Yingjie Liu

Natural gas hydrates are regarded as a vital strategic energy resource for the future owing to their high energy density and clean combustion characteristics. To facilitate research into the physical and mechanical properties of pressure-maintained hydrate samples, this paper presents an integrated multi-parameter online analysis system capable of rapidly measuring the P-wave velocity, electrical resistivity, thermal conductivity, and shear strength of core samples under pressure-maintaining conditions. The system comprises hardware acquisition boards based on ZYNQ and ARM platforms, specialized measurement probes, and comprehensive data acquisition and analysis software. To mitigate the susceptibility of P-wave signals to noise interference, an improved denoising algorithm combining Empirical Mode Decomposition (EMD) and wavelet thresholding is proposed. By employing autocorrelation function analysis, the algorithm identifies the transition boundary between noise-dominated and signal-dominated Intrinsic Mode Functions (IMFs), subsequently applying wavelet soft-thresholding to the noise-dominant components. Experimental results demonstrate that the proposed algorithm achieves a superior signal-to-noise ratio (SNR) compared to traditional EMD methods, particularly under low SNR conditions. System validation indicates measurement accuracies of 3.2% for P-wave velocity at 20 °C, 1.76% for electrical resistivity at 25 °C, and within 7% for both thermal conductivity and shear strength. Furthermore, sea trials conducted aboard the “HAIYANG SHIYOU 708” drilling vessel confirm that the system operates stably and effectively fulfills the requirements for deep-sea core parameter characterization.

This paper proposes a robust navigation method based on a robust square-root cubature Kalman filter (RSRCKF) to address the accuracy divergence of integrated navigation systems caused by drilling-induced slippage and the mismatch between the tail-cable encoder and the robot motion during operations of a seafloor drilling robot in deep-sea soft sedimentary layers. Considering the large-deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model incorporating a time-varying odometer scale-factor error is first established. To alleviate the numerical instability of the nonlinear system in the presence of non-Gaussian noise, a square-root cubature Kalman filter (SRCKF) framework is employed, in which the positive definiteness of the error covariance matrix is dynamically preserved via QR decomposition. Subsequently, an online fault detection mechanism based on a modified chi-square test is developed. By introducing a two-segment IGG (a classical robust weighting scheme) weighting function, an adaptive variance inflation factor is constructed to enable real-time identification and down-weighting of abnormal observations induced by slippage. Field experiments, including drilling and turning tests conducted on tidal mudflats off the coast of Zhoushan, demonstrate that the proposed method can effectively mitigate the impact of “false displacement” disturbances caused by typical soft clay slippage conditions through enhanced statistical robustness. Taking the conventional SINS/OD integration scheme as the baseline, the proposed method achieves an approximate 82.4% reduction in positioning error. These results verify the robustness and engineering applicability of the proposed algorithm in complex seabed environments.

To address the accuracy divergence problem of the integrated navigation system caused by drilling slippage and mismatch between the tail cable encoder and the robot's motion when a seafloor drilling robot operates in deep-sea soft sedimentary layers, this paper proposes a robust navigation method based on robust square root ductile Kalman filter (RSRCKF). Considering the large deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model including the time-varying odometer scaling factor error is first established. To solve the numerical instability of the nonlinear system under non-Gaussian noise interference, the square root ductile Kalman filter (SRCKF) framework is introduced, and the positive definiteness of the error covariance matrix is dynamically maintained using QR decomposition. Based on this, an online fault detection mechanism based on the novel chi-square test is designed, and an adaptive variance expansion factor is constructed by combining a two-segment IGG weight function to realize the real-time identification and weight reduction processing of abnormal observations caused by slippage. Field drilling and turning tests on the mudflats off the coast of Zhoushan show that, under typical soft clay slippage conditions, this method can effectively identify "false displacement" interference. Compared with the traditional EKF and standard SRCKF, the position error is reduced by approximately 82.4%, and the heading angle error is controlled within±0.5∘Within a certain range, the high robustness and engineering practicality of the algorithm under complex seabed topography were verified.