[HTML][HTML] Variational Bayesian inference with complex geostatistical priors using inverse autoregressive flows
We combine inverse autoregressive flows (IAF) and variational Bayesian inference
(variational Bayes) in the context of geophysical inversion parameterized with deep …
(variational Bayes) in the context of geophysical inversion parameterized with deep …
Recent advances and opportunities in data assimilation for physics-based hydrological modeling
M Camporese, M Girotto - Frontiers in Water, 2022 - frontiersin.org
Data assimilation applications in integrated surface-subsurface hydrological models
(ISSHMs) are generally limited to scales ranging from the hillslope to local or meso-scale …
(ISSHMs) are generally limited to scales ranging from the hillslope to local or meso-scale …
3-D Bayesian variational full waveform inversion
Seismic full-waveform inversion (FWI) provides high resolution images of the subsurface by
exploiting information in the recorded seismic waveforms. This is achieved by solving a …
exploiting information in the recorded seismic waveforms. This is achieved by solving a …
Estimating surface runoff and groundwater recharge in an urban catchment using a water balance approach
RK Weatherl, MJ Henao Salgado, M Ramgraber… - Hydrogeology …, 2021 - Springer
Land-use changes often have significant impact on the water cycle, including changing
groundwater/surface-water interactions, modifying groundwater recharge zones, and …
groundwater/surface-water interactions, modifying groundwater recharge zones, and …
[HTML][HTML] Do baseline assumptions alter the efficacy of green stormwater infrastructure to reduce combined sewer overflows?
Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer
overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction …
overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction …
Characterization of the non-Gaussian hydraulic conductivity field via deep learning-based inversion of hydraulic-head and self-potential data
Accurate characterization of the spatial heterogeneity of hydraulic properties such as
hydraulic conductivity (K) is essential for understanding groundwater flow and contaminant …
hydraulic conductivity (K) is essential for understanding groundwater flow and contaminant …
Geostatistical inversion for subsurface characterization using Stein variational gradient descent with autoencoder neural network: an application to geologic carbon …
Geophysical subsurface characterization plays a key role in the success of geologic carbon
sequestration (GCS). While deterministic inversion methods are commonly used due to their …
sequestration (GCS). While deterministic inversion methods are commonly used due to their …
The UWO dataset–long-term observations from a full-scale field laboratory to better understand urban hydrology at small spatio-temporal scales
F Blumensaat, S Bloem, C Ebi, A Disch… - Earth System …, 2025 - essd.copernicus.org
Urban drainage systems are integral infrastructural components. However, their monitoring
poses considerable challenges owing to the intricate, hazardous nature of the process …
poses considerable challenges owing to the intricate, hazardous nature of the process …
[HTML][HTML] The effect of green infrastructure on resilience performance in combined sewer systems under climate change
Climate change is currently reshaping precipitation patterns, intensifying extremes, and
altering runoff dynamics. Particularly susceptible to these impacts are combined sewer …
altering runoff dynamics. Particularly susceptible to these impacts are combined sewer …
Posterior sampling with convolutional neural network-based plug-and-play regularization with applications to poststack seismic inversion
Uncertainty quantification is a crucial component in any geophysical inverse problem, as it
provides decision makers with valuable information about the inversion results. Seismic …
provides decision makers with valuable information about the inversion results. Seismic …