Seafloor Classification by Fusing AUV Acoustic and Magnetic Data: Toward Complex Deep-Sea Environments

Mid-ocean ridges, seamounts, and back-arc basins are focal points for deep-sea research due to their abundant mineral and biological resources. Unlike the sediment-covered deep-sea plains, the above areas have complex topography due to frequent geological activity, which has greatly constrained the accuracy of seafloor classification using shipborne underwater acoustic remote sensing. With the development of autonomous underwater vehicles (AUVs), near range measurements become feasible. Here, we present a method for fusing acoustic and magnetic AUV data on a meter scale, thereby providing evidence for improved seafloor classification. First, a space scale normalized model was built for obtaining high spatial resolution seafloor magnetic anomalies. In the second step, nine features were extracted from the acoustic backscatter mosaic, the bathymetry, and the seafloor magnetic anomaly. Finally, deep neural network (DNN) models were built for training, testing, and classification. To evaluate the classification performance of the model, the method was applied to the survey of the Chinese contract area for polymetallic sulfide exploration on the Southwest Indian Ridge (SWIR) and validated by field data (seafloor video). The integration of acoustic and magnetic data, as opposed to using single acoustic data, improved the overall classification accuracy and kappa coefficient of DNN for basalts, breccias, and sediment by 6.4% and 0.096%, respectively. The experimental results show that the method can fully mine the acoustic and magnetic properties of the seafloor, effectively respond to the challenges of seafloor classification presented by the deep-sea complex environment, and provide a fresh idea for the research of near-bottom high-precision seafloor classification methods.