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In large-scale debris flow susceptibility assessments, there is often excessive manual intervention, low efficiency, and inadequate model accuracy. To address these issues, this paper integrates multiple data sources and proposes a Multi-channel and Multi-scale Residual Network (MMRNet) for automatic extraction of gully features. Firstly, MMRNet employs a multi-scale feature fusion module to capture both local and global features of gullies, enhancing the model’s feature representation capabilities. It then uses an improved residual structure to fuse shallow features, compress features, and improve assessment efficiency. Additionally, channel rearrangement techniques are used to enhance feature flow. Finally, susceptibility prediction is made based on the similarity between the gully under evaluation and gullies where debris flows have occurred. The natural breakpoint method is used to classify susceptibility results into five levels. Experimental results show that the very high susceptibility zones for debris flows are mainly concentrated in areas with abundant river systems along the Nujiang River, covering 61.68% of the entire study area, with a debris flow proportion of 98.78%. The MMRNet model achieves an accuracy (ACC) of 81.6% and an area under the curve (AUC) of 0.8320, indicating that this model is a high-performance method for debris flow susceptibility assessment.

Abstract Multistage plate subduction plays a crucial role in magmatism; however, the mechanisms by which deep geodynamic processes govern volcanism in the Alaska subduction zone remain controversial. Using numerous travel-time data from several seismic arrays, we constructed high-resolution tomographic models to investigate the velocity structure of the Pacific Plate and Yakutat slab. Our tomographic results revealed high-velocity anomalies in the Pacific Plate and Yakutat slab, while the low-velocity areas within the Pacific Plate were identified as slab tears. We suggest that the Pacific Plate transitioned from oblique subduction along the Aleutian volcano chain to lower-angle subduction beneath the Pacific-Yakutat Plate interaction zone, forming two slab tears that enhance hot asthenosphere materials upwelling. The partial melting of the mantle wedge induced by Pacific slab dehydration and the concurrent upwelling of mantle materials jointly drove volcanism in the transition zone. Conversely, the flat subduction of the Yakutat slab into the mantle wedge overlying the Pacific slab effectively hindered the upwelling of hot hybrid materials, cooling the Pacific mantle wedge. These results offer a new perspective on the influence of slab dynamics on volcanic and magmatic processes in the region and represent an advancement in our understanding compared to previous studies, which did not resolve the tears within the slab or their geodynamic implications at this level of detail.

In response to the challenges posed by rugged terrain in Yunnan, hindering large-scale mudslide screening efforts, this article introduces a dual-channel Convolutional Neural Network (CNN) constructed using elevation data from historical mudslide-prone valleys (Digital Elevation Model, DEM) and remote sensing imagery. The network is designed to facilitate the comprehensive assessment of potential mudslide hazards in gullies, serving as a crucial tool for early mudslide disaster warning. The model initially employs an enhanced residual structure to extract fundamental features from both types of data. Subsequently, it leverages the SE module and deep separable structure to emphasize the importance of relevant features and expedite model convergence. Finally, the model classifies the gullies under evaluation based on their similarity to gullies where mudslides have previously occurred. Experimental results demonstrate the model’s robust performance in assessing mudflow-prone gullies, achieving an impressive precision rate of up to 81.10% and a recall rate of 82.76%. When applied to evaluate the potential hazard of mudslide gullies across the entirety of Nujiang Prefecture, the model predicts that 87.80% of the mudslide locations are at an extremely high risk. These findings underscore the viability of utilizing image-based gully feature analysis for assessing the hazard levels of mudslide-prone gullies.

This experimental study investigated the influence of different turbine guide vane parameters on the ignition limit and light-round processes in a triple-dome combustor. It was found that for the triple-dome combustor, the minimum fuel/air ratios at the ignition limit all show a trend of initial decrease followed by subsequent increase with the growth of incoming air mass flow rate. The ignition fuel/air ratio decreases with the increase in turbine blockage ratio, and the optimal scheme is achieved with a blockage ratio of 0.8. Under the condition where the incoming air mass flow rate is 0.089 kg/s, the light-round time decreases with the increase in fuel/air ratio, and the light-round time of the combustor without guide vanes is shorter than that of the other schemes. With the increase in incoming air mass flow rate, the light-round time of the schemes with guide vanes is shortened. Under the same incoming air mass flow rate and fuel/air ratio, the increase in the blockage ratio will lead to an increase in the light-round time of the triple-dome combustor.

Debris flow susceptibility evaluation plays a crucial role in the prevention and control of debris flow disasters. Therefore, this paper proposes a convolutional neural network model named multi-level feature extraction network (MFENet). First, a dual-channel CNN architecture incorporating the Embedding Channel Attention mechanism is used to extract shallow features from both digital elevation model images and multispectral images. Subsequently, channel shuffle and feature concatenation are applied to the features from the two channels to obtain fused feature sets. Following this, a deep feature extraction is performed on the fused feature sets using a residual module improved by maximum pooling. Finally, the susceptibility index of gullies to debris flows is calculated based on the similarity scores. Experimental results demonstrate that the model exhibits favorable classification performance, with an accuracy of 73.45%. Furthermore, the percentage of debris flow valleys in high and very high susceptibility zones reaches 93.97%. [ABSTRACT FROM AUTHOR]