Monitoring the thermal state of permafrost by automated time-lapse capacitive resistivity imaging

Long-term monitoring of subsurface processes increasingly relies on intelligent, systematic data collection by innovative field sensors. The aim of the proposed project is to develop a new technology concept for the non-invasive volumetric imaging and routine temporal monitoring of the thermal state of permafrost. Permafrost has been identified as one of six cryospheric indicators of global climate change within the monitoring framework of the World Meteorological Organization (WMO) Global Climate Observing System (GCOS). Changes in permafrost temperature, associated with the freezing or thawing of pore water, result in significant changes in electrical resistivity. Non-invasive assessment and volumetric monitoring of resistivity changes are facilitated by 4D Electrical Resistivity Tomography (ERT). Tomographic reconstruction with appropriate spatial and temporal resolution enables intuitive visualisation and opens up the important opportunity for quantitative analysis of freeze-thaw processes, including the calibration to permafrost temperature. However, despite the broad appeal of conventional ERT methodology, electrical sensors require galvanic coupling with the ground. This requires that metal electrodes are physically implanted into the active layer (which is subject to seasonal freezing and thawing) or into the underlying permafrost. As a result, this can lead to significant practical limitations on field measurements due to high levels of and large variations in contact resistances between sensors and the host bedrock, soil or building material as it freezes and thaws. Using a novel capacitively-coupled ERT approach, we propose to demonstrate the technical feasibility of undertaking time-lapse tomographic measurements using permanent, in-situ capacitive sensors to remotely monitor the thermal state of permafrost. This will lead to significant improvements in monitoring capability, both for permafrost simulation experiments in the laboratory and for practical applications in the field. The work will include numerical simulation to determine optimal distributed capacitive sensor networks required for volumetric imaging and long-term monitoring of permafrost temperature, both at the field and laboratory scale. Based on the results, a measurement system for multi-sensor automated time-lapse data acquisition will be designed and a viable architecture for a laboratory prototype system will be established. Subsequently, a functional benchtop prototype will be developed and technical feasibility of multi-sensor data acquisition and automated operation will be demonstrated. Finally, we will validate the concept of making automated time-lapse temperature-calibrated CRI measurements in controlled laboratory experiments that simulate permafrost growth, persistence and thaw in bedrock.