In the conditions of magnetic dipole model, this paper proposed forward a centroid localization algorithm on magnetic anomaly target based on wireless sensor network node which distribution are random and the improved the weighted centroid localization algorithm based on magnetic induction intensity. According to the fluctuation of magnetic field intensity which detected by magnetic sensors, that can detect the existence of magnetic anomaly target and its location. Established an experimental system of the wireless sensor network for magnetic anomaly detection whose core designs including the HMC1043 three-axis magnetic resistance sensor and the CC2530 Zigbee RF chip. The experimental results show that the algorithm can accurately positioning the magnetic anomaly target within the network.

The quantitative evaluations of mineral resources and delineation of promising areas in survey regions for future mining have attracted many researchers’ interest. Cobalt-Rich manganese crusts (Mn-crusts), as one of the three significant strategic submarine mineral resources, lack effective and low-cost detection devices for surveying since the challenging distribution requires a high vertical and horizontal resolution. To solve this problem, we have built an engineering prototype parametric acoustic probe named PPPAAP19. With the echo data acquired by the probe, the interpretation of the accurate thickness information and the seabed classification using the deep learning network-based method are realized. We introduce the acoustic dataset of the minerals collected from two sea trials. Firstly, the preprocessing method and data augment strategy used to form the dataset are described. Afterward, the performances of several baseline approaches are assessed on the dataset, and the experimental results show that they all achieve high accuracy for binary classification. We find that the end-to-end approach for binary classification based on a 1D Convolution Neural Network has a comprehensive advantage. Such a demonstration validates the possibility of binary classification for recognizing the ferromanganese crust only in an acoustic manner, which may significantly contribute to the efficiency of the survey.

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