A Volterra series-based method for extracting target echoes in the seafloor mining environment.
The purpose of this research was to evaluate the applicability of the Volterra adaptive method to predict the target echo of an ultrasonic signal in an underwater seafloor mining environment. There is growing interest in mining of seafloor minerals because they offer an alternative source of rare metals. Mining the minerals cause the seafloor sediments to be stirred up and suspended in sea water. In such an environment, the target signals used for seafloor mapping are unable to be detected because of the unavoidable presence of volume reverberation induced by the suspended sediments. The detection of target signals in reverberation is currently performed using a stochastic model (for example, the autoregressive (AR) model) based on the statistical characterisation of reverberation. However, we examined a new method of signal detection in volume reverberation based on the Volterra series by confirming that the reverberation is a chaotic signal and generated by a deterministic process. The advantage of this method over the stochastic model is that attributions of the specific physical process are considered in the signal detection problem. To test the Volterra series based method and its applicability to target signal detection in the volume reverberation environment derived from the seafloor mining process, we simulated the real-life conditions of seafloor mining in a water filled tank of dimensions of 5×3×1.8m. The bottom of the tank was covered with 10cm of an irregular sand layer under which 5cm of an irregular cobalt-rich crusts layer was placed. The bottom was interrogated by an acoustic wave generated as 16μs pulses of 500kHz frequency. This frequency is demonstrated to ensure a resolution on the order of one centimetre, which is adequate in exploration practice. Echo signals were collected with a data acquisition card (PCI 1714 UL, 12-bit). Detection of the target echo in these signals was performed by both the Volterra series based model and the AR model. The results obtained confirm that the Volterra series based method is more efficient in the detection of the signal in reverberation than the conventional AR model (the accuracy is 80% for the PIM-Volterra prediction model versus 40% for the AR model).
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China; State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, China.
Haiming
H
Zhao
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China. Electronic address: saqlag_yaqian@sina.cn.
Yaqian
Y
Ji
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
Yujiu
Y
Hong
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
Qi
Q
Hao
School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China.
Liyong
L
Ma
Seafloor mining; Target echo detection; Underwater acoustic detection; Volterra series; Volume reverberation