model of subsurface resistivity. In order to detect and track spatial and temporal variations in water distribution, ERT monitoring methods, that incorporate the temporal dimension, are actively developed. Here we focus on the challenges associated with the monitoring of groundwater distribution based on such methods. Taking reliable electrical resistance measurements is a necessary precondition to ERT monitoring. We present our approach to achieve this, by examining several key points: (1) definition and qualification of the monitoring system and procedures, (2) automation of measurement and reporting, (3) assessment of measure repeatability and measurement error and (4) (semi-)automated verification of the system integrity and derive. ERT monitoring quickly produce large datasets. Handling such datasets for pre-processing requires efficient storage and retrieval structures as well as semi-automated (or fully automated) processing workflows. Several strategies that we developed for storing, selecting, filtering, filling gaps associated to instrumental maintenance or breakdowns, including external time series of additionally monitored environmental parameters (e.g. effective rainfall, soil humidity, temperature), formatting data prior inversion and correcting for external factors (e.g. temperature) and inverting datasets are discussed. Python routines have been developed to integrate all the steps prior inversion: data acquisition, append of the hierarchically formatted file, standard filtering and time series pre-processing. Datasets are then generated for inversion program such as BERT. After inversion, models are appended in the hierarchically formatted file for further processing and interpretation. First results obtained with this approach show its added value not only for subsurface imaging and data management but also for designing improved acquisition and processing workflows.