Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework

Abstract

Geothermal systems, including borehole thermal energy storage (BTES) and shallow aquifer thermal energy storage systems (ATES) are becoming more popular as the world looks for ways to reduce greenhouse gas emissions. Such systems use thermal energy extracted from the ground or groundwater to heat or cool buildings, which necessitates some electrical energy input for the heat pump, while storing the excess heat or cold underground. The goal is to re-use this thermal energy during the next season in a cyclic utilization (Bayer et al., 2013;Duijff et al., 2021;Saner et al., 2010;Vanhoudt et al., 2011). The performance of BTES and ATES strongly depends on the subsurface properties. Many variables are involved in geothermal processes, including porosity, hydraulic conductivity, thermal conductivity, and heat capacity. Subsurface temperature fluctuations are strongly influenced by the spatial distribution of these parameters, the boundary conditions, and the aquifer's hydraulic gradient

A multiscale accuracy assessment of moisture content predictions using time-lapse electrical resistivity tomography in mine tailings

“…This further highlights the importance of using complementary hydrogeological sensors for field surveys to locally monitor temperature, pore water EC and VWC, which would be used both as input and validation data for processing TL-ERT datasets 1 , 18 . As noted by 141 , the key question for future hydrogeophysical monitoring programs would then be how to select the critical number of sensors and how to optimally deploy them in the field (e.g., 142 , 143 ). In particular, strategies should be investigated to tackle the impact of pore water EC at large scales, especially in media where strong spatial variability of pore water EC is expected.…”

A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems

“…Under a limited budget, it is essential to make rational decisions about when, where, and what kind of data to collect. This can be achieved through Bayesian experimental design (Tarakanov & Elsheikh, 2020; Thibaut et al., 2022; J. Zhang et al., 2015) or data‐worth analysis (Dausman et al., 2010; Wang et al., 2018; Xue et al., 2014). To handle non‐linear and non‐Gaussian observation/system models, Markov chain Monte Carlo (MCMC) or particle filter (PF) methods can be used as the suitable DA methods to approximate the posterior, even when its exact form is unknown (Moradkhani et al., 2005; Shi et al., 2023; Vrugt, 2016).…”

“…Under a limited budget, it is essential to make rational decisions about when, where, and what kind of data to collect. This can be achieved through Bayesian experimental design (Tarakanov & Elsheikh, 2020;Thibaut et al, 2022;J. Zhang et al, 2015) or data-worth analysis (Dausman et al, 2010;Wang et al, 2018;Xue et al, 2014).…”

Machine Learning for Bayesian Experimental Design in the Subsurface

“…• "What is the optimal sensor combination for monitoring a groundwater system? ", e.g., Thibaut et al (2022).…”

“…When working with data that must be collected through an experimental process, as is often the case in geoscience, one of the most important considerations is where to gather new observations: designing a monitoring network necessitates careful consideration of many factors, especially when measurements are costly or resources are limited (Moghaddam et al, 2022;Thibaut et al, 2022). Controlling the experimental conditions for data acquisition is essential to maximize resource utilization and infor-124 CHAPTER 5.…”