A novel error compensation framework for MEMS sensor array applied to seabed terrain deformation monitoring

Micro-Electro-Mechanical System (MEMS) sensor array plays a significant role in in-situ and long-term monitoring of seabed terrain deformation. However, multi-source errors in the monitoring process are difficult to avoid, such as natural disaster, communication issues, and calculation models. A novel error compensation framework based on MEMS sensor array is presented and validated in this paper. An elaborate laboratory test is carried out and two datasets with an interval of 5mm between 0-150mm are collected using two MEMS sensor arrays. The Correlation Coefficient (CC) is introduced and applied for constructing optimal model input features. Window size, sliding step, differential coefficient, differential step, lag length is determined by CC analysis and 12 model input features are identified. Seven models are comprehensively compared on two datasets, including 1D-CNN-BiLSTM, BiLSTM, 1D-CNN, GRU, LSTM, LR, and SVR. The proposed hybrid 1D-CNN-BiLSTM can achieve the lowest average RMSE (2.9677mm; 2.3319mm) and the highest average R2 (0.9269; 0.9356) on two datasets with K-fold cross validation. And it outperforms other models in the compensation experiment, with RMSE reduction of up to 34.46%, 31.00%, respectively. The results demonstrate that the proposed error compensation framework can effectively compensate deformation monitoring errors, and provide practical guidelines for seabed terrain monitoring and deformation error compensation.