A: CPU, central processing unit; EnKF, ensemble Kalman fi lter; GDPM, generalized dual-porosity model; KF, Kalman fi lter; LANL, Los Alamos National Laboratory; MDA, Material Disposal Area; MPI, message passing interface; MVG, Mualem-van Genuchten; PFBA, pentafl uorobenzoate; RTD, residence time distribution; SLS, simple least squares; SVE, soil vapor extraction; VOC, volatile organic compound. S S : V Z M Many of the parameters in subsurface fl ow and transport models cannot be es mated directly at the scale of interest, but can only be derived through inverse modeling. During this process, the parameters are adjusted in such a way that the behavior of the model approximates, as closely and consistently as possible, the observed response of the system under study for some historical period of me. We briefl y review the current state of the art of inverse modeling for es ma ng unsaturated fl ow and transport processes. We summariz how the inverse method works, discuss the historical background that led to the current perspec ves on inverse modeling, and review the solu on algorithms used to solve the parameter es ma on problem. We then highlight our recent work at Los Alamos related to the development and implementa on of improved op miza on and data assimila on methods for computa onally effi cient calibra on and uncertainty es ma on in complex, distributed fl ow and transport models using parallel compu ng capabili es. Finally, we illustrate these developments with three diff erent case studies, including (i) the calibra on of a fully coupled three-dimensional vapor extrac on model using measured concentra ons of vola le organic compounds in the subsurface near the Los Alamos Na onal Laboratory, (ii) the mul objec ve inverse es ma on of soil hydraulic proper es in the HYDRUS-1D model using observed tensiometric data from an experimental fi eld plot in New Zealand, and (iii) the simultaneous es ma on of parameter and states in a groundwater solute mixture model using data from a mul tracer experiment at Yucca Mountain, Nevada.
Abstract. In paper 1 of this two-part series we described a three-dimensional numerical inverse model for the interpretation of cross-hole pneumatic tests in unsaturated fractured tuffs at the Apache Leap Research Site (ALRS) near Superior, Arizona. Our model is designed to analyze these data in two ways: (1) by considering pressure records from individual borehole monitoring intervals one at a time, while treating the rock as being spatially uniform, and (2) by considering pressure records from multiple tests and borehole monitoring intervals simultaneously, while treating the rock as being randomly heterogeneous. The first approach yields a series of equivalent air permeabilities and airfilled porosities for rock volumes having length scales ranging from meters to tens of meters, represented nominally by radius vectors extending from injection to monitoring intervals. The second approach yields a high-resolution geostatistical estimate of how air permeability and air-filled porosity, defined on grid blocks having a length scale of 1 m, vary spatially throughout the tested rock volume. It amounts to three-dimensional pneumatic "tomography" or stochastic imaging of the rock. Paper 1 described the field data, the model, and the effect of boreholes on pressure propagation through the rock. This second paper implements our inverse model on pressure data from five cross-hole tests at ALRS. We compare our cross-hole test interpretations by means of the two approaches with earlier interpretations by means of type curves and with geostatistical interpretations of single-hole test data. The comparisons show internal consistency between all pneumatic test interpretations and reveal a very pronounced scale effect in permeability and porosity at ALRS.
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. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method takes a matrix as input and produces two low-rank matrices as output—one containing latent features in the data and another matrix describing how the features can be combined to approximately reproduce the input matrix. Despite the limited number of bits in the D-Wave hardware, this method is capable of handling a large input matrix. The D-Wave only limits the rank of the two output matrices. We apply this method to learn the features from a set of facial images and compare the performance of the D-Wave to two classical tools. This method is able to learn facial features and accurately reproduce the set of facial images. The performance of the D-Wave shows some promise, but has some limitations. It outperforms the two classical codes in a benchmark when only a short amount of computational time is allowed (200-20,000 microseconds), but these results suggest heuristics that would likely outperform the D-Wave in this benchmark.
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capable of robust determination of the number of basis patterns present in the data which, in turn, enables straightforward identification of any possible peakshifted patterns. Peak-shifting arises due to continuous change in the lattice constants as a function of composition, and is ubiquitous in XRD datasets from composition spread libraries. Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns, which is necessary in order to decipher the contribution of each unique singlephase structure to the multi-phase regions. The process can be utilized to determine accurately the compositional phase diagram of a system under study. The presented method is applied to one synthetic and one experimental dataset, and demonstrates robust accuracy and identification abilities.
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