Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive ...coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and Ito process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum-Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals.
California's methane super-emitters Duren, Riley M; Thorpe, Andrew K; Foster, Kelsey T ...
Nature,
11/2019, Volume:
575, Issue:
7781
Journal Article
Peer reviewed
Open access
Methane is a powerful greenhouse gas and is targeted for emissions mitigation by the US state of California and other jurisdictions worldwide
. Unique opportunities for mitigation are presented by ...point-source emitters-surface features or infrastructure components that are typically less than 10 metres in diameter and emit plumes of highly concentrated methane
. However, data on point-source emissions are sparse and typically lack sufficient spatial and temporal resolution to guide their mitigation and to accurately assess their magnitude
. Here we survey more than 272,000 infrastructure elements in California using an airborne imaging spectrometer that can rapidly map methane plumes
. We conduct five campaigns over several months from 2016 to 2018, spanning the oil and gas, manure-management and waste-management sectors, resulting in the detection, geolocation and quantification of emissions from 564 strong methane point sources. Our remote sensing approach enables the rapid and repeated assessment of large areas at high spatial resolution for a poorly characterized population of methane emitters that often appear intermittently and stochastically. We estimate net methane point-source emissions in California to be 0.618 teragrams per year (95 per cent confidence interval 0.523-0.725), equivalent to 34-46 per cent of the state's methane inventory
for 2016. Methane 'super-emitter' activity occurs in every sector surveyed, with 10 per cent of point sources contributing roughly 60 per cent of point-source emissions-consistent with a study of the US Four Corners region that had a different sectoral mix
. The largest methane emitters in California are a subset of landfills, which exhibit persistent anomalous activity. Methane point-source emissions in California are dominated by landfills (41 per cent), followed by dairies (26 per cent) and the oil and gas sector (26 per cent). Our data have enabled the identification of the 0.2 per cent of California's infrastructure that is responsible for these emissions. Sharing these data with collaborating infrastructure operators has led to the mitigation of anomalous methane-emission activity
.
Methane (CH₄) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners ...region in the Southwest United States as an area of large CH₄ enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit ∼ 2 kg/h to 5 kg/h through ∼ 5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognormal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real time provides an efficient and effective method to identify and mitigate major emissions contributors over a wide geographic area. With improved instrumentation, this capability scales to spaceborne applications Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571–6578. Further illustration of this potential is demonstrated with two detected, confirmed, and repaired pipeline leaks during the campaign.
We describe advanced spectral and radiometric calibration techniques developed for NASA’s Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). By employing both statistically ...rigorous analysis and utilizing in situ data to inform calibration procedures and parameter estimation, we can dramatically reduce undesirable artifacts and minimize uncertainties of calibration parameters notoriously difficult to characterize in the laboratory. We describe a novel approach for destriping imaging spectrometer data through minimizing a Markov Random Field model. We then detail statistical methodology for bad pixel correction of the instrument, followed by the laboratory and field protocols involved in the corrections and evaluate their effectiveness on historical data. Finally, we review the geometric processing procedure used in production of the radiometrically calibrated image data.
Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up ...observations. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and focus attention on the most promising or novel observations. Several novelty detection methods have been explored in prior work for three-channel color images and non-image datasets, but few have considered multispectral or hyperspectral image datasets for the purpose of scientific discovery. We compared the performance of four novelty detection methods—Reed Xiaoli (RX) detectors, principal component analysis (PCA), autoencoders, and generative adversarial networks (GANs)—and the ability of each method to provide explanatory visualizations to help scientists understand and trust predictions made by the system. We show that pixel-wise RX and autoencoders trained with structural similarity (SSIM) loss can detect morphological novelties that are not detected by PCA, GANs, and mean squared error autoencoders, but that the latter methods are better suited for detecting spectral novelties—i.e., the best method for a given setting depends on the type of novelties that are sought. Additionally, we find that autoencoders provide the most useful explanatory visualizations for enabling users to understand and trust model detections, and that existing GAN approaches to novelty detection may be limited in this respect.
Type Ia supernovae are destructive explosions of carbon-oxygen white dwarfs. Although they are used empirically to measure cosmological distances, the nature of their progenitors remains mysterious. ...One of the leading progenitor models, called the single degenerate channel, hypothesizes that a white dwarf accretes matter from a companion star and the resulting increase in its central pressure and temperature ignites thermonuclear explosion. Here we report observations with the Swift Space Telescope of strong but declining ultraviolet emission from a type Ia supernova within four days of its explosion. This emission is consistent with theoretical expectations of collision between material ejected by the supernova and a companion star, and therefore provides evidence that some type Ia supernovae arise from the single degenerate channel.
ABSTRACT In this article, we compare optical light curves of two SN2002es-like Type Ia supernovae (SNe), iPTF14atg and iPTF14dpk, from the intermediate Palomar Transient Factory. Although the two ...light curves resemble each other around and after maximum, they show distinct early-phase rise behavior in the r-band. On the one hand, iPTF14atg revealed a slow and steady rise that lasted for 22 days with a mean rise rate of 0.2-0.3 mag day-1, before it reached the R-band peak (−18.05 mag). On the other hand, iPTF14dpk rose rapidly to −17 mag within a day of discovery with a rise rate , and then rose slowly to its peak (−18.19 mag) with a rise rate similar to iPTF14atg. The apparent total rise time of iPTF14dpk is therefore only 16 days. We show that emission from iPTF14atg before −17 days with respect to its maximum can be entirely attributed to radiation produced by collision between the SN and its companion star. Such emission is absent from iPTF14dpk probably because of an unfavored viewing angle, provided that SN2002es-like events arise from the same progenitor channel. We further show that an SN2002es-like SN may experience a dark phase after the explosion but before its radioactively powered light curve becomes visible. This dark phase may be lit by radiation from supernova-companion interaction.
Ongoing planetary exploration missions are returning large volumes of image data. Identifying surface changes in these images, e.g., new impact craters, is critical for investigating many scientific ...hypotheses. Traditional approaches to change detection rely on image differencing and manual feature engineering. These methods can be sensitive to irrelevant variations in illumination or image quality and typically require before and after images to be coregistered, which itself is a major challenge. Additionally, most prior change detection studies have been limited to remote sensing images of earth. We propose a new deep learning approach for binary patch-level change detection involving transfer learning and nonlinear dimensionality reduction using convolutional autoencoders. Our experiments on diverse remote sensing datasets of Mars, the moon, and earth show that our methods can detect meaningful changes with high accuracy using a relatively small training dataset despite significant differences in illumination, image quality, imaging sensors, coregistration, and surface properties. We show that the latent representations learned by a convolutional autoencoder yield the most general representations for detecting change across surface feature types, scales, sensors, and planetary bodies.
At local scales, emissions of methane and carbon dioxide are highly uncertain. Localized sources of both trace gases can create strong local gradients in its columnar abundance, which can be ...discerned using absorption spectroscopy at high spatial resolution. In a previous study, more than 250 methane plumes were observed in the San Juan Basin near Four Corners during April 2015 using the next-generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and a linearized matched filter. For the first time, we apply the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) method to AVIRIS-NG data and generate gas concentration maps for methane, carbon dioxide, and water vapor plumes. This demonstrates a comprehensive greenhouse gas monitoring capability that targets methane and carbon dioxide, the two dominant anthropogenic climate-forcing agents. Water vapor results indicate the ability of these retrievals to distinguish between methane and water vapor despite spectral interference in the shortwave infrared. We focus on selected cases from anthropogenic and natural sources, including emissions from mine ventilation shafts, a gas processing plant, tank, pipeline leak, and natural seep. In addition, carbon dioxide emissions were mapped from the flue-gas stacks of two coal-fired power plants and a water vapor plume was observed from the combined sources of cooling towers and cooling ponds. Observed plumes were consistent with known and suspected emission sources verified by the true color AVIRIS-NG scenes and higher-resolution Google Earth imagery. Real-time detection and geolocation of methane plumes by AVIRIS-NG provided unambiguous identification of individual emission source locations and communication to a ground team for rapid follow-up. This permitted verification of a number of methane emission sources using a thermal camera, including a tank and buried natural gas pipeline.
In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and ...qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global coverage and granularity in order to test our models’ capabilities to represent structure at finer and broader scales, using many different kinds of instrumentation, that can be fused when applicable. In all cases tested, our models show a strong ability to segment the objects within input scenes, use multiple datasets fused together where appropriate to improve results, and, at times, outperform the pre-existing datasets. The success here will allow this methodology to be used within use concrete cases and become the basis for future dynamic object tracking across datasets from various remote sensing instruments.
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