Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data

Abstract

This article presents a summary of three common false-bottom occurrences in echosounder imaging based on an analysis of echosounder data. Utilizing the imaging principle of the echosounder, a comprehensive analysis was conducted and an explanation of each situation’s causes, imaging characteristics, impacts, and solutions is presented. Additionally, the article includes calculations to determine the precise location of the false bottom, which were subsequently validated through actual data collection. To address the two most impactful false-bottom scenarios in target detection, solutions are proposed from two perspectives. By accurately judging the position and imaging characteristics of these false bottoms, the article concludes with an analysis of the causes of false bottoms and presents corresponding solutions. The article aims to facilitate the rapid identification and elimination of false bottoms, thus mitigating their adverse effects on target detection.

Estimation of Artificial Reef Pose Based on Deep Learning

“…Targets with different densities, such as gas/air or rock/concrete, exhibit distinct reflection characteristics, resulting in echo signals of varying intensities. For instance, materials like concrete produce strong echoes, while materials like mud, silt, sand, and vegetation produce weaker echo signals [21]. Therefore, the sonar system's acoustic "illumination" range for targets made of concrete is limited, only displaying targets within the sonar beam, as shown in Figure 1a.…”