Ground surface temperature monitoring data analysis and applications to geothermal exploration in volcanic areas, Mount Meager, western Canada

Abstract

The southern Mount Meager area has been studied for its geothermal resource potential for a half century. Although attempts to commercialize the resource were unsuccessful in the past, a large volume of surface and subsurface geoscience data have been collected and made publicly available, making the Mount Meager area a unique natural laboratory for testing new ideas or tools for geothermal exploration. This study investigates the potential of using a ground surface temperature (GST) monitoring network as a tool in geothermal resource exploration. Twenty-two temperature data loggers were deployed in, and surrounding, the core area of the geothermal prospect for nearly one year. The collected temperature time series were analyzed statistically, and machine learning methods such as hierarchical clustering and k-means classification, were applied to data interpretation. The temporal variation of measured temperature can be divided into four segments: 1) snow free, 2) snow affected, 3) snow curtain, and 4) snow melt seasons. The intensity of daily ground temperature variation in the snow free season shows clear environmental and topographic footprints and is complicated by soil properties. The sites with a short period of near zero GST during the snow curtain period is spatially coincident with high heat flow areas. The measured mean GST shows a close affinity with subsurface heat flow and shallow ground water circulation, suggesting that GST time series contain information indicative of subsurface geothermal anomalies and that a GST monitoring network could be an effective tool for geothermal resource exploration in high latitude and/or elevation areas with seasonal snow cover.