A new method relying on machine learning and resistivity to predict concentrations of
petroleum hydrocarbon pollution in soil was proposed as a means of investigation and …

To explore the migration and transformation process of dense non-aqueous liquid (DNAPL)
pollutants' multiphase flow, specifically nitrobenzene (NB), in confined groundwater (CG) …

The mass discharge emanating from dense non‐aqueous phase liquid (DNAPL) source
zones (SZs) is often used as a key metric for risk assessment. To predict the temporal …

Accurate identification of groundwater contamination sources is important for designing
efficacious site remediation strategies. Currently, the methods for identifying contamination …

Thanks to their low computational cost, reduced-order models (ROMs) are indispensable in
ensemble-based simulations used, eg, for uncertainty quantification, inverse modeling, and …

In this study, we designed a machine learning–based parallel global searching method
using the Bayesian inversion framework for efficient identification of dense non-aqueous …

The combined application of direct current (DC) resistivity and induced polarization (IP)
methods, referred to as combined DCIP method, has gained popularity for characterizing the …

Estimating spatially distributed properties such as hydraulic conductivity (K) from available
sparse measurements is a great challenge in subsurface characterization. However, the use …

Characterization of geologic heterogeneity at a geological carbon storage (GCS) system is
crucial for cost-effective carbon injection planning and reliable carbon storage. With recent …

Characterizing the subsurface properties such as permeability and thermal conductivity is
important for stimulation planning and heat production in enhanced geothermal systems …