Generating reasonable heterogeneous aquifer structures is essential for understanding the physicochemical processes controlling groundwater flow and solute transport better. The inversion process of aquifer structure identification is usually time-consuming. This study develops an integrated inversion framework, which combines the geological single-sample generative adversarial network (GeoSinGAN), the deep octave convolution dense residual network (DOCRN), and the iterative local updating ensemble smoother (ILUES), named GeoSinGAN-DOCRN-ILUES, for more efficiently generating heterogeneous aquifer structures. The performance of the integrated framework is illustrated by two synthetic contaminant experiments. We show that GeoSinGAN can generate heterogeneous aquifer structures with geostatistical characteristics similar to those of the training sample, while its training time is at least 10 times faster than that of typical approaches (e.g., multi-sample-based GAN). The octave convolution layer and multi-residual connection enable the DOCRN to map the heterogeneity structures to the state variable fields (e.g., hydraulic head, concentration distributions) while reducing the computational cost. The results show that the integrated inversion framework of GeoSinGAN and DOCRN can effectively and reasonably generate the heterogeneous aquifer structures. 

An Integrated Inversion Framework for Heterogeneous Aquifer Structure Identification with Single-Sample Generative Adversarial Network

Underground coal mining suffers from groundwater intrusion from the aquifers overlying coal seams. Therefore, developing methods for the accurate prediction of roof water inflow is urgently needed to design a safe drainage system. In this study, we developed a novel upscaling framework to predict roof water inflow by integrating the multiscale hydrogeological properties of roof aquifers. In this framework, we imaged rock samples via scanning electron microscopy and performed pore-scale analysis based on fractal theory. A fractal model of permeability was introduced to calculate the seepage capacity of the pore structure in the samples. The effect of fractures was further evaluated via core-scale pneumatic experiments. Subsequently, we derived an upscaling formula of hydraulic conductivity used for predicting roof water inflow at the field scale. The proposed upscaling approach was demonstrated using data from a coal mine in Northern China. The results indicate that the actual water inflow (21 m3/h) is within the predicted range of our upscaling framework (9.32–92.78 m3/h), and the initial line fracture rate dx is distributed between 0.02 % and 0.03 %. Therefore, these findings can guide the development of methods for considering micropores and fractures simultaneously and scaling them up to the field scale for effective prediction of water inflow from roof aquifers. 

An upscaling approach to predict mine water inflow from roof sandstone aquifers

This study develops an integrated framework to guide the monitoring network optimization and duration selection for solute transport in heterogeneous sand tank experiments. The method is designed based on entropy and data worth analysis. Numerical models are applied to approach prior observation datasets and to support optimization analysis. Several candidate monitoring locations are synthetically assumed in numerical models. Entropy analysis considers local scale heterogeneity in experiment and identifies stable monitoring locations through extracting maximum information and minimizing optimization redundancies. Data worth analysis quantifies the potential of observation data to reduce the uncertainty of key parameters and selects the monitoring locations with higher data worth. Final monitoring network comprises of optimized monitoring locations obtained based on entropy and data worth analysis. A lab-scale tracer experiment is presented to explore the applicability of the proposed framework. Results show that the optimized monitoring network can accurately characterize the distribution of contaminant plumes in 3D domains and provides estimation of key flow and transport parameters (e.g., hydraulic conductivity and dispersivity). With the extension of experiment time, the total information of monitoring network is maximized, while the uncertainty of key parameters is minimized. The recommended experimental duration is the time by which both joint entropy and parameter variation coefficients are stabilized. Our developed methodology can be used as a flexible and powerful tool to design more complex transport experiments at different spatiotemporal scales. 

An integrated experimental design framework for optimizing solute transport monitoring locations in heterogeneous sedimentary media

Reliable characterization of subsurface structures is essential for earth sciences and related applications. Data assimilation‐based identification frameworks can reasonably estimate subsurface structures using available lithological (e.g., borehole core, well log) and dynamic (e.g., hydraulic head, solute concentration) observations. However, a reasonable selection of the observation type and frequency is essential for accurate structure identification. To achieve this, we extended a recently developed stage‐wise stochastic deep learning inversion framework by coupling it with non‐isothermal flow and transport simulations. With the extended framework, the worth of three common observations (hydraulic head, concentration, and temperature) are compared under different observation noise and frequency. The framework combines the emerging deep‐learning (DL)‐based framework with the traditional stochastic approaches. This combination makes it possible to simultaneously compare the ability of these two methods to assimilate observation data. Our results show that including at least one type of dynamic observation strongly improves subsurface structure identifiability and reduces the uncertainty. However, the DL‐based framework is able to identify subsurface structures more accurately than stochastic identification methods under the same scenarios. Assimilation of certain types of dynamic observations could reduce the prediction error for related dynamic responses, but not necessarily for other uncorrelated dynamic responses. Observation data worth is affected by the observation noise and frequency. High observation noise increases the uncertainty of the prediction and reduces the estimation accuracy. However, the higher observation frequency can significantly improve the temporal dynamic information of observations. This information can compensate for negative impacts of high observation noise.

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

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2022WR033241

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2021WR030595

An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media

Short-chain fatty acids (SCFAs) could be harvested from waste sludge via anaerobic fermentation to offset the cost of waste sludge management. Recovering SCFAs from wastes could also help build a more sustainable society. The present study proposed an approach to recover SCFAs from wasted activated sludge via biocarriers and microfiltration (MF) enhanced anaerobic fermentation. Results show that biocarriers and MF membrane separation promoted SCFA production through different ways. The application of biocarriers alone increased the concentration of soluble organics and SCFAs by enhancing the disintegration and solubilization of sludge particles and enriching hydrolytic bacteria such as Christensenellaceae_R-7_group, Proteiniclasticum, and Proteocatella. The MF membrane alone promoted SCFA production due to the retention of organic matter and acidogenic bacteria such as DMER64 by the MF membrane. The fact that the MF membrane could hardly retain NH4+ and SCFAs was beneficial to the mitigation of ammonia inhibition and acid inhibition. Employing biocarriers and MF membrane separation at the same time achieved a considerable SCFAs concentration of 3500 mg/L and increased the concentration of soluble COD and SCFAs by 2 and 1.3 times, respectively, higher than either of them alone. In addition, biocarriers effectively alleviated the membrane fouling of the MF membrane and increased the membrane flux from 5 L/m2/h to 7 L/m2/h. This study shows the synergistic effects of biocarriers and MF on sludge anaerobic fermentation, and provides a cost-effective approach for SCFA recovery. 

Yu Pan

Papers

An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media

Recovering short-chain fatty acids from waste sludge via biocarriers and microfiltration enhanced anaerobic fermentation

Bacterial antioxidant mechanism in calcium peroxide aided sludge anaerobic fermentation.