**4. Discussion**

#### *4.1. Determination of the Sample Size*

Sample size is an important factor in accurately recognizing the bottom data samples and further realizing bottom tracking of the side scan data. If the sample size is too large, then the samples cannot represent the special variation characteristics of the bottom data sequences. However, if the sample size is too small, then the samples can be easily affected by local noise. For a better comparison, bottom tracking experiments were conducted with sample sizes of 10, 20, 40 (chosen in this paper), and 100, as shown in Table 3.

**Table 3.** Comparison of results obtained under different sample sizes after a 50-epoch training with 242 positive samples and 2662 negative samples.


As shown in Table 3, when the sample size was as small as 10, although the training and validation accuracies were high enough, the bottom tracking accuracy was 0%, which suggests that the variation characteristics of the samples can be easily affected by noise. When the sample size was as large as 20, the training and validation accuracies were improved, and the bottom tracking accuracy reached as high as 98.3%. When the sample size was 40 (as used in this paper), the training and validation accuracies were further improved, and the bottom tracking accuracy increased to 100%, which suggests that the samples can accurately reflect the variation characteristics of backscatter strengths. However, when the sample size was 100, although the training and validation accuracies were 100%, the bottom tracking accuracy was only 46.3%, which suggests that the samples cannot properly reflect the variation characteristics of bottom backscatter strengths. The comparison results reveal that the proper sample size of the window should be 40 (as used in this paper) for the side scan sonar.
