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A muographic image is represented as a function of elevation and azimuth angles (θ, ϕ). These angles of an incident particle can be computed as follows by connecting two points in a space S(x, y, z) with a straight line:

(1.3)

(1.4)

Figure 1.2 Principle of the muon tracking. The top view (a) and the side view (b) of the detectors are shown.

where L is the distance between the upstream (pointed towards the target object) and downstream detectors (pointed away from the target object). The subscripts of x and y indicate the first and second points (Fig. 1.2). The positioning resolution of the points (Δx, Δy) therefore gives the detector’s angular resolution (Δθ, Δϕ).

The maximum definition of the muographic image, R, will then be Φ/Δϕ × Θ/Δθ pixels, where Φ and Θ are, respectively, the horizontal and vertical viewing angles of the detector. Bin sizes (pixel size) of the image can be optimized to attain the sufficient statistics for muon counts recorded in each bin. Since the power to resolve the target volume depends on this pixel size and the distance between the target and detector, the measurement time required for attaining a given level of statistics for counts of muons arriving from a given section of the target volume is inversely proportional to the square of the distance between the target and detector. In muographic measurements, it is therefore ideal to get the detector as close to the target as possible. However, in most cases, accessibility and infrastructure availability (e.g., electricity) at geological sites limit the locations for measurements. Tanaka (2016) proposed airborne muography (placing the detector inside a helicopter) to practically remove these restrictions (Fig. 1.3). Tanaka (2013) and Kusagaya (2017) proposed another technique to observe dynamics within a shorter timescale than the time resolution of the observation system by integrating time‐sequential muographic images for the repetitionary processes.