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At least 1,513 records · Page 84

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NASA NTRS2020

Identifying Hurricane Impacts on Barbuda Using Citizen Science Ground Observations, Drone Photography and Satellite Imagery

Barbuda is a low-lying leeward island in the West Indies. In September 2017 Hurricane Irma battered the island with 185 mph winds. Damage to housing and infrastructure was so extensive that all 1800 residents of the island were immediately evacuated post hurricane to the neighboring island of Antigua. Our research is focused on documenting and analyzing the landscape scale changes that took place on the island as a result of the hurricane using a nested methodology. Ground level observations were recorded using a beta version of the Global Learning and Observation to Benefit the Environment (GLOBE) Observer Land Cover tool. Aerial photographs provide medium scale landscape resolution. Satellite images were processed to provide NDVI values. We describe how nested data at several spatial scales enable detailed description of Hurricane Irma’s impact across the landscape of this 62 sq. mile island. Here we are connecting observations at different spatial scales, using citizen science observations, aerial drone photography and satellite imagery to document and analyze hurricane impacts on the island of Barbuda, eastern Caribbean.

Barbuda↗

NASA NTRSDate not supplied

Generating a 4D Global CH(4) Product by Assimilating TROPOMI column CH(4) in NASA’s GEOS GCM

Examination of temporal and spatial CH4 variability is crucial for better understanding the human and natural processes driving climate change and ultimately designing mitigation strategies. Here we present an analysis framework that uses NASA’s GEOS General Circulation Model (GCM) to construct a high-resolution, time varying picture of atmospheric CH4 consistent with measurements from a variety of platforms, both in situ and remotely sensed. The resulting time varying atmospheric CH4 product can (i) support interpretation of high-resolution point source detection approaches, (ii) provide reanalysis fields for CH4 and other greenhouse gases, and (iii) supply boundary conditions for regional models. Our approach starts with a set of CH4 emissions from various inventories that have been adjusted to match the global annual growth rate over recent decades. These emissions are transported by the GEOS GCM, which in turn is constrained by meteorology from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) product. The simulated atmospheric field is compared with CH4 measurements, such as those from the TROPOspheric Monitoring Instrument (TROPOMI), and adjustments calculated following a Bayesian protocol. The accuracy of the resultant optimal atmospheric CH4 field can be demonstrated by its improved agreement (compared to a direct simulation of the CH4 inventories) with a host of independent CH4 measurements, such as those from the Total Carbon Column Observation Network (TCCON) and in situ observations from surface and airborne platforms.

Nikolay V. Balashov↗

NASA NTRS2004

Method and apparatus for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.

Hood, Kenneth Brown↗

NASA NTRS2007

Method and system for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

Hood, Kenneth Brown↗

DOE OSTI2021

Instrument for in situ hard x-ray nanobeam characterization during epitaxial crystallization and materials transformations

Solid-phase epitaxy (SPE) and other three-dimensional epitaxial crystallization processes pose challenging structural and chemical characterization problems. The concentration of defects, the spatial distribution of elastic strain, and the chemical state of ions each vary with nanoscale characteristic length scales and depend sensitively on the gas environment and elastic boundary conditions during growth. The lateral or three-dimensional propagation of crystalline interfaces in SPE has nanoscale or submicrometer characteristic distances during typical crystallization times. Here, an in situ synchrotron hard x-ray instrument allows these features to be studied during deposition and crystallization using diffraction, resonant scattering, nanobeam and coherent diffraction imaging, and reflectivity. The instrument incorporates a compact deposition system allowing the use of short-working-distance x-ray focusing optics. Layers are deposited using radio-frequency magnetron sputtering and evaporation sources. The deposition system provides control of the gas atmosphere and sample temperature. The sample is positioned using a stable mechanical design to minimize vibration and drift and employs precise translation stages to enable nanobeam experiments. Results of in situ x-ray characterization of the amorphous thin film deposition process for a SrTiO 3 /BaTiO 3 multilayer illustrate implementation of this instrument.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Confocal Raman Microscopy as a Probe of Material Deconstruction in Processed Low-Density Polyethylene Particles

Confocal Raman microscopy was applied to detect structural change within individual particles of low-density polyethylene (LDPE) following chemical and electrochemical processing steps that aimed to facilitate material decomposition. A high numerical aperture (NA) oil-immersion objective enabled depth-profiling through the near surface region (20 μm–40 μm) of irregularly shaped particles with an axial spatial resolution < 2 μm estimated from measurements of instrument detection efficiency profiles. Changes in vibrational bands sensitive to polyethylene crystallinity were evident following treatments and linked to the release of low molecular weight compounds present as additives and products of processing. Effects of processing were probed by monitoring the rise of Raman scattering intensity in vibrational modes associated with polyethylene chains in a zig-zag (trans) conformation near 1128 cm –1 , 1294 cm –1 , and 1418 cm –1 , signaling chain clustering and development of organized, crystalline-like assemblies. Pristine LDPE particles displayed a uniform structure across the near surface region, while particles treated initially with chemical extractant and then further processed displayed increasingly enhanced crystallinity up to the maximum depth probed (40 μm). As a step toward measurements on ensembles of particles, least squares modeling was adapted to derive pure component spectra reflecting crystallinity change within spectral datasets. The work demonstrates high spatial resolution Raman depth-profiling for the characterization of processed polymers using a high NA immersion objective to overcome the limitations of air-objectives often used for confocal Raman microscopy.

Wahiduzzaman, Md. [Department of Chemistry and Bio↗

DOE OSTI2022

Probing multiscale dissolution dynamics in natural rocks through microfluidics and compositional analysis

Mineral dissolution significantly impacts many geological systems. Carbon released by diagenesis, carbon sequestration, and acid injection are examples where geochemical reactions, fluid flow, and solute transport are strongly coupled. The complexity in these systems involves interplay between various mechanisms that operate at timescales ranging from microseconds to years. Current experimental techniques characterize dissolution processes using static images that are acquired with long measurement times and/or low spatial resolution. These limitations prevent direct observation of how dissolution reactions progress within an intact rock with spatially heterogeneous mineralogy and morphology. We utilize microfluidic cells embedded with thin rock samples to visualize dissolution with significant temporal resolution (100 ms) in a large observation window (3 × 3 mm). Here we injected acidic fluid into eight shale samples ranging from 8 to 86 wt % carbonate. The pre- and postreaction microstructures are characterized at the scale of pores (0.1 to 1 µm) and fractures (1 to 1,000 µm). We observe that nonreactive particle exposure, fracture morphology, and loss of rock strength are strongly dependent on both the relative volume of reactive grains and their distribution. Time-resolved images of the rock unveil the spatiotemporal dynamics of dissolution, including two-phase flow effects in real time and illustrate the changes in the fracture interface across the range of compositions. Moreover, the dynamical data provide an approach for characterizing reactivity parameters of natural heterogeneous samples when porous media effects are not negligible. The platform and workflow provide real-time characterization of geochemical reactions and inform various subsurface engineering processes.

58 GEOSCIENCES↗

NASA NTRS1997

Laser-Induced Incandescence in Microgravity

Knowledge of soot concentration is important due to its presence and impact upon a wide range of combustion processes ranging from diffusion to premixed flames, laminar to turbulent processes and homogeneous to heterogeneous combustion. Measurement of soot volume fraction (f(sub v)) is essential to discerning its formation and growth. The presence of soot also affects other physical and chemical properties of combustion thereby affecting studies not directly concerned with either its formation or growth, such as radiative heat transfer, CO oxidation and fuel vaporization or pyrolysis rates. Microgravity offers unique opportunities for studying both soot growth and the effect of soot radiation upon flame structure and spread. Spatial scales and residence time scales are greatly extended in 0-g facilitating soot growth studies. With the varied geometries, short duration microgravity test times and time-varying processes there is a demand for measurement of f(sub v) with high spatial and temporal resolution. Laser-induced incandescence (LII) has advanced f(sub v) measurements in many 1-g combustion processes. To create laser-induced incandescence, a pulsed high intensity laser heats soot to incandescence temperatures. Using appropriate spectral and temporal detection conditions, the resulting incandescence can be selectively detected apart from the non-laser-heated soot and flame gases. Theoretical modelling and experiments have shown that the resulting incandescence is representative of f(sub v). Using an intensified array camera and a laser sheet for excitation, one- and two-dimensionally resolved LII images of f(sub v) have been obtained in 1-g. LII has been characterized and developed at NASA-Lewis for soot volume fraction determination in a wide range of 1-g combustion applications. Broadly grouped, the characterization work has included studies of excitation intensity, excitation wavelength and the optimum temporal and spectral detection conditions to enable an accurate representation of soot volume fraction by LII. Tests for special requirements imposed by different combustion processes have been performed in laminar and turbulent diffusion flames, rich sooting premixed flames, single droplet combustion, and other heterogeneous combustion. These studies demonstrated LII's high sensitivity, temporal and spatial capabilities and its geometric versatility. In contrast to the advantages offered to combustion studies by a microgravity environment, advanced diagnostics, specifically those requiring pulsed laser diagnostics have been limited due to the size, weight and power limitations in a low-gravity environment. Reported here are the first demonstrations of LII performed in a microgravity environment. Examples are shown for laminar and turbulent gas-jet diffusion flames in 0-g.

VanderWal, Randy L.↗

NASA NTRS2006

Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.

Chatterji, Gano B.↗

DOE OSTI2026

A hybrid CNN-LSTM surrogate model for hyper-resolution spatiotemporal flood forecasting in Norfolk, Virginia

Study region: Norfolk, Virginia, United States Study focus: Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features. New hydrologic insights for the region: The hybrid CNN-LSTM model was trained using the physics-based hydrodynamic model simulations obtained from the Two-dimensional Unsteady FLOW (TUFLOW) model for Norfolk, Virginia, and achieved high predictive accuracy across diverse flood-prone areas. The reduced computational time from four to six hours using TUFLOW to 3.2 min per event using CNN-LSTM enables rapid flood inundation mapping and early warning applications. The model effectively captured both spatial flood extents and their temporal evolution across different flooding scenarios, providing forecasts at a 2.5-m spatial resolution and 15-min temporal resolution and a one-hour-ahead prediction horizon. While challenges remain in terms of transferability to new regions and real-time data assimilation, this approach demonstrates strong potential for supporting operational flood risk management in coastal urban environments.

Coastal urban flooding↗

NASA NTRS2001

Models of Small-Scale Patchiness

Patchiness is perhaps the most salient characteristic of plankton populations in the ocean. The scale of this heterogeneity spans many orders of magnitude in its spatial extent, ranging from planetary down to microscale. It has been argued that patchiness plays a fundamental role in the functioning of marine ecosystems, insofar as the mean conditions may not reflect the environment to which organisms are adapted. Understanding the nature of this patchiness is thus one of the major challenges of oceanographic ecology. The patchiness problem is fundamentally one of physical-biological-chemical interactions. This interconnection arises from three basic sources: (1) ocean currents continually redistribute dissolved and suspended constituents by advection; (2) space-time fluctuations in the flows themselves impact biological and chemical processes, and (3) organisms are capable of directed motion through the water. This tripartite linkage poses a difficult challenge to understanding oceanic ecosystems: differentiation between the three sources of variability requires accurate assessment of property distributions in space and time, in addition to detailed knowledge of organismal repertoires and the processes by which ambient conditions control the rates of biological and chemical reactions. Various methods of observing the ocean tend to lie parallel to the axes of the space/time domain in which these physical-biological-chemical interactions take place. Given that a purely observational approach to the patchiness problem is not tractable with finite resources, the coupling of models with observations offers an alternative which provides a context for synthesis of sparse data with articulations of fundamental principles assumed to govern functionality of the system. In a sense, models can be used to fill the gaps in the space/time domain, yielding a framework for exploring the controls on spatially and temporally intermittent processes. The following discussion highlights only a few of the multitude of models which have yielded insight into the dynamics of plankton patchiness. In addition, this particular collection of examples is intended to furnish some exposure to the diversity of modeling approaches which can be brought to bear on the problem. These approaches range from abstract theoretical models intended to elucidate specific processes, to complex numerical formulations which can be used to actually simulate observed distributions in detail.

McGillicuddy, D. J.↗

NASA NTRS1995

Clouds and the Earth's Radiant Energy System (CERES) algorithm theoretical basis document: Determination of surface and atmosphere fluxes and temporally and spatially averaged products (subsystems 5-12) - volume 4

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and the Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 4 details the advanced CERES techniques for computing surface and atmospheric radiative fluxes (using the coincident CERES cloud property and top-of-the-atmosphere (TOA) flux products) and for averaging the cloud properties and TOA, atmospheric, and surface radiative fluxes over various temporal and spatial scales. CERES attempts to match the observed TOA fluxes with radiative transfer calculations that use as input the CERES cloud products and NOAA National Meteorological Center analyses of temperature and humidity. Slight adjustments in the cloud products are made to obtain agreement of the calculated and observed TOA fluxes. The computed products include shortwave and longwave fluxes from the surface to the TOA. The CERES instantaneous products are averaged on a 1.25-deg latitude-longitude grid, then interpolated to produce global, synoptic maps to TOA fluxes and cloud properties by using 3-hourly, normalized radiances from geostationary meteorological satellites. Surface and atmospheric fluxes are computed by using these interpolated quantities. Clear-sky and total fluxes and cloud properties are then averaged over various scales.

Wielicki, Bruce A.↗

NASA NTRS2014

Diagnosis of Middle Atmosphere Climate Sensitivity by the Climate Feedback Response Analysis Method

We present a new method to diagnose the middle atmosphere climate sensitivity by extending the Climate Feedback-Response Analysis Method (CFRAM) for the coupled atmosphere-surface system to the middle atmosphere. The Middle atmosphere CFRAM (MCFRAM) is built on the atmospheric energy equation per unit mass with radiative heating and cooling rates as its major thermal energy sources. MCFRAM preserves the CFRAM unique feature of an additive property for which the sum of all partial temperature changes due to variations in external forcing and feedback processes equals the observed temperature change. In addition, MCFRAM establishes a physical relationship of radiative damping between the energy perturbations associated with various feedback processes and temperature perturbations associated with thermal responses. MCFRAM is applied to both measurements and model output fields to diagnose the middle atmosphere climate sensitivity. It is found that the largest component of the middle atmosphere temperature response to the 11-year solar cycle (solar maximum vs. solar minimum) is directly from the partial temperature change due to the variation of the input solar flux. Increasing CO2 always cools the middle atmosphere with time whereas partial temperature change due to O3 variation could be either positive or negative. The partial temperature changes due to different feedbacks show distinctly different spatial patterns. The thermally driven globally averaged partial temperature change due to all radiative processes is approximately equal to the observed temperature change, ranging from 0.5 K near 70 km from the near solar maximum to the solar minimum.

MCFRAM↗

NASA NTRS2010

Direct Measurement Sensor of the Boundary Shear Stress in Fluid Flow

The flow fields and boundary erosion that are associated with scour at bridge piers are very complex. Direct measurement of the boundary shear stress and boundary pressure fluctuations in experimental scour research has always been a challenge and high spatial resolution and fidelity have been almost impossible. Most researchers have applied an indirect process to determine shear stress using precise measured velocity profiles. Laser Doppler Anemometry and Particle Image Velocimetry are common techniques used to accurately measure velocity profiles. These methods are based on theoretical assumptions to estimate boundary shear stress. In addition, available turbulence models cannot very well account for the effect of bed roughness which is fundamentally important for any CFD simulation. The authors have taken on the challenge to advance the magnitude level to which direct measurements of the shear stress in water flow can be performed. This paper covered the challenges and the efforts to develop a higher accuracy and small spatial resolution sensor. Also, preliminary sensor designs and test results are presented.

Badescu, Mircea↗

NASA NTRS1993

Grand challenge scientific questions in coupled modeling

Most convective field experiments in the past (e.g., SESAME, CCOPE, CINDE) have attempted to resolve only the immediate scales of moist convection using network arrays that spanned two or three atmospheric scales at most. Furthermore, these scales have been defined more on practical considerations (cost, manpower, etc.) than on a clear understanding of their theoretical significance. Unfortunately, this has precluded a description of the entire life cycle of MCS's and their interaction with larger scale systems, the land surface, and trace species. Fortunately, the following factors now make it possible to attempt to simulate scale contraction processes from the synoptic scale down to the cloud scale, as well as interactions between complex meteorological, land surface, precipitation, chemical, and hydrologic processes with coupled, multiscale models: the availability of new technology to sample meteorological fields at high temporal and spatial resolution over a broad region made possible by the weather observing modernization program; increased computer power and improved numerical approaches to run limited area models with nonhydrostatic precipitation physics so as to explicitly resolve MCS (Mesoscale Convective System) processes; and four dimensional assimilation of non-conventional data to provide dynamically consistent datasets for diagnostic analysis of nonlinear scale-interactive dynamics. Several examples of scale-interactive processes which present grand challenges for coupled, multiscale modeling were presented.

Koch, Steven↗

DOE OSTI2021

Developing and testing capabilities for simulating cases with heterogeneous land/water surfaces in a novel atmospheric large eddy simulation code

Large eddy simulations (LES) are the primary computational tool used to simulate high Reynolds number three-dimensional turbulent flows. In the context of earth system sciences, particularly atmospheric science, LES are uniquely able to resolve the scales of atmospheric motion that are key for building process-level understanding of boundary layer turbulence, atmosphere-surface interaction, clouds, and cloud-aerosol-chemistry interaction, and are a core limited-area modeling capability. Increasing demands are being placed on LES code bases as growing high performance computing resources allow LES to address a wider range of scientific problems. In addition, LES are emerging as a source of high-quality machine learning training data. These demands necessitate an agile and extensible code base that allows the model to quickly adapt to emergent needs. However, LES have largely relied on legacy Fortran code bases that lack flexibility. A new, Python-based LES capability called Predicting INteractions of Aerosol and Clouds in Large Eddy Simulation (PINACLES) has been developed as part of the Department of Energy’s Earth System Model Development (ESMD) program area’s Enabling Aerosol-cloud interactions at Global convection-permitting scalES (EAGLES) project. PINACLES was developed from the ground up with a philosophy of maximizing scientific throughput, by attempting to optimize for both model throughput and software extensibility. The initial development of PINACLES delivered a state-of-the-art idealized LES capability solving the non-hydrostatic anelastic equations of motion with doubly periodic boundary conditions and idealized homogenous surface boundary conditions. Here we provide a final report on the outcomes of a fiscal year 2021 Seed Laboratory Directed Research Project that extended PINACLES in two key ways. First, PINACLES was coupled to a state-of-the-art land surface model enabling it to simulate spatially inhomogeneous land-atmosphere interactions that are known to control key atmospheric processes. Second, the dynamical core of PINACLES was modified to permit non-periodic boundary conditions. This model enhancement enables simulation of realistic cases with boundary conditions prescribed from atmospheric reanalysis and enables nested simulations conducted on a hierarchy of computational domains with increasing resolution. Together, these extensions to PINACLES make it a formidable modeling capability and expand its potential application to diverse components of DOE’s atmospheric science portfolio.

42 ENGINEERING↗

NASA NTRS2001

A dissociation between attention and selection

It is widely assumed that the allocatian of spatial attention results in the "selection" of attended objects or regions of space. That is, once a stimulus is attended, all its feature dimensions are processed irrespective of their relevance to behavioral goals. This assumption is based in part on experiments showing significant interference for attended stimuli when the response to an irrelevant dimension conflicts with the response to the relevant dimension (e.g., the Stroop effect). Here we show that such interference is not due to attending per se. In two spatial cuing experiments, we found that it was possible to restrict processing of attended stimuli to task-relevant dimensions. This new evidence supports two novel conclusions: (a) Selection involves more than the focusing of attention per se: and (b) task expectations play a key role in detertnining the depth of processing of the elementary feature dimensions of attended stimuli.

Attention↗

DOE OSTI2023

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗