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 as a porous magma pathway.

      It is difficult to directly observe phreatic explosions with muography because the gas flow doesn’t generate significant density variations inside a volcano. However, like an example shown above, indirect evidence could be captured as the structural modification as a consequence of phreatic explosion. In contrast with phreatic explosions, magmatic eruptions could be more directly captured with muography. Dense magmatic materials ascend through the highly porous pathway during magmatic eruptions. On June 4, 2013, the eruption alert level had risen from level 1 (signs of volcano unrest) to level 2 (minor eruptive activity) at Satsuma‐Iwojima volcano, Japan. Time sequential muographic images showed the ascent and descent of the magma head, which synchronized to the visual observation timings of volcanic glows during this eruption episode (Tanaka et al., 2014). Currently, the automatic analysis and data visualization system is available for volcano muography (Tanaka et al., 2020c). The resultant muographic images were similar to medical radiographic images. In the field of medical imaging, by taking advantage of recent deep learning techniques, for example, convolutional neural network (CNN), image processing techniques have been highly developed to realize automated medical image analysis and evaluation. A deep learning technique similar to what is used now for medical image processing has been found to be applicable to analyzing time‐sequential muographic images (Nomura et al., 2020).

      1.3.4 Plate Tectonics and Volcanism

      Muography has been also applied to studying the tectonic history of the Earth by revealing the subterranean bulk density distribution averaged over the hectokilometric‐scale area. By installing the detectors inside the multiple tunnels, which are randomly but uniformly distributed over this area, the density distribution above these tunnels can be measured. Tanaka (2015) conducted tunnel muography to the southern part of Izu peninsula, the tectonically active peninsula that was made by collision with Honshu Island, Japan, about one million years ago. The formation history of Izu peninsula will be summarized below.

Schematic illustration of density variations of the central southern region of Honshu, Japan.

      The density variations between the submarine volcano‐originated peninsula and accretionary prisms‐originated peninsulas could also be found in the results of the gravimetric measurements, which compared Izu peninsula (2.2–2.3 g/cm3) with Kii peninsula (2.5–2.7 g/cm3) (Nawa et al., 1997). The gravimetric density of Izu peninsula is in agreement with the muographic density before excluding fault segments. While gravimetric measurements provide geologically meaningful subterranean bulk density information, the derived density values are strongly affected by local topography and geology. In particular, sharp offsets generated by faults in Bouguer anomaly result in erroneous and unreasonably high or low density (Nawa et al., 1997).

      As a consequence, Tanaka et al. (2015) derived bulk densities for different rock types: (i) accretionary complex formed between 40 and 20 million years ago (2.79 ± 0.05 g/cm3); (ii) marine and non‐marine sediments formed between 15 and 1.7 million years ago (2.57 ± 0.03 g/cm3); (iii) marine and non‐marine sediments formed between 1.7 and 0.7 million years ago (2.43 ± 0.05 g/cm3); and (iv) basaltic and andesitic rock formed between 7 and 1.7 million years ago (2.51 ± 0.05 g/cm3). These muographic values were consistent with the gravimetric values of 2.63 ± 0.09 g/cm3, 2.54 ± 0.15 g/cm3, and 2.58 ± 0.2 g/cm3, respectively, for Mesozoic, Tertiary, and Quaternary sedimentary rocks, and 2.53 ± 0.15 g/cm3 for Tertiary volcanic rock (Nawa et al., 1997).

      1.3.5 Underground Water

      Risks in landslips (smaller‐scale landslides, mudslides, debris slides, and flows), landslides (larger‐scale mass movements), and flood in tunnels (for example, the flood accident known as “The Great Spring” inside the Severn Tunnel in 1879) are all related to the existence and mobility of subterranean water. Therefore, monitoring the subsurface water content is a key factor to predict underground‐originated disasters. The subsurface water content has been monitored with water gauges and soil moisture sensors inserted into vertically and horizontally drilled boreholes (Yuliza et al., 2015), however, from only a few data points, it is generally difficult to extrapolate the actual movements of underground water that are affected by the complicated surrounding geology. Deployments of an array of multiple sensors in multiple boreholes are not practical, thus our current quantitative understanding is far from the level of constructing a satisfactory model for prediction. As a consequence, the current risk management is rather palliative, for example, concrete shotcrete to stabilize mechanically unstable slopes or drainage tunnels to lower the level of the underground water table (Tanaka & Sannomiya, 2012).