Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture.

Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that are capable of decomposing and categorizing the evolution of thousands if not millions of lipids involved in the phenomenon. The interpretation and analysis of molecular dynamics (MD) simulations representing temporal and spatial changes in such systems are still a challenging task.

Nonnegative/Binary matrix factorization with a D-Wave quantum annealer.

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful.

Nonnegative tensor factorization for contaminant source identification.

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. Here, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources.

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored.

Contaminant source identification using semi-supervised machine learning.

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data.

Local Equilibrium and Retardation Revisited.

In modeling solute transport with mobile-immobile mass transfer (MIMT), it is common to use an advection-dispersion equation (ADE) with a retardation factor, or retarded ADE. This is commonly referred to as making the local equilibrium assumption (LEA). Assuming local equilibrium, Eulerian textbook treatments derive the retarded ADE, ostensibly exactly.

Fate and transport of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and its degradation products in sedimentary and volcanic rocks, Los Alamos, New Mexico.

High-explosive compounds including hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) were used extensively in weapons research and testing at Los Alamos National Laboratory (LANL). Liquid effluents containing RDX were released to an outfall pond that flowed to Cañon de Valle at LANL's Technical Area 16 (TA-16), resulting in the contamination of the alluvial, intermediate and regional groundwater bodies. Monitoring of groundwater within Cañon de Valle has shown persistent RDX in the intermediate perched zone located between 225 and 311 m below ground surface.

Contaminant point source localization error estimates as functions of data quantity and model quality.

We develop empirically-grounded error envelopes for localization of a point contamination release event in the saturated zone of a previously uncharacterized heterogeneous aquifer into which a number of plume-intercepting wells have been drilled. We assume that flow direction in the aquifer is known exactly and velocity is known to within a factor of two of our best guess from well observations prior to source identification. Other aquifer and source parameters must be estimated by interpretation of well breakthrough data via the advection-dispersion equation.

Diffusive mixing and Tsallis entropy.

Brownian motion, the classical diffusive process, maximizes the Boltzmann-Gibbs entropy. The Tsallis q entropy, which is nonadditive, was developed as an alternative to the classical entropy for systems which are nonergodic. A generalization of Brownian motion is provided that maximizes the Tsallis entropy rather than the Boltzmann-Gibbs entropy.

Identification of pumping influences in long-term water level fluctuations.

Identification of the pumping influences at monitoring wells caused by spatially and temporally variable water supply pumping can be a challenging, yet an important hydrogeological task. The information that can be obtained can be critical for conceptualization of the hydrogeological conditions and indications of the zone of influence of the individual pumping wells. However, the pumping influences are often intermittent and small in magnitude with variable production rates from multiple pumping wells.

An investigation of numerical grid effects in parameter estimation.

Modern ground water characterization and remediation projects routinely require calibration and inverse analysis of large three-dimensional numerical models of complex hydrogeological systems. Hydrogeologic complexity can be prompted by various aquifer characteristics including complicated spatial hydrostratigraphy and aquifer recharge from infiltration through an unsaturated zone. To keep the numerical models computationally efficient, compromises are frequently made in the model development, particularly, about resolution of the computational grid and numerical representation of the governing flow equation.

Coupling basin- and site-scale inverse models of the Española aquifer.

Large-scale models are frequently used to estimate fluxes to small-scale models. The uncertainty associated with these flux estimates, however, is rarely addressed. We present a case study from the Española Basin, northern New Mexico, where we use a basin-scale model coupled with a high-resolution, nested site-scale model.