Muography. Группа авторов
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Название: Muography

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: Физика

Серия:

isbn: 9781119723066

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СКАЧАТЬ Muographic Image Processing

       Seigo Miyamoto1,2 and Shogo Nagahara1,3

       1 Earthquake Research Institute, The University of Tokyo, Tokyo, Japan

       2 International Virtual Muography Institute, Global

       3 Graduate School of Human Development and Environment Kobe University, Kobe, Japan

      ABSTRACT

      This chapter introduces two mathematical methods to reconstruct three‐dimensional density images from multi‐directional muographic images. One method is a linear inversion, while the other is a filtered back projection. Linear joint inversion has been studied in recent years to effectively incorporate both gravity and muon flux, which are density‐sensitive observables, as well as other a priori information. Since it can be also applied to multi‐directional muography, the basic theory of this method is described. The filtered back projection method is widely common in the medical field; however, intensity, angle of the beam, and number of detectors are considerably different in observation of large structures such as volcanoes. In order to resolve these differences and to make filtered back projection applicable to volcanic observations, we have introduced an improved geometric approximation and a method to estimate the error due to the statistics of muons. We also evaluate the performance of these two methods with a forward simulation and describe future research avenues.

      The internal structure of a volcanic body reflects the eruption dynamics and history of activity. The shallow structure of a volcano controls the eruptive activity. For example, Koyaguchi and Suzuki (2018) reported that whether an eruption forms a pyroclastic fall or flow deposit during an explosive eruption depends on the shape of the shallow crater and conduit. Therefore, density imaging of the shallow volcanic structure can provide insights into such processes.

      The technology for muon detection is improving (e.g., Morishima et al., 2017; Oláh et al., 2018). Nagahara and Miyamoto (2018) studied the feasibility of multi‐directional muon tomography of a volcano based on a simulation using the filtered back projection method. These technological and analytical developments will make multi‐directional muon tomography more feasible. In this chapter, we introduce two methods to reconstruct the three‐dimensional density structure of a body using multi‐directional muographic images. We also evaluate the performance of our methods.

      In this section, we introduce the linear inversion method used by Nishiyama et al. (2014a). The method is based on the theory described in Tarantola (2005). By assuming that the observed number of muons and density probability distribution is Gaussian, the problem can be solved with a least‐squares method by introducing a priori information about the density structure as elements of the covariance matrix.

      If the volume of interest is subdivided into voxels with index j and density ρ j (j = 1, 2, …), then the relationship between density length d i and ρ j can be represented as: