In this paper, a symplectic method for structure-preserving modelling of the damped acoustic wave equation is introduced. The equation is traditionally solved using non-symplectic schemes. However, these schemes corrupt some intrinsic properties of the equation such as the conservation of both precision and the damping property in long-term calculations. In the method presented, an explicit second-order symplectic scheme is used for the time discretization, whereas physical space is discretized by the discrete singular convolution differentiator. The performance of the proposed scheme has been tested and verified using numerical simulations of the attenuating scalar seismic-wave equation. Scalar seismic wave-field modelling experiments on a heterogeneous medium with both damping and high-parameter contrasts demonstrate the superior performance of the approach presented for suppression of numerical dispersion. Long-term computational experiments display the remarkable capability of the approach presented for long-time simulations of damped acoustic wave equations. Promising numerical results suggest that the approach is suitable for high-precision and long-time numerical simulations of wave equations with damping terms, as it has a structure-preserving property for the damping term.

The manual identification of non-metallic pipelines in ground-penetrating radar (GPR) images is inefficient and heavily experience-dependent. Existing deep-learning methods suffer from performance degradation caused by image noise, signal attenuation, and false anomalies. To address these challenges, this paper proposes YOLOv8-WT, which introduces a novel WTConv-ATT module combining wavelet transform and multi-dimensional dynamic attention. This module performs multi-level wavelet decomposition to extract frequency-domain features and enhance global and low-frequency information perception; meanwhile spatial-channel-pixel attention adaptively generates feature fusion weights to suppress noise interference. Several existing well-established modules (Wise-IoU, C2f-FSDA, CBAM, EMA) are also integrated to further boost detection performance. Experimental results on merged public GPR datasets show that compared with the YOLOv8s baseline, the proposed model achieves increases of 1.27, 4.59, and 2.55 percentage points in Precision, Recall, and mAP50, reaching 92.98%, 91.83%, and 96.76%, respectively. YOLOv8-WT obtains promising detection performance for non-metallic pipelines under mixed-dataset conditions. Further validation is still required for unknown real-world GPR scenarios.

Understanding the crack development and rainfall infiltration in loess under wetting–drying cycles is crucial for assessing slope stability and promoting sustainable land management in ecologically vulnerable regions. This study employed a three-dimensional column model (Φ₂₄ × 50 cm) with 64 buried electrodes to simulate short-term heavy rainfall by changing the light duration (10 h/d and 5 h/d) and using 100 mm rainfall water. Results indicate that dry–wet cycles cause cumulative damage, significantly altering soil infiltration properties. After four cycles, the rainfall infiltration recharge coefficient increased from an initial 0.44% to 45.58%, a more than 100-fold rise. Resistivity imaging revealed a shift in water transport mode: from uniform matrix flow initially to preferential flow dominated by crack networks as cracks developed. During drying, crack zones exhibited high resistivity (ρ > 150 Ω·m), while water-filled cracks during infiltration showed low resistivity (ρ < 50 Ω·m). Resistivity is an excellent comprehensive index to quantify multi-field coupling damage, and its change (ρ∝ 1/w^1.86 × 1/(1 + 0.032 width)) synchronously responds to water content, crack development and dry–wet process. Low water content (w < 15%) and medium crack width (4–6 mm) are the most sensitive states. Longer illumination (10 h/d) promoted greater crack development and higher infiltration capacity compared to shorter cycles (5 h/d). The developed resistivity–moisture relationship provides a non-destructive monitoring tool for slope moisture dynamics, supporting not only geotechnical stability assessment but also optimized irrigation scheduling and adaptive land-use planning. These insights contribute directly to the sustainable management of soil and water resources in loess landscapes, aligning with sustainability goals in fragile ecosystems.