Abstract
Background: The seafloor is an essential ocean boundary, and the detection of seafloor information is necessary basis for seafloor scientific research. The classification and identification of seafloor geological types is necessary for researchers to conduct seafloor research, military activities, and marine platform construction.
Objective: The purpose of this paper is to summarize the progress of seafloor substrate classification research based on backscattering and to seek a new development direction for seafloor substrate classification research.
Method: The literature on various types of submarine sediment attenuation geoacoustic models, backscatter intensity calculations, and submarine substrate classification is summarized, and the progress of theoretical research required for the positive and negative problems of submarine substrate classification is described that include the geoacoustic parameter models based on fluid theory, elastomer theory and poroelastic theory and submarine acoustic scattering models, including the small roughness perturbation approximation model, the Kirchhoff approximation model, the Kirchhoff approximation model and the Kirchhoff approximation model.
Result: The development of the Kirchhoff approximation model, the slight slope approximation model, the volume scattering model, and the inversion methods for seafloor substrate classification are summarized, and breakthroughs in seafloor substrate classification are sought by summarizing previous studies.
Conclusion: The classification of seafloor substrate based on backscattering intensity needs the support of a perfect geoacoustic model and scattering model, and the current research of low and medium-frequency scattering models and multi-layer seafloor scattering models are the further development direction in the future. Currently, the better performance of the prediction model, geo-acoustic parameter inversion results are more than 90% accuracy, sound velocity ratio and other parameters in the high-frequency band inversion accuracy of 98%, are able to better meet the measured data. Finally, some patented technologies are also reported.