An inversion method for shallow temperature analysis to estimate thermal anomalies in the southern Mount Meager geothermal field, British Columbia, Canada

Deploying shallow temperature probes to record temperature variation is one approach to identify anomalous zones of high ground heat-flux, and following data processing and analysis of the subsurface temperature time series is essential to interpretation. This study presents a different processing workflow to estimate thermal anomalies more accurately and reliably. In order to reduce the daily solar radiation effect, calculate the thermal variations with depth, and then estimate the near-surface relative thermal anomalies, a regularized conjugate gradient inversion algorithm with a Monte Carlo (MC) parameter generator was employed to solve the objective function and overcome the uncertainty related to variation of complex parameters. A physical model was created by assuming that the thermal conductivity and thermal diffusivity vary seasonally as a function of soil water content and density. An empirical equation was then employed to estimate variations in the thermal parameters. The workflow and code using the Python language were developed for the pre-processing and analysis of thermal datasets. Based on the processed transient temperature variations, the estimated long-term temperature trends at different depths were used to deduce deeper geothermal gradients. In a study with real data, the proposed inversion and simulation workflow was applied to calculate the temperature variations at 2.0 m depth. The results identified an anomalous zone of relatively high thermal flux in the south Mount Meager area. By comparing with previous results from shallow temperature wells, the proposed method for estimation of anomalous thermal fields provide a significant advantage for identifying variations in regional thermal gradient and could be a practical tool for different case studies. • Temperature probes were deployed to record subsurface temperature variations. • An iterative inversion approach was developed to reduce the daily solar radiation effect. • A Monte Carlo (MC) parameter generator was incorporated for calculating thermal diffusivity. • The method was validated by applying for thermal anomaly analysis in the Mount Meager area.