## 2 Materials and Methods
### Dynamic Image Analysis  
Image analysis methods can be divided into two main groups, namely static (2D) and dynamic (3D). Dynamic methods are further divided into real 3D and quasi-3D. Dynamic image analysis methods are described in ISO 13322-2:2021 \"Particle Size Analysis--Image Analysis Methods--Part 2: Dynamic Image Analysis Methods\" [35]. Recently, as dynamic image analysis has been shown to be useful and convenient for particle analysis, it was adopted in numerous studies, such as [PERSON] et al. [36], [PERSON] et al. [37], [PERSON] and [PERSON] [38], etc. Visual techniques for morphological analysis are extensively discussed in [PERSON] et al. [4]. We used a Microtrac Camsizer XL (the operating scheme is presented in Figure 4), which is a quasi-3D dynamic image analyser used for particle characterization. The quasi-3D analysis results in 3D data obtained from a series of 2D images taken from different angles of an analysed object. Input material (particles) can range from 160 \(\upmu\)m to 135 mm. As the material enters the machine, in its measuring area, the high-speed camera captures (~150 frames per second) the particle from different angles during its tumbling. We repeated the analysis two to three times, depending on the breakage tendency of the analysed material. If it seemed that the material could break during the impact after free fall, the measurements were stopped after two repetitions. For single-clast analysis, we aligned the particles' a and b axes, and a transversal between a and b axes to the measurement feed direction, in an attempt to capture each particle from as many angles as possible. When analysing particle mixtures, the particles were not aligned in any specific orientation.  
Figure 4: Dynamic image analyser—Microtrac Camsizer XL operating scheme.  
Figure 3: Los Angeles machine.  
The software accompanying Microtrac Camsizer XL, PartAn 3D, calculates 33 3D size and shape parameters based on a sequence of images. We selected three parameters to trace the morphological changes of clasts, addressing shape and texture. The parameter \"sphericity\" (_SPH_) was selected to describe the particles' form. It presents particles' proximity to a perfect sphere, with values between 0 and 1 (a perfect sphere has a sphericity value of 1). It is calculated as the ratio between the area equivalent diameter (\(D_{a}\)--calculated according to area \(A\)) and the equivalent perimeter diameter (\(D_{p}\)--calculated according to perimeter _PERIM_) (Equation (1)). The parameter \"angularity\" (_ANG_) was selected to describe the roundness/angularity aspect of the particles' shapes. The parameter \"angularity\" was calculated according to Equation (2). Particles' outlines were described by a polygon with \(n\) sides. The angle at each vortex was measured and the changes in angles were grouped into intervals of 10 degrees, with the value \(e\) being the starting value of each interval. \(P(e)\) is the probability that the change in angle has a value between \(e\) and \(e+10\). More angular particles had higher values (up to 180) and those that were more rounded had lower values (up to 0) of the \"angularity\" parameter. The calculation of _ANG_ value is described in detail in [PERSON] et al. [39] and [PERSON] et al. [3]. The parameter \"convexity\" (_CONV_) was selected to describe the surface roughness or surface texture of particles. It was calculated as a ratio between the convex hull perimeter (_PERIMCHULL_) and the perimeter of the particle (_PERIM_) (Equation (3)). Convexity values are between 0 and 1, where 1 represents a perfectly smooth particle (a particle with an aligned perimeter and convex hull perimeter).  
\[SPH=\frac{D_{a}}{D_{p}}=\frac{4\pi A}{PERIM^{2}} \tag{1}\]  
\[ANG=\frac{\sum_{e=0}^{e=170}eP(e)}{n} \tag{2}\]  
\[CONV=\frac{PERIMCHULL}{PERIM} \tag{3}\]