Search Results (2)

Full-duplex acoustic telemetry is important for real-time bidirectional measurement and control in intelligent coiled-tubing operations, but its reliability in deep horizontal wells is limited by long-range dispersion, asymmetric flow-induced noise, and severe near-end self-interference. This study proposes an orthogonal frequency-band planning and synergistic interference suppression method for full-duplex acoustic communication in coiled tubing. A dispersion model and an asymmetric attenuation model were first established for a fluid-filled coiled-tubing cylindrical-shell waveguide to characterize the physical transmission constraints. A multiphysics multi-objective cost function was then formulated by considering dispersion flatness, channel attenuation, asymmetric noise adaptability, and spectral isolation, and an improved simulated annealing algorithm was used to optimize the uplink and downlink frequency bands. In addition, a three-stage suppression architecture integrating mechanical decoupling, physical-layer frequency isolation, and CEEMDAN–wavelet denoising was developed to reduce self-interference and residual nonstationary noise. Full-scale experiments on a 457.2 m coiled-tubing surface circulation system showed that the proposed method improved the output signal-to-interference-plus-noise ratio from −15 dB to 18.5 dB and maintained a bit error rate below 1.2 × 10⁻⁴ at 400 L/min. These results indicate that the proposed approach can enhance the robustness of full-duplex acoustic telemetry under strong flow-induced noise. Full article

High-precision extraction of mangrove areas is a crucial prerequisite for estimating mangrove area as well as for regional planning and ecological protection. However, mangroves typically grow in coastal and near-shore areas with complex water colors, where traditional mangrove extraction algorithms face challenges such as unclear region segmentation and insufficient accuracy. To address this issue, in this paper we propose a new algorithm for mangrove identification and extraction based on Orthogonal Matching Filter–Weighted Least Squares (OMF-WLS) target spectral information. This method first selects GF-6 remote sensing images with less cloud cover, then enhances mangrove feature information through preprocessing and band extension, combining whitened orthogonal subspace projection with the whitened matching filter algorithm. Notably, this paper innovatively introduces Weighted Least Squares (WLS) filtering technology. WLS filtering precisely processes high-frequency noise and edge details in images using an adaptive weighting matrix, significantly improving the edge clarity and overall quality of mangrove images. This innovative approach overcomes the bottleneck of traditional methods in effectively extracting edge information against complex water color backgrounds. Finally, Otsu’s method is used for adaptive threshold segmentation of GF-6 remote sensing images to achieve target extraction of mangrove areas. Our experimental results show that OMF-WLS improves extraction accuracy compared to traditional methods, with overall precision increasing from 0.95702 to 0.99366 and the Kappa coefficient rising from 0.88436 to 0.98233. In addition, our proposed method provides significant improvements in other metrics, demonstrating better overall performance. These findings can provide more reliable technical support for the monitoring and protection of mangrove resources. Full article