Uncertainty In Reconstruction Research Articles

Optimum image alignment setting selection for structure-from-motion photogrammetry using Remotely Piloted Aircraft Systems (RPAS) to support coral habitat classification

Coral reefs provide a range of ecological services and support highly diverse coastal ecosystem. However, the extent of living corals has been reduced by half globally as a result of anthropogenic natural stressors. Continuous monitoring by deploying accessible remote sensing (RS) methods to map coral habitats is necessary for effective conservation and management strategies. Recently, there has been an increased use of Remotely Piloted Aircraft Systems (RPAS) for coral reef RS. Image misalignment is considered a key problem in the Structure from Motion (SfM) workflow. Combinations of image alignment settings (IAS) and key point limit (KPL) optimizations aim to improve quality of the sparse cloud while lowering SfM reconstruction uncertainty, increasing projection accuracy, and ultimately improving coral habitat classification accuracy. In this study, the orthoimages were produced from a total of 25 IAS (lowest, low, medium, high and highest) and KPM (5k, 10k, 20k, 40k, and 60k) combinations using Agisoft™ Metashape software. Measurements of geometric distortion, efficiency, and completeness were used to evaluate these orthoimages with three visible bands. Results that satisfied the requirements for both geometric quality, spectral accuracy, processing efficiency identified the optimum alignment methods needed for routinely monitoring and mapping of coral reefs of Pulau Bidong. The SfM-image alignment techniques chosen for this study could produce the coral mapping extent, a higher number of tie points and matches, better image alignment success and coral habitat classification accuracy, less processing time and memory usage and no geometric distortions. The development of the methodology for the optimum parametrization of RPAS multispectral imagery would be beneficial to researchers studying coral reefs, marine sciences, and RS data analysts. It would provide them reliable methods for evaluating the quality of orthoimages and faster processing methods to achieve coastal RS objectives.

The Roofing System of the Tholos of Athena Pronaia in Delphi: Archaeological Hypotheses and Structural Suggestions

ABSTRACT This study aims to provide an integrated understanding of archaeological findings by evaluating reconstruction hypotheses from a technological and structural perspective, focusing on the Tholos of Athena Pronaia in Delphi, a prime example of 4th and 3rd-century BC Greek architecture. Archaeological research primarily focuses on proportional rules and decorative aspects. The original roof configuration remains uncertain despite its architectural significance and characteristic interlocking marble tiles. The investigation of structural problems is crucial for selecting the most feasible reconstructions and formulating hypotheses on materials, dimensions of structural elements, and how connections between elements were constructed. Due to the many reconstruction uncertainties, a reverse design approach was employed, utilizing schematic technological sections based on the known structural and technical knowledge of the time to ensure consistency between archaeological images and potential underlying structural diagrams. Variables were considered for uncertain dimensions, and the structure’s safety was analysed in terms of material strength and stability. This highlighted not only unsafe solutions but also potential novelties in structural conceptions. This contribution serves as a starting point for research, placing structural mechanics at the service of archaeology with a focus on the tholos roof system.

A model for photon counting X-ray event reconstruction uncertainty

Evaluation of detectors for a soft X-ray imaging spectrometer has resulted in the need to understand the effect of charge spreading on apparent detector noise properties, and therefore achievable energy resolution. This paper presents a mathematical model for the processes leading to increased uncertainty within a simplified X-ray reconstruction process. This is a description for additional uncertainty introduced by the process of collecting X-ray generated electrons into a region of noisy pixels and reconstructing the recorded pixels values back into an estimated X-ray energy value. The predictions of the model, and preliminary experimental verification are shown.

Variability of Uncertainty in Mental Reconstruction of Danger and Safety

Subjective images of danger and security depend on the context of uncertainty. This article introduces a mental reconstruction of danger and safety for different types of uncertainty. The authors used the methods of associative experiment, subjective ranking, and semantic differential. The empirical data were processed by averaging, ranking, factor analysis, and the Mann-Whitney criterion. The survey involved students from several Russian metropolitan and provincial universities (19–20 y.o.; n = 105; 74 females). Danger demonstrated a less complex mental structure than safety. The mental reconstruction of danger / safety included three types of uncertainty. Temporal uncertainty included period, time, and frequency of danger / safety. Objective uncertainty was connected with the place, environment, and source of danger / safety. Subjective uncertainty covered controllability, scale, and cause of danger / safety. Uncertainty proved more important for danger than for safety. The subjective level of danger grew together with objective uncertainty, especially when the source of danger was unclear, and went down with temporal uncertainty. The subjective level of security increased with temporal and subjective uncertainty and decreased with objective uncertainty. When the subjective significance of danger and safety went up, the differentiation of uncertainty in their assessment became less significant. The study identified the effect of variability of uncertainty on the mental reconstruction made by the subjects of danger / security, as well as identified some understudied areas.

Convex model-based regularization method for force reconstruction

In the process of reconstructing structural forces, the influence of measurement errors and inherent model inaccuracies cannot be ignored. These errors exhibit a degree of correlation, and the presence of such correlation inevitably affects the quantification of uncertainties in force reconstruction. Objectively, the inherent ill-posed nature of structural inverse problems makes it difficult to obtain the forces to be identified, subject to uncertainties and highly susceptible to perturbations. Consequently, this paper introduces a force reconstruction regularization approach that explicitly considers the correlation of uncertainty parameters based on the truncated singular value regularization method. The primary objective is to refine the influence of uncertainties on the reconstructed force bounds with greater precision. When determining robust regularization parameters, the generalized cross-validation method and convex modelling approach are introduced to consider the uncertainty and its correlation in solving inverse problems. The proposed approach is rigorously validated through a comprehensive numerical case study. Error indexes and dispersion indices are employed to analyze the impact of different levels of noise and correlation on force reconstruction results. The force bounds obtained using the proposed method are compared with Monte Carlo simulation results. Finally, the validity of the proposed method is verified by an experiment with a four-story shear frame.

Modeling the timing of Patagonian Ice Sheet retreat in the Chilean Lake District from 22–10 ka

Abstract. Studying the retreat of the Patagonian Ice Sheet (PIS) during the last deglaciation represents an important opportunity to understand how ice sheets outside the polar regions have responded to deglacial changes in temperature and large-scale atmospheric circulation. At the northernmost extension of the PIS during the Last Glacial Maximum (LGM), the Chilean Lake District (CLD) was influenced by the southern westerly winds (SWW), which strongly modulated the hydrologic and heat budgets of the region. Despite progress in constraining the nature and timing of deglacial ice retreat across this area, considerable uncertainty in the glacial history still exists due to a lack of geologic constraints on past ice margin change. Where the glacial chronology is lacking, ice sheet models can provide important insight into our understanding of the characteristics and drivers of deglacial ice retreat. Here we apply the Ice Sheet and Sea-level System Model (ISSM) to simulate the LGM and last deglacial ice history of the PIS across the CLD at high spatial resolution (450 m). We present a transient simulation of ice margin change across the last deglaciation using climate inputs from the National Center for Atmospheric Research Community Climate System Model (CCSM3) Trace-21ka experiment. At the LGM, the simulated ice extent across the CLD agrees well with the most comprehensive reconstruction of PIS ice history (PATICE). Coincident with deglacial warming, ice retreat ensues after 19 ka, with large-scale ice retreat occurring across the CLD between 18 and 16.5 ka. By 17 ka, the northern portion of the CLD becomes ice free, and by 15 ka, ice only persists at high elevations as mountain glaciers and small ice caps. Our simulated ice history agrees well with PATICE for early deglacial ice retreat but diverges at and after 15 ka, where the geologic reconstruction suggests the persistence of an ice cap across the southern CLD until 10 ka. However, given the high uncertainty in the geologic reconstruction of the PIS across the CLD during the later deglaciation, this work emphasizes a need for improved geologic constraints on past ice margin change. While deglacial warming drove the ice retreat across this region, sensitivity tests reveal that modest variations in wintertime precipitation (∼10 %) can modulate the pacing of ice retreat by up to 2 ka, which has implications when comparing simulated outputs of ice margin change to geologic reconstructions. While we find that TraCE-21ka simulates large-scale changes in the SWW across the CLD that are consistent with regional paleoclimate reconstructions, the magnitude of the simulated precipitation changes is smaller than what is found in proxy records. From our sensitivity analysis, we can deduce that larger anomalies in precipitation, as found in paleoclimate proxies, may have had a large impact on modulating the magnitude and timing of deglacial ice retreat. This fact highlights an additional need for better constraints on the deglacial change in strength, position, and extent of the SWW as it relates to understanding the drivers of deglacial PIS behavior.

Uncertainty-Aware GAN for Single Image Super Resolution

Generative adversarial network (GAN) has become a popular tool in the perceptual-oriented single image super-resolution (SISR) for its excellent capability to hallucinate details. However, the performance of most GAN-based SISR methods is impeded due to the limited discriminative ability of their discriminators. In specific, these discriminators only focus on the global image reconstruction quality and ignore the more fine-grained reconstruction quality for constraining the generator, as they predict the overall realness of an image instead of the pixel-level realness. Here, we first introduce the uncertainty into the GAN and propose an Uncertainty-aware GAN (UGAN) to regularize SISR solutions, where the challenging pixels with large reconstruction uncertainty and importance (e.g., texture and edge) are prioritized for optimization. The uncertainty-aware adversarial training strategy enables the discriminator to capture the pixel-level SR uncertainty, which constrains the generator to focus on image areas with high reconstruction difficulty, meanwhile, it improves the interpretability of the SR. To balance weights of multiple training losses, we introduce an uncertainty-aware loss weighting strategy to adaptively learn the optimal loss weights. Extensive experiments demonstrate the effectiveness of our approach in extracting the SR uncertainty and the superiority of the UGAN over the state-of-the-arts in terms of the reconstruction accuracy and perceptual quality.

A multi-objective optimization model to minimize the evacuation time during a disaster considering reconstruction activity and uncertainty: A case study of Cork City

Disasters can potentially disrupt transportation systems, resulting in complete or partial blockages of specific links, which can limit the available options for transportation. During a disaster, crucial decisions must be made, such as planning the safe evacuation of people from affected areas and strategizing to repair damaged transportation links. The primary objective of this research is to develop a multi-objective optimization framework that can enhance the effectiveness of transportation networks in natural disasters, such as floods. To achieve this, the proposed model incorporates fuzzy set theory since it is challenging to determine parameters like capacity precisely in these situations. The model assumes that some damaged links can be reconstructed within the available resources and budget. The optimization model focuses on minimizing the total evacuation time and reconstruction cost, and its effectiveness is validated using a case study of Cork City with 100 nodes and 141 links. The model is solved, and optimal solutions are generated using an exact method to handle various uncertainty scenarios. The results demonstrate that developing the fuzzy optimization approach as an analytical tool can be used to make critical decisions for evacuation planning and emergency management.

Predictive uncertainty in deep learning-based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set.

To estimate pixel-wise predictive uncertainty for deep learning-based MR image reconstruction and to examine the impact of domain shifts and architecture robustness. Uncertainty prediction could provide a measure for robustness of deep learning (DL)-based MR image reconstruction from undersampled data. DL methods bear the risk of inducing reconstruction errors like in-painting of unrealistic structures or missing pathologies. These errors may be obscured by visual realism of DL reconstruction and thus remain undiscovered. Furthermore, most methods are task-agnostic and not well calibrated to domain shifts. We propose a strategy that estimates aleatoric (data) and epistemic (model) uncertainty, which entails training a deep ensemble (epistemic) with nonnegative log-likelihood (aleatoric) loss in addition to the conventional applied losses terms. The proposed procedure can be paired with any DL reconstruction, enabling investigations of their predictive uncertainties on a pixel level. Five different architectures were investigated on the fastMRI database. The impact on the examined uncertainty of in-distributional and out-of-distributional data with changes to undersampling pattern, imaging contrast, imaging orientation, anatomy, and pathology were explored. Predictive uncertainty could be captured and showed good correlation to normalized mean squared error. Uncertainty was primarily focused along the aliased anatomies and on hyperintense and hypointense regions. The proposed uncertainty measure was able to detect disease prevalence shifts. Distinct predictive uncertainty patterns were observed for changing network architectures. The proposed approach enables aleatoric and epistemic uncertainty prediction for DL-based MR reconstruction with an interpretable examination on a pixel level.

3D evolution of a solar flare thermal X-ray loop-top source

Context. The recent launch of Solar Orbiter has placed a solar X-ray imager (Spectrometer/Telescope for Imaging X-rays; STIX) beyond Earth orbit for the first time. This introduces the possibility of deriving the 3D locations and volumes of solar X-ray sources by combining STIX observations with those of Earth-orbiting instruments such as the Hinode X-ray Telescope (XRT). These measurements promise to improve our understanding of the evolution and energetics of solar flares. However, substantial design differences between STIX and XRT present important challenges that must first be overcome. Aims. We aim to: 1) explore the validity of combining STIX and XRT for 3D analysis given their different designs, 2) understand uncertainties associated with 3D reconstruction and their impact on the derived volume and thermodynamic properties, 3) determine the validity of the scaling law that is traditionally used to estimate source volumes from single-viewpoint observations, 4) chart the temporal evolution of the location, volume, and thermodynamic properties of a thermal X-ray loop-top source of a flare based on a 3D reconstruction for the first time. Methods. The SOL2021-05-07T18:43 M3.9-class flare is analysed using co-temporal observations from STIX and XRT, which, at the time, were separated by an angle of 95.4° relative to the flare site. The 3D reconstruction is performed via elliptical tie-pointing and the visualisation by JHelioviewer, which is enabled by new features developed for this project. Uncertainties associated with the 3D reconstruction are derived from an examination of projection effects given the observer separation angle and the source orientation and elongation. Results. Firstly, we show that it is valid to combine STIX 6–10 keV and XRT Be-thick observations for 3D analysis for the flare examined in this study. However, the validity of doing so in other cases may depend on the nature of the observed source. Therefore, careful consideration should be given on a case-by-case basis. Secondly, the optimal observer separation angle for 3D reconstruction is 90° ± 5°, but the uncertainties are still relatively small in the range 90° ± 20°. Other angles are viable, but are associated with higher uncertainties, which can be quantified. Thirdly, the traditional area-to-volume scaling law may overestimate the 3D-derived volume of the thermal X-ray loop-top source studied here by over a factor of 2. This is beyond the uncertainty of the 3D reconstruction. The X-ray source was not very asymmetric, and so the overestimation may be greater for more elongated sources. In addition, the degree of overestimation can vary with time and viewing angle, demonstrating that the true source geometry can evolve differently in different dimensions. 3D reconstruction is therefore necessary to derive more reliable volumes. Simply applying a modified scaling law to single-viewpoint observations is not sufficient. Finally, the vertical motion of the X-ray source is consistent with previous observations of limb flares. This indicates that 3D reconstruction by elliptical tie-pointing provides reliable 3D locations. The uncertainties of thermodynamic properties derived from volume, temperature, and/or emission measure are dominated by those of the volume. In contrast to single-viewpoint studies, observationally constrained volume uncertainties can be assigned via 3D reconstruction, which lends quantifiable credibility to scientific conclusions drawn from the derived thermodynamic properties.

Reconstruction of neutron spectrum emitted by portable 14 MeV neutron generator by a combination of different methods

The presented paper is a continuation of the Tikhonov Regularisation, Minimum Fisher Information, Maximum Entropy, and Maximum Likelihood methods application to unfolding the neutron spectra emitted by the fusion devices from activation measurement. The aim of the analysis is the evaluation of the possible uncertainty improvement. The developed algorithms and methodology were validated with 14 MeV portable neutron generators before application to the analysis of the neutron spectrum emitted from tokamak. The analysis concerns plasma-target generator did not provide satisfactory uncertainty of the results. The modification of the irradiation foils’ set and the improvement in the activity measurement precision progress the results. The sealed tube portable neutron generator irradiated the Al, Fe, Ni, Zr, Au, Mg, and Nb foils. The HPGe spectrometer measured the induced activity with an uncertainty of about 10 %. The synthetic data analysis was made to establish the ratio between the error of the spectrum reconstruction and the discrepancy between the input data and activities reconstructed for this spectrum by the FISPACT-II inventory code. Two bin structures were considered. One was compatible with the FISPACT-II code, but the energy resolution was not regular. The second was characterised by constant bin spacing connected with the modification requirement before simulation and additional uncertainty of about 5 %. The best results were obtained by calculation of the mean value from all methods. The corresponding reconstruction uncertainty is equal to 11 %. The reconstructed spectrum has a dominant deuterium tritium peak slightly shifted to the higher energies due to the beam-target reaction kinematics. The evaluation of the impact of the deuterium-deuterium reaction due to the long-term operation was not reachable due to the measured high intensity from scattered neutrons.

Approaches for constraining uncertainty and degeneracy in geometry reconstruction of molecules from simulated Coulomb explosion data

Coulomb explosion imaging (CEI) data, consisting of atomic ion momenta, resulting from a rapid (few fs) ionization event, is typically recorded as a first step in the generation of a single molecule geometry measurement or the production of a ‘molecular movie’ that watches the evolution of an excited state geometry in time. Deriving a reliable geometry from this data is a crucial and challenging step, as unique solutions may not exist. In this work, we start by simulating the geometries of a molecule, which is out of equilibrium, the asymmetric carbonyl sulfide (OCS) molecule. We generate momentum data resulting from six-fold ionization and investigate two methods to reconstruct geometries. The first method is a look-up table, which is simple in principle but shows limitations in terms of accuracy and ability to identify multiple ‘degenerate’ solutions. The second is a new method using nonlinear constrained optimization, which shows potential for scalability to higher dimension. We investigate the possibility of identifying problematic geometry regions where different degenerate geometries can not be distinguished.

Using Gaussian process for velocity reconstruction after coronary stenosis applicable in positron emission particle tracking: An in-silico study

Accurate velocity reconstruction is essential for assessing coronary artery disease. We propose a Gaussian process method to reconstruct the velocity profile using the sparse data of the positron emission particle tracking (PEPT) in a biological environment, which allows the measurement of tracer particle velocity to infer fluid velocity fields. We investigated the influence of tracer particle quantity and detection time interval on flow reconstruction accuracy. Three models were used to represent different levels of stenosis and anatomical complexity: a narrowed straight tube, an idealized coronary bifurcation with stenosis, and patient-specific coronary arteries with a stenotic left circumflex artery. Computational fluid dynamics (CFD), particle tracking, and the Gaussian process of kriging were employed to simulate and reconstruct the pulsatile flow field. The study examined the error and uncertainty in velocity profile reconstruction after stenosis by comparing particle-derived flow velocity with the CFD solution. Using 600 particles (15 batches of 40 particles) released in the main coronary artery, the time-averaged error in velocity reconstruction ranged from 13.4% (no occlusion) to 161% (70% occlusion) in patient-specific anatomy. The error in maximum cross-sectional velocity at peak flow was consistently below 10% in all cases. PEPT and kriging tended to overestimate area-averaged velocity in higher occlusion cases but accurately predicted maximum cross-sectional velocity, particularly at peak flow. Kriging was shown to be useful to estimate the maximum velocity after the stenosis in the absence of negative near-wall velocity.

Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions. Current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) method to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability, a filtered rFRC is combined with a modified resolution-scaled error map (RSM), offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Overall, we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

A Real-Time and Robust Neural Network Model for Low-Measurement-Rate Compressed-Sensing Image Reconstruction.

Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world applications. Existing end-to-end learning methods achieved real time sensing but lack theory guarantee for robust reconstruction results. This paper proposes a neural network called RootsNet, which integrates the CS mechanism into the network to prevent error propagation. So, RootsNet knows what will happen if some modules in the network go wrong. It also implements real-time and successfully reconstructed extremely low measurement rates that are impossible for traditional optimization-theory-based methods. For qualitative validation, RootsNet is implemented in two real-world measurement applications, i.e., a near-field microwave imaging system and a pipeline inspection system, where RootsNet easily saves 60% more measurement time and 95% more data compared with the state-of-the-art optimization-theory-based reconstruction methods. Without losing generality, comprehensive experiments are performed on general datasets, including evaluating the key components in RootsNet, the reconstruction uncertainty, quality, and efficiency. RootsNet has the best uncertainty performance and efficiency, and achieves the best reconstruction quality under super low-measurement rates.

Otolith-temperature estimates in Atlantic bluefin tuna (Thunnus thynnus) from the Mediterranean Sea: Insights from clumped isotope measurements

The subpopulation and/or contingent structure of Atlantic bluefin tuna (Thunnus thynnus) within the Mediterranean Sea is undefined, leading to uncertainty regarding the best strategy for an effective assessment and management of this highly exploited stock. This study aimed to reconstruct temperatures experienced by Atlantic bluefin tuna during the early life period (<3.5 months) using clumped isotope temperature proxy, an innovative geothermometer for carbonates, that does not require previous knowledge of other environmental parameters such as water oxygen composition. We examined otolith chemistry in fish captured from 3 different areas of the Mediterranean Sea and adjacent waters. We found that mean seasonal temperature estimates from clumped isotopes did not differ significantly from satellite derived and otolith oxygen stable isotopic ratios derived temperatures, except for the central Mediterranean Sea, were clumped isotopes derived temperatures were significantly higher than satellite derived temperatures. However, the sensitivity of the clumped isotope thermometer was found to be lower than that based on oxygen fractionation equation, with high variance observed in the clumped isotopes derived temperature estimates. We also observed that clumped isotope derived temperatures were undistinguishable among bluefin tuna captured in the Gibraltar Strait, the central, and eastern Mediterranean Sea. In this paper, we discuss the major sources of uncertainty in temperature reconstructions using bluefin tuna otoliths.

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders * *Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide

Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

Evaluation of Electron Tomography Capabilities for Shale Imaging.

Despite the advantageous resolution of electron tomography (ET), reconstruction of three-dimensional (3D) images from multiple two-dimensional (2D) projections presents several challenges, including small signal-to-noise ratios, and a limited projection range. This study evaluates the capabilities of ET for thin sections of shale, a complex nanoporous medium. A numerical phantom with 1.24 nm pixel size is constructed based on the tomographic reconstruction of a Barnett shale. A dataset of 2D projection images is numerically generated from the 3D phantom and studied over a range of conditions. First, common reconstruction techniques are used to reconstruct the shale structure. The reconstruction uncertainty is quantified by comparing overall values of storage and transport metrics, as well as the misclassification of pore voxels compared to the phantom. We then select the most robust reconstruction technique and we vary the acquisition conditions to quantify the effect of artifacts. We find a strong agreement for large pores over the different acquisition workflows, while a wider variability exists for nanometer-scale features. The limited projection range and reconstruction are identified as the main experimental bottlenecks, thereby suggesting that sample thinning, advanced holders, and advanced reconstruction algorithms offer opportunities for improvement.

Reconstruction Method of 3D Turbulent Flames by Background-Oriented Schlieren Tomography and Analysis of Time Asynchrony

Background-oriented Schlieren tomography (BOST) is widely used for 3D reconstruction of turbulent flames. Two major concerns are associated with 3D reconstruction. One is the time asynchrony within the data acquisition of the high-speed camera. The other is that the ray tracing process requires significant computational consumption. This study proposes a ray tracing optimization method based on the k-d tree. The study results show that the average search nodes for each ray are only 0.018% of 3D flame with 3.07 million grid nodes. In addition, a parameter estimation method of the unknown azimuth power spectrum function is proposed. First, a typical Sandia turbulent jet diffusion flame dataset was built and validated accordingly, with experiments. The algorithm’s applicability to the 3D reconstruction of temperature and density fields is discussed on this basis. The root-mean-square error (RMSE) of the cross-section density for 3D reconstruction is below 0.1 kg/m3. In addition, the RMSE of the cross-section temperature is below 270 K. Finally, an uncertainty analysis of the flame reconstruction based on a physical model is performed by optimizing the ray tracing method. For the time asynchronous variance of 1 ms, the density uncertainty of the 3D reconstruction is below 1.6 × 10−2 kg/m3, and the temperature uncertainty is below 70 K. The method can provide an essential basis for the design of BOST systems and the 3D reconstruction of turbulent flames.

Impact of global climate cooling on Ordovician marine biodiversity

Global cooling has been proposed as a driver of the Great Ordovician Biodiversification Event, the largest radiation of Phanerozoic marine animal Life. Yet, mechanistic understanding of the underlying pathways is lacking and other possible causes are debated. Here we couple a global climate model with a macroecological model to reconstruct global biodiversity patterns during the Ordovician. In our simulations, an inverted latitudinal biodiversity gradient characterizes the late Cambrian and Early Ordovician when climate was much warmer than today. During the Mid-Late Ordovician, climate cooling simultaneously permits the development of a modern latitudinal biodiversity gradient and an increase in global biodiversity. This increase is a consequence of the ecophysiological limitations to marine Life and is robust to uncertainties in both proxy-derived temperature reconstructions and organism physiology. First-order model-data agreement suggests that the most conspicuous rise in biodiversity over Earth’s history – the Great Ordovician Biodiversification Event – was primarily driven by global cooling.