The Fukushima inverse problem

Martinez-Camara, Marta; Ranieri, Juri; Scheibler, Robin; Vetterli, Martin; Stohl, Andreas

doi:10.1109/icassp.2013.6638477

Cited by 15 publications

(10 citation statements)

References 11 publications

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“…Martinez-Camara et al (2013) used a sparse reconstruction approach to estimate emissions of radioactive substances for the Fukushima accident, and Ray et al (2015) analyzed fossil fuel carbon dioxide emissions in an idealized, synthetic data setup.…”

Section: N Hase Et Al: Atmospheric Inverse Modeling Via Sparse Recomentioning

confidence: 99%

Atmospheric inverse modeling via sparse reconstruction

et al. 2017

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Abstract. Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots.Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH 4 ) emissions from simulated atmospheric measurements.Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.

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Section: N Hase Et Al: Atmospheric Inverse Modeling Via Sparse Recomentioning

confidence: 99%

Atmospheric inverse modeling via sparse reconstruction

et al. 2017

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“…İşte bu noktada ters problemler teorisi, kaynak fonksiyonunun belirlenmesi için alternatif bir çözüm yolu olarak ortaya çıkmaktadır. Başka bir deyişle, ölçüm noktalarından toplanan veriler ve oluşturulan matematiksel model kullanılarak radyoaktif madde salınımının zamansal değişiminin belirlenmesi ters probleminin çözümü, yukarıdaki sorunun cevabı olarak karşımıza çıkmaktadır, (Martinez-Camara et al 2013).…”

Section: Endüstrideki Ters Problemlerunclassified

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Gölgeleyen¹,

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2016

KSEJ

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bir ters problemdir. Bu belirtiler farklı sağlık sorunlarından kaynaklanabileceğinden böyle bir problemin çözümünün tek olmadığı açıktır. Matematiksel fizikte direkt problem; denklem, bölge ve koşullar verildiğinde denklemi ve koşulları sağlayan çözümün bulunması problemi olarak tanımlanır. Burada amaç, belli bir anda bölgenin belli bir noktasındaki fiziksel alanı veya süreci tanımlayan (elektromanyetik, akustik, sismik vb.) bir fonksiyonun bulunmasıdır. Ayrıca bu problemlerde ortamın özelliklerinin, fiziksel sürecin başlangıç anındaki durumunun ve/veya sınırda sağlanan özelliklerin bilindiği kabul edilir. Diğer yandan sıklıkla ortamın özelliklerinin bilinmediği durumlarla da karşılaşılmaktadır. Bu da direkt problemin çözümü hakkında verilen ek bilgi yardımıyla ilgili denklemin katsayılarının belirlenmesi ters problemi olarak karşımıza çıkmaktadır, (Amirov 2001). 2. Ters Problemler Teorisinin Bazı Uygulama Alanları Bilim ve teknolojinin farklı alanlarında ortaya çıkan çeşitli ters problemlere örnekler aşağıda sunulmuştur.

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“…A slightly different approach is the use of a sparsity constraint, together with a non-negative constraint, as in Martinez-Camara et al (2013). Yet, another point of view is given in Bocquet (2007), where both the source and the error distributions are estimated at the same time.…”

Section: Related Workmentioning

confidence: 99%

A robust method for inverse transport modeling of atmospheric emissions using blind outlier detection

et al. 2014

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Abstract. Emissions of harmful substances into the atmosphere are a serious environmental concern. In order to understand and predict their effects, it is necessary to estimate the exact quantity and timing of the emissions from sensor measurements taken at different locations. There are a number of methods for solving this problem. However, these existing methods assume Gaussian additive errors, making them extremely sensitive to outlier measurements. We first show that the errors in real-world measurement data sets come from a heavy-tailed distribution, i.e., include outliers. Hence, we propose robustifying the existing inverse methods by adding a blind outlier-detection algorithm. The improved performance of our method is demonstrated on a real data set and compared to previously proposed methods. For the blind outlier detection, we first use an existing algorithm, RANSAC, and then propose a modification called TRANSAC, which provides a further performance improvement.

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The Fukushima inverse problem

Cited by 15 publications

References 11 publications

Atmospheric inverse modeling via sparse reconstruction

Atmospheric inverse modeling via sparse reconstruction

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A robust method for inverse transport modeling of atmospheric emissions using blind outlier detection

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