## 4 Results and Discussion  
### Validation Using Various Hydrogeological Indices  
To confirm whether the flow dimension \(n\) value can be used as a basis for interpreting the fracture network patterns, this study proposes five hydrogeological indices (i.e., RQD,  
Figure 2: The process of a double-packer constant head hydraulic test at the interval of 22.3–23.7 m in a specific borehole. FA, FD, hydraulic conductivity, and K\({}_{\rm f}\)/K\({}_{\rm m}\) ratio) to verify the rationality and credibility of the proposed method. The following verification results for five hydrogeological indices are described as follows. 1. RQD
2. RQD is mainly used to measure the degree of fracturing in strata. Theoretically, the lower the RQD, the more broken the rock mass, and the higher the fracture network density. Therefore, by examining a relationship strength between the flow dimension \(n\) and RQD, the feasibility of the n-value to interpret the fracture network density around a borehole was conducted. In this study, RQD is divided into four levels, which are RQD < 25%, 25-50%, 50-75%, and >75%, respectively, representing the degree of rock mass fragmentation from high to low. The flow dimension \(n\) value between 1 (flow geometry: linear flow) and 3 (flow geometry: spherical flow) is divided into five classes: 3.0-2.75 (spherical flow), 2.75-2.25 (spherical-cylinder flow), 2.25-1.75 (cylinder flow), 1.75-1.25 (cylinder-linear flow), and 1.25-1 (linear flow), which respectively represent five different levels of fracture network density, namely high, medium-high, medium, medium-low, and low. Further, the data points whose \(n\) values are in the medium-high level of fracture network density (\(n>2.25\)) were selected, and the RQD of each data point was examined to see if it shows a tendency to be smaller. The same analysis was performed for the data points with the highest level (\(n>2.75\)). Figures 3 and 4 show the corresponding RQD distribution results when the fracture network density belongs to the medium-high grade (\(n>2.25\)) and the high grade (\(n>2.75\)), respectively. Approximately eighty percent of the data points for each of the two fracture network classes are distributed among the grades with the smaller RQD classes in the levels of RQD < 25% and RQD = 25%-50% representing high fractured rock mass, while fewer data points are attributed in relatively intact rocks in the level of RQD > 75%. The overall result is in line with the expectation of this study. Regarding data points with relatively high RQD but appearing at the high n-value level, the main reason is that the RQD calculated from the drill core, which is a small scale, may be different from that computed from the adjacent region of the drill hole, which is a large scale. As a result, the connectivity of the fracture network around the borehole is not the same as that seen in the drilling core samples. Although a few results are not expected, most of the data show that RQD is highly correlated with n. Therefore, it is feasible to further explain the fracture network density around the borehole from the flow dimension \(n\) values. In addition, Figures 3 and 4 also show the amount of data occupied by sedimentary rocks and metamorphic rocks for each RQD class, so as to understand which lithological fracture network is relatively developed in the study area. The results show that about 95% of the metamorphic rocks are within the classes of RQD \(<25\%\) and RQD \(=25\%\)-\(50\%\), while the sedimentary rocks are relatively less in the range of 75% to 80%. From the above statistical results, it can be concluded that metamorphic rocks are more likely to have high  
Figure 4: Quantity distribution of different RQD levels for the samples of \(n>2.75\). Figure 3: Quantity distribution of different rock-quality designation (RQD) levels for the samples of \(n>2.25\).