Modelling temperature effect in Time-Lapse DC monitoring experiments through inversion of thermal diffusivity

The seasonal temperature effect on electrical resistivity data is too often overlooked in ERT moni-toring surveys and modelling efforts. When oversight, this can lead to anomalies in the models that are orders of magnitude greater than the target anomalies being monitored, potentially re-sulting in unusable or misleading results. When not overlooked, temperature correction involves costly and logistically complex measurements of ground temperature alongside resistivity data collection. In this study, we propose a novel Time-Lapse inversion scheme, named ARES, to address the sea-sonal temperature effect without the need of subsoil measurements. The ARES correction directly incorporates temperature into the modelling, estimating subsoil temperature by solving the heat diffusion equation for each time-step and introducing the thermal diffusivity of the medium as an inversion parameter. We present synthetic modelling to test the effectiveness of the ARES correc-tion and develop guidelines for implementing ERT monitoring with the ARES correction. Subse-quently, the application of ARES scheme to a 20-month ERT monitoring project over a Municipal Solid Waste landfill is presented, where a 3D acquisition layout is employed to observe waste evo-lution and identify area of high biogas productivity. The monitoring study was not featured by any thermal monitoring despite the atmospheric temperature effect on data is extremely strong. Our results demonstrate that without the ARES correction, temperature effects overshadowed target anomalies, hindering interpretations. However, with the ARES correction, we successfully compen-sated for temperature effects in the inversion models. The new Time-Lapse inversion scheme finally enabled the detection of anomalies associated with the biogas formation and allowing for their volumetric analysis.