Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological data (lithology, structure, lineaments), Landsat-8, Sentinel-2, and ASTER multispectral remote sensing data were processed to produce various predictor maps, which then formed four distinct datasets (namely Landsat-8, Sentinel-2, ASTER, and Data-integration). Remote sensing enhancement techniques, including band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF), were applied to produce predictor maps related to hydrothermal alteration zones in Hamissana area, while geological-based predictor maps were derived from applying spatial analysis methods. These four datasets were used independently to train a random forest algorithm (RF), which was then employed to conduct data-driven gold mineral prospectivity modeling (MPM) of the study area and compare the capability of different datasets. The modeling results revealed that ASTER and Sentinel-2 datasets achieved very similar accuracy and outperformed Landsat-8 dataset. Based on the area under the ROC curve (AUC), both datasets had the same prediction accuracy of 0.875. However, ASTER dataset yielded the highest overall classification accuracy of 73%, which is 6% higher than Sentinel-2 and 13% higher than Landsat-8. By using the data-integration concept, the prediction accuracy increased by about 6% (AUC: 0.938) compared with the ASTER dataset. Hence, these results suggest that the framework of exploiting remote sensing data is promising and should be used as an alternative technique for MPM in case of data availability issues.

This study compares the capability of Landsat-8, Sentinel-2, and ASTER multispectral data in mapping lithological units in the Bukadaban Peak, China, by evaluating the performance of the Random Forest (RF) classifier. Moreover, the study assesses the importance of remote sensing original bands and the bands derived from enhancement techniques such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) to the classification process. Results revealed that RF selected PC8, PC9, PC12, and MNF1 as the most important features in the Sentinel-2 dataset. Several MNF bands of ASTER were more important than the original and PC bands. The original bands were the strongest predictors in Landsat-8 dataset, while PC2, PC5, and MNF5 were relatively significant. Sentinal-2 and ASTER datasets achieved very similar classification accuracy but outperformed Landsat-8 dataset. ASTER dataset yielded the highest overall accuracy 81.8%, which is 0.18% higher than Sentinel-2 and 3.86% higher than Landsat-8.

Severe vortex-induced vibration (VIV) poses a major threat to deep-water riser integrity under complex ocean conditions. To address the active control of riser VIV, this paper introduces a bio-inspired flow-disturbing robot (FDR) design. A nonlinear dynamic model for deep-water risers is established via the variational principle and Kirchhoff's hypothesis, incorporating top tension, internal flow, added mass, and FDR coverage effects. The robot distribution function and experimentally fitted wake oscillator parameters enable efficient numerical solutions using the Newmark-β and Newton–Raphson methods. Model validation against experimental data shows good agreement at FDR coverage below 20%, with root mean square acceleration predictions tending toward conservative engineering estimates. Bayesian optimization derives optimal FDR deployment schemes. Results demonstrate that vibration suppression effectiveness varies significantly with flow velocity—higher velocities yield more pronounced suppression in both cross-flow (CF) and in-line directions. Optimal suppression occurs at the riser mid-section. FDR installation transforms riser nonlinear behavior from chaotic (bare pipe) to quasi-periodic motion with distinct limit cycles and attractors. At 10% coverage, the Lyapunov exponent of CF vibration reduces by up to 48.99% compared to the bare riser. These findings provide a theoretical foundation for engineering applications of riser vibration control using FDRs.