# Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data
## 5 Conclusions  
Echoosounders are important tools for fishery resource assessment, with a wide range of applications in fisheries [35, 36, 37, 38]. Understanding the working principles of echosounders is crucial for effectively addressing any problems that are encountered. False bottoms are a common issue when using echosounders for target detection, making the study of false bottoms essential for improving detection accuracy. This study encompasses a large dataset of echosounder data to identify and analyze three common false-bottom situations. It provides a detailed analysis and explanation of the causes, imaging characteristics, impacts, and solutions for each situation. The study also calculates the locations of false bottoms. Moreover, a plan is proposed to prevent the occurrence of false bottoms based on the principle of false-bottom formation and the characteristics of echosounder propagation, which enhances the reliability of echosounder detection. We compared our research with the existing research [16,17,39,40]. Our study is unique in starting from the principle of false-bottom formation, categorizing common false bottomsinto three situations, and analyzing echosounder false bottoms from multiple dimensions, such as the causes, characteristics, and impacts. This study avoids making generalizations about false bottoms by providing a clear and accurate classification. The differentiation of false-bottom types allows for corresponding prevention and elimination plans to be established for each type, surpassing the limitations of traditional research. Nonetheless, research on false-bottom imaging algorithms still lags behind other research [41, 42, 43, 44]. The article's limitations include the fact that the attempts to prevent false bottoms are limited to the collection stage, and an effective solution for processing offline echosounder data has not been presented. Only the true-bottom depth obtained from the offline echosounder data is currently used to determine the position of false bottoms and assist in their identification. Thus, there is a need for an efficient and reliable method to remove false bottoms. If an algorithm can accurately and effectively remove false bottoms, echosounders could be better applied in scientific research [45], fisheries, and other fields. However, further exploration and research are required for the development of this algorithm. X.X. and Y.D. ; methodology, X.X. ; software, X.X. ; validation, X.X., Y.D. and R.H.; formal analysis, Z.W. ([PERSON]) and Y.W. ; investigation in, Z.W. ([PERSON]); resources, X.X. and S.Y. ; data curation, X.X. ; writing--original draft preparation, X.X. and R.H.; writing--review and editing, X.X. ; visualization, Y.D. ; supervision, Y.D. and W.F. ; project administration, Y.D. and W.F. ; funding acquisition, Y.D. and W.F. All authors have read and agreed to the published version of the manuscript. This research was financially supported by the National Key Research and Development Project of China, grant No. 2023 YFD2401202, and the Laoshan Laboratory, grant No. LSKJ202201801. The following software used in the experiment can be downloaded at: Welcome to Python.org (accessed on 10 June 2023), Software: python v3.10 [46]. The following software used in the experiment can be downloaded at: OSOceanAcoustics/echopype: Enabling interoperability and scalability in ocean sonar data analysis (github.com) (accessed on 10 June 2023), Software: Echopype v0.7.1 [47], part data be downloaded at: [[https://drive.google.com/drive/folders/19/hn62d_Or8](https://drive.google.com/drive/folders/19/hn62d_Or8) MC08_iOseG2 PxV6 LDoD2?usp=sharing]([https://drive.google.com/drive/folders/19/hn62d_Or8](https://drive.google.com/drive/folders/19/hn62d_Or8) MC08_iOseG2 PxV6 LDoD2?usp=sharing) (accessed on 10 February 2024). The data collection for this study was supported by the research vessel \"Deep Blue\". The authors declare no conflicts of interest.