Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.

Urban road collapses are characterized by sudden occurrence and strong spatial heterogeneity, posing substantial challenges for proactive infrastructure management. Susceptibility mapping can provide spatially explicit evidence to support targeted inspection and early-warning strategies. Using Futian District, Shenzhen (China) as a case study, a total of 315 road collapse events recorded during 2019–2023 were compiled to develop an integrated framework for urban road collapse relative susceptibility mapping based on multi-source remote sensing and urban spatial data. First, an indicator-based susceptibility index (SI) was constructed using eight conditioning factors, including PS-InSAR-derived deformation, topographic–hydrological conditions, and distance-based infrastructure variables (distance to underground utilities, metro lines, and roads). Factor weights were determined by coupling the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM), producing a comprehensive SI for historical collapse locations. Subsequently, a set of 17 remote-sensing predictors, including Sentinel-2 spectral bands, Sentinel-2 GLCM texture features, and Sentinel-1 SAR backscatter variables, was used to train a Random Forest model to predict SI and generate continuous susceptibility maps at the urban road-network scale. The influence of neighborhood window size on predictive performance was systematically evaluated. Results show that the Random Forest model performed best at the 5 × 5 window scale (R² = 0.70, RMSE = 0.0172, MAE = 0.0122), outperforming both pixel-based inputs (1 × 1) and larger windows. Uncertainty analysis further indicated that the 5 × 5 RF configuration yielded the most stable and spatially coherent predictions, whereas overly small windows and less robust learners produced more fragmented or higher-uncertainty susceptibility patterns. Spatiotemporal analysis indicates that susceptibility patterns remained broadly stable from 2019 to 2023, with moderate susceptibility accounting for 50.82–57.89% and high susceptibility for 21.94–23.30%, while very high susceptibility consistently remained below 1%. Overall, this study demonstrates that integrating multi-source remote sensing with scale-optimized machine learning provides an effective approach for fine-scale susceptibility mapping of urban road collapses, offering practical guidance for differentiated monitoring and risk prevention along critical road corridors.

The accuracy of seismic horizon identification results significantly impacts the precision of subsequent structural interpretation and reservoir analysis, making it a critical step in seismic data interpretation. While deep learning achieves horizon identification by establishing a mapping relationship between seismic data and training labels, networks often struggle to learn effectively when faced with highly similar profile features within the same survey area, leading to issues such as discontinuities and horizon mis-ties. To fully leverage the spatial relationships between seismic horizons and enhance network feature extraction capabilities, this paper proposes a progressive methodology based on the VGG16-UNet architecture. This paper first employs data augmentation to enrich seismic profile features and then introduces the Spatial and Channel Squeeze and Excitation (scSE) attention mechanism to improve the representation of salient features, designing the scSE-VGG16-UNet architecture. Building on this foundation, the deep supervision mechanism from UNet++ is integrated to finally propose the data-augmented scSE-VGG16-UNet++ method for seismic horizon identification. Quantitatively, the baseline VGG16-UNet method achieves a Pixel Accuracy (PA) and mean Intersection over Union (mIoU) of only 93.01% and 46.96%, respectively. Through the synergistic effect of data augmentation, the scSE attention mechanism, and the UNet++ architecture, the final data-augmented scSE-VGG16-UNet++ method elevates these metrics to 97.70% and 79.23%. The test results on both model data and field data demonstrate that the proposed method not only achieves high-continuity horizon identification and effectively mitigates the problem of horizon mis-ties, but also exhibits robust performance in the presence of noise, indicating superior generalization ability.