Zhiqing Yang

Compact high-frequency surface wave radars (HFSWRs) utilize miniaturized antennas, resulting in lower antenna gain and a reduced signal-to-noise ratio (SNR) for target echoes. Due to noise interference, ship echoes in the noise region often fall below the detection threshold, leading to missed detections. To address this issue, this paper proposes a cascaded detection method in the time–frequency (TF) domain to improve ship detection performance under such conditions. First, TF features are extracted from TF representations of ship and noise signals. Supervised machine learning algorithms are then employed to distinguish targets from noise, reducing false alarms. Next, a non-constant false alarm rate (CFAR) threshold is computed based on the noise mean, standard deviation, and an adjustment factor to improve detection robustness. Experiments show that the classification accuracy between the ship and noise signals exceeds 99%, and the proposed method significantly outperforms the conventional CFAR and TF-domain CFAR in terms of detection performance.

Accurate shear wave velocity is important for shale reservoir exploration and characterization. However, the effect of the ubiquitous preferred orientation of clay minerals on the velocities of shale has rarely been considered in existing S-wave velocity estimation methods, resulting in limited accuracy of the estimation method. In this study, a S-wave velocity estimation method is proposed for shale while considering the effect of the preferred orientation of clay. First, a compaction model is built by taking the effects of the orientation distribution of clay and the aspect ratio of pores into account. Then, the compaction model is utilized in a workflow to obtain the model parameters by fitting the estimated P-wave velocity with the bedding-normal P-wave velocity from well logging. Finally, the S-wave velocity is estimated using the compaction model and calculated model parameters. The proposed method is verified by laboratory data and successfully applied to a shale gas reservoir. The result shows that the root mean square error almost halves compared with the Xu–White model. Additionally, the correlation coefficient also improves. The improvement in S-wave velocity estimation indicates that the effect of the preferred orientation of clay on the velocities of shale is effectively corrected. The proposed method improves the accuracy of velocity modeling and reservoir characterization for shale.

The traditional constant false alarm rate (CFAR) method, with fixed parameter settings and single noise background calculation, is unable to intelligently catch the current detection background. To improve the performance of the CFAR method, this paper proposes a target detection method based on decision tree classification (DTC) for high-frequency surface wave radar (HFSWR). Firstly, the training sample set and labels are obtained by means of a ship automatic identification system (AIS). Then, feature vector of range dimension, Doppler dimension and range-Doppler (RD) dimension is extracted by way of cell averaging, ordered statistics, censored mean and trimmed mean. Finally, DTC is used to recognize “true” and “false” targets in feature space. Experimental results show that, under the same number of detection targets, the DTC method is superior to traditional CFAR methods, and the accuracy of target detection can be increased by more than 5%.