Hydrogeophysical model calibration and uncertainty analysis via full integration of PEST/PEST++ and COMSOL

Hydrogeophysical Inversion of Time‐Lapse ERT Data to Determine Hillslope Subsurface Hydraulic Properties

“…In the second method, predicted state variables of hydrologic models are transformed into geophysical variables with petrophysical relationships and are then used in forward geophysical simulations to predict a geophysical response. This procedure has been referred to as Level 2 data fusion (Yeh & Šimůnek, 2002) and joint inversion (Kowalsky et al., 2005), but recently has most often been referred to as coupled hydrogeophysical inversion (Busch et al., 2013; Camporese et al., 2015; Claes et al., 2020; González‐Quirós & Comte, 2021; Hinnell et al., 2010; Huisman et al., 2010; Irving & Singha, 2010; Mboh et al., 2012; Pollock & Cirpka, 2012; Yu et al., 2021). In both methods, the calibrated parameters can include hydraulic and petrophysical parameters.…”

“…In the first method, collected geophysical data are inverted separately from hydrologic model simulations, converted to hydrologic state variables using petrophysical relationships, and then used as calibration targets for the hydrologic model. This procedure has been referred to as Level 1 data fusion (Yeh & Šimůnek, 2002), sequential inversion (Yu et al., 2021), or left unnamed (Doetsch et al., 2012; Farmani et al., 2008; Kemna et al., 2002; Vanderborght et al., 2005), but recently has most often been referred to as uncoupled hydrogeophysical inversion (Beaujean et al., 2014; Camporese et al., 2015; Claes et al., 2020; González‐Quirós & Comte, 2021; Hinnell et al., 2010; Irving & Singha, 2010). In the second method, predicted state variables of hydrologic models are transformed into geophysical variables with petrophysical relationships and are then used in forward geophysical simulations to predict a geophysical response.…”

Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework

“…Furthermore, estimated subsurface structures using lithological observations might be biased and highly uncertain (Chen & Rubin, 2003; Fogg & Zhang, 2016). However, as shown in Figure 1, some dynamic observations (e.g., hydraulic head, solute concentration) may contain information that can be used to reduce the uncertainty (Butera et al., 2009; Carrera et al., 2005; Doherty, 2003; Ezzedine & Rubin, 1996; Freixas et al., 2017; González‐Quirós & Comte, 2021; Trabucchi et al., 2021). The data assimilation (DA) framework combines the theory (e.g., forward simulation) with data (e.g., dynamic observations) to improve the subsurface systems' identifiability.…”

A COMSOL‐PHREEQC Coupled Python Framework for Reactive Transport Modeling in Soil and Groundwater

“…In addition, a COMSOL‐PHREEQC interface for modeling the multi‐components transport of saturated cement‐based materials was carried out to model the influence of the variation of the porosity on the ionic transport (Guo et al 2018). Besides, a novel interface, COMPEST, that couples the parameter estimation and uncertainty analysis package, PEST, with COMSOL were presented (Halloran et al 2019), and a methodological approach to perform coupled inversion by integrating the calibration software PEST/PEST++ with COMSOL was proposed using MATLAB (González‐Quirós and Comte 2021).…”