• Korean Journal of Heritage: History & Science
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• v.56 no.3
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• pp.174-193
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• 2023

Korea is surrounded by the sea and has rivers connecting to it throughout the inland areas, which has been a geographical characteristic since ancient times. As a result, there have been exchanges and conflicts with various countries through the sea, and rivers have facilitated the transportation of ships carrying grain, goods paid for by taxes, and passengers. Since the past, the sea and rivers have had a significant impact on the lives of Koreans. Consequently, it is expected that there are many cultural heritages submerged in the sea and rivers, and continuous efforts are being made to discover and preserve them. Underwater cultural heritage is difficult to discover due to its location in the sea or rivers, making direct visual observation and exploration challenging. To overcome these limitations, various geophysical survey techniques are employed. Geophysical survey methods utilize the physical properties of elastic waves, including their reflection and refraction, to conduct surveys such as bathymetry, underwater topography and strata. These techniques detect the physical characteristics of underwater objects and seafloor formation in the underwater environment, analyze differences, and identify underwater cultural heritage located on or buried in the seabed. Bathymetry uses an echo sounder, and an underwater topography survey uses a side-scan sonar to find underwater artifacts lying on or partially exposed to the seabed, and a marine shallow strata survey uses a sub-bottom profiler to find underwater heritages buried in the seabed. However, the underwater cultural heritage discovered in domestic waters thus far has largely been accidental findings by fishermen, divers, or octopus hunters. This study aims to analyze and summarize the latest research trends in equipment used for underwater cultural heritage exploration, including bathymetric surveys, underwater topography surveys and strata surveys. The goal is to contribute to research on underwater cultural heritage investigation in the domestic context.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.