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1. Aquifer Characterization and Uncertainty in Multi‐Frequency Oscillatory Flow Tests: Approach and Insights

   https://doi.org/10.1111/gwat.13134  

   Patterson, Jeremy_R; Cardiff, Michael (September 2021, Groundwater)

   Abstract Characterizing aquifer properties and their associated uncertainty remains a fundamental challenge in hydrogeology. Recent studies demonstrate the use of oscillatory flow interference testing to characterize effective aquifer flow properties. These characterization efforts relate the relative amplitude and phase of an observation signal with a single frequency component to aquifer diffusivity and transmissivity. Here, we present a generalized workflow that relates extracted Fourier coefficients for observation signals with single and multiple frequency components to aquifer flow properties and their associated uncertainty. Through synthetic analytical modeling we show that multi‐frequency oscillatory flow interference testing adds information that improves inversion performance and decreases parameter uncertainty. We show increased observation signal length, sampling frequency, and pressure sensor accuracy all produce decreased parameter uncertainty. This work represents the first attempt we are aware of to quantify effective aquifer parameters and their associated uncertainty using multi‐frequency oscillatory flow interference testing. 

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2. Automated cell properties toolbox from 3D bioprinted hydrogel scaffolds via deep learning and optical coherence tomography

   https://doi.org/10.1364/BOE.550401  

   Babaei, Mahdi; Shamouil, Aaron; Wang, Jiaying; Khare, Deepak; Wang, Tianyuanye; Shih, Meijie; Yu, Xiaojun; Gan, Yu (April 2025, Biomedical Optics Express)

   Accurately assessing cell viability and morphological properties within 3D bioprinted hydrogel scaffolds is essential for tissue engineering but remains challenging due to the limitations of existing invasive and threshold-based methods. We present a computational toolbox that automates cell viability analysis and quantifies key properties such as elongation, flatness, and surface roughness. This framework integrates optical coherence tomography (OCT) with deep learning-based segmentation, achieving a mean segmentation precision of 88.96%. By leveraging OCT’s high-resolution imaging with deep learning-based segmentation, our novel approach enables non-invasive, quantitative analysis, which can advance rapid monitoring of 3D cell cultures for regenerative medicine and biomaterial research. 

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3. Do Simple Analytical Models Capture Complex Fractured Bedrock Hydraulics? Oscillatory Flow Tests Suggest Not

   https://doi.org/10.1111/gwat.13297  

   Patterson, Jeremy_R; Cardiff, Michael (February 2023, Groundwater)

   Abstract Fractured sedimentary bedrock aquifers represent complex flow systems that may contain fast, fracture‐dominated flow paths and slower, porous media‐dominated flow paths. Thus, characterizing the dynamics of flow and transport through these aquifers remains a fundamental hydrogeologic challenge. Recent studies have demonstrated the utility of a novel hydraulic testing approach, oscillatory flow testing, in field settings to characterize single bedrock fractures embedded in low‐porosity sedimentary bedrock. These studies employed an idealized analytical model assuming Darcian flow through a nondeforming, constant‐aperture, nonleaky fracture for data interpretation, and reported period‐dependent effective fracture flow parameters. Here, we present the application of oscillatory flow testing across a range of frequencies and inter‐well spacings on a fracture embedded in poorly cemented sedimentary bedrock with considerable primary porosity at the Field Site for Research in Fractured Sedimentary Rock. Consistent with previous studies, we show an apparent period‐dependence in returned flow parameters, with hydraulic diffusivity decreasing and storativity increasing with increasing oscillation period, when assuming an idealized fracture conceptual model. We present simple analyses that examine non‐Darcian flow and borehole storage effects as potential test design artifacts and a simple analytical model that examines fluid leakage to the surrounding host rock as a potential hydraulic mechanism that might contribute to the period‐dependent flow parameters. These analyses represent a range of conceptual assumptions about fracture behavior during hydraulic testing, none of which account for the measured responses during oscillatory flow testing, leading us to argue that other hydraulic processes (e.g., aperture heterogeneity and/or fracture hydromechanics) are necessary to accurately represent pressure propagation through fractured sedimentary bedrock. 

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

   https://doi.org/10.1029/2021WR031073  

   Pleasants, M. S.; Neves, F. dos A.; Parsekian, A. D.; Befus, K. M.; Kelleners, T. J. (April 2022, Water Resources Research)

   Abstract Time‐lapse electrical resistivity tomography (ERT) data are increasingly used to inform the hydrologic dynamics of mountainous environments at the hillslope scale. Despite their popularity and recent advancements in hydrogeophysical inversion methods, few studies have shown how time‐lapse ERT data can be used to determine hydraulic parameters of subsurface water flow models. This study uses synthetic and field‐collected, hillslope‐scale, time‐lapse ERT data to determine subsurface hydraulic properties of a two‐layer, physics‐based, 2‐D vertical flow model with predefined layer and boundary locations. Uncoupled and coupled hydrogeophysical inversion methods are combined with a fine‐earth fraction optimization scheme to reduce the number of parameters needing calibration and interpret the influence of the hydraulic parameters on the hydrologic model predictions. Inversions of synthetic ERT data recover the prescribed fine‐earth fraction bulk density to within 0.1 g cm⁻³. Field‐collected ERT data from a mountain hillslope result in hydrologic model dynamics that are consistent with previous studies and measured water content data but struggle to capture measured groundwater levels. The uncoupled hydrogeophysical inversion method is more sensitive to changes in hydraulic parameter values of the lower hydrologic model layer than the coupled hydrogeophysical inversion method. Time series of minimum objective function value simulations indicate that periodically collected ERT data may recover hydraulic parameters to a similar level of uncertainty as daily ERT data. Using simple hydrologic model domains within hydrogeophysical inversions shows promise for providing reasonable hydrologic predictions while maintaining relatively simple calibration schemes and should be explored further in future studies. 

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5. Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks

   https://doi.org/10.1088/1361-6420/ad5373  

   Afkham, Babak_Maboudi; Chung, Julianne; Chung, Matthias (June 2024, Inverse Problems)

   Abstract In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient uncertainty quantification forgoal-orientedinverse problems. Contrary to standard inverse problems, these approaches are goal-oriented in that the goal is to estimate some quantities of interest (QoI) that are functions of the solution of an inverse problem, rather than the solution itself. Moreover, we are interested in computing uncertainty metrics associated with the QoI, thus utilizing a Bayesian approach for inverse problems that incorporates the prediction operator and techniques for exploring the posterior. This may be particularly challenging, especially for nonlinear, possibly unknown, operators and nonstandard prior assumptions. We harness recent advances in machine learning, i.e. VED networks, to describe a data-driven approach to large-scale inverse problems. This enables a real-time uncertainty quantification for the QoI. One of the advantages of our approach is that we avoid the need to solve challenging inversion problems by training a network to approximate the mapping from observations to QoI. Another main benefit is that we enable uncertainty quantification for the QoI by leveraging probability distributions in the latent and target spaces. This allows us to efficiently generate QoI samples and circumvent complicated or even unknown forward models and prediction operators. Numerical results from medical tomography reconstruction and nonlinear hydraulic tomography demonstrate the potential and broad applicability of the approach. 

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