## 4 Experiment and Analysis  
### Data Collection  
Data for re-training the DNN model comprised the surface strengths and hammering sounds. In this experiment, two batches of data, Batch A and Batch B, were collected, and the rocks in Batch B were different from the rocks in Batch A. Batch A was used for re-training and conducting the preliminary tests. Batch B was used for the final test of the method.  
The surface strengths of rocks were measured by an N-type rebound device. An N-type rebound device can measure the rebound values by hitting a rock. As mentioned in Section 2.1, current research  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Strength** & 10 & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline
**Probability** & 0.0002 & 0.0000 & 0.0098 & 0.0005 & 0.0000 & 0.8806 & 0.1017 & 0.0004 & 0.0006 & 0.0051 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction result of a spectrogram.  
has demonstrated that there is a strong link between the rebound value and the UCS. Therefore, in this study, the surface strengths of the rocks were represented by their rebound values.  
The objects used in the experiment were rocks that existed in the natural environment. Every rock was intact and at least 0.05 m\({}^{3}\) in volume. Rocks were not limited to a particular type. It was not possible to measure the strength of the whole rock mass due to the rock anisotropy, and what was measured was just the strength of one point on the rock. To avoid damage to the surface of the rock caused by measuring one point too many times (especially when the rock was weak), three measuring points close to each other were set on one rock. Each point was hit five times by the rebound hammer. In total, there were 15 rebound values for each rock. Then, the surface strength of a rock could be calculated by filtering the values with the three sigma rule and calculating the mean of the remaining rebound values. Moreover, before measuring, the weathered layers of the rocks were removed. Table 2 shows the measurements of 15 rock samples A1-A10 (strengths of Batch A) and B1-B5 (strengths of Batch B). In addition, the rocks included granites, basalts, killas, and andesites. However, the types and mineral compositions of rocks were not regarded as the influential factors.  
The reason why we divided the whole dataset into two batches was that an algorithm (even DNN) may be sensitive to the data that similar to the training data. The rocks in Batch B were different from the rocks in Batch A, and the strengths of Batch-B rocks were also different from the Batch-A rocks. A part of Batch A was for the primary test of our method, and the whole data of Batch B was for the further verification of the generalization of the method.  
After measuring the strengths, each rock was hammered 200-260 times with a geological hammer around the three measuring points. A voice recorder was used to record the hammering sounds. The hammering rate was approximately one-two times per second, and the hammering force was slightly varied every 20-30 times to ensure the variability of the hammering force. The voice recorder had two channels, and its sampling frequency was 24 kHz.  
It should be noted that in fact the hammering force mainly affects the amplitude of the hammering sound (sound level). Therefore, we did not hammer the rocks too hard, but put the voice recorder very near the hammering points to obtain clear sounds, and in this way prevented (or reduced) damage of the rock surface. Despite that, some rocks were still damaged in our experiment, and in these cases, new hammering points near the old ones were selected to continue the experiment. However, for those rocks of which nearby areas of the measuring points were all damaged, the measuring processes were terminated immediately.