Study on the Relationship between Resistivity and the Physical Properties of Seafloor Sediments Based on the Deep Neural Learning Algorithm

Abstract

The occurrence of deep-sea geohazards is accompanied by dynamic changes in the physical properties of seafloor sediments. Therefore, studying the physical properties is helpful for monitoring and early warnings of deep-sea geohazards. Existing physical property inversion methods have problems regarding the poor inversion accuracy and limited application scope. To address these issues, we establish a deep learning model between the resistivity of seafloor sediment and its density, water content, and porosity. Compared with empirical formulas, the deep learning model has the advantages of a more concentrated prediction range and a higher prediction accuracy. This algorithm was applied to invert the spatial distribution characteristics and temporal variation of the seafloor sediment density, water content, and porosity in the South China Sea hydrate test area for 12 days. The study reveals that the dynamic changes in the physical properties of seafloor sediments in the South China Sea hydrate zone exhibit obvious stratification characteristics. The dynamic changes in the physical properties of seafloor sediments are mainly observed at depths of 0–0.9 m below the seafloor, and the sediment properties remain stable at depths of 0.9–1.8 m below the seafloor. This study achieves the monitoring and early warning of dynamic changes in the physical properties of seafloor sediments and provides a guarantee for the safe construction of marine engineering.

Keywords

• sediment resistivity
• physical property
• deep learning
• the South China Sea