A FRACTURE ZONE EXTRACTION METHOD FOR LIDAR POINT CLOUD BASED ON MULTI-SCALE NEURAL NETWORK WITH RS-CONV
Fracture zones are geological formations resulting from the strong movement of the Earth's crust,often manifesting as fragile and sensitive areas.These zones are closely linked to natural disasters such as earthquakes and landslides.Accurate extraction of fracture zones is crucial for quantitative studies of earthquake faults,providing a scientific basis for risk assessment and decision-making in earthquake prevention and mitigation.Thus,an in-depth study to determine their distribution patterns and surface geometry is essential for understanding earthquake dynamics and mechanisms.This paper addresses the shortcomings of existing methods in extracting fracture zones from LiDAR point clouds,which often suffer from incomplete extraction,poor continuity,and high error rates.We propose a method based on a multi-scale neural network with RS-Conv to improve the automatic extraction of fault zones in complex terrain regions.Fracture zones exhibit complex morphologies and scale features;therefore,single-scale neighborhood point sets fail to reveal their intrinsic structural information fully.Our approach begins by constructing neighborhood point sets at different spatial scales to comprehensively examine geometric features at various levels within the point cloud.The RS-Conv operator effectively portrays the spatial relationship between the center point and neighboring points.We then build a multi-scale neural network model using the RS-Conv operator as the convolution module.This model captures the spatial relationships in the point cloud,efficiently extracting deep features at different scales.The extracted multi-scale features are concatenated to form a richer and more comprehensive feature representation,which is inputted into a fully connected layer to classify the centroid and solve the fracture zone extraction problem.We compared our method with the Tensor Decomposition and Deep Neural Networks(DNN)methods using the ISPRS point cloud dataset,the Sichuan-Yunnan point cloud dataset,and the Xianshuihe dataset.Results show that our method achieves the highest classification accuracy across all three datasets.Specifically,our method's total classification error is only 0.3%,a reduction of 0.91%-2.79%compared to other methods.This significant error reduction demonstrates the accuracy,stability,and reliability of our proposed method in handling complex point cloud data.The main conclusions of this study are as follows:(1)The construction of neighborhood point sets at different scales reveals that the combination of these scales significantly impacts the model's classification performance.Selecting appropriate scale combinations is crucial for optimizing the model's classification accuracy,facilitating better distinction between fracture zone points and non-fracture zone points.(2)Compared to traditional and machine learning methods,the deep learning network model developed in this study shows significant advantages in extracting fracture zones from point clouds.The model can automatically learn deep features from point cloud data and process large-scale,high-dimensional point cloud datasets,thereby achieving more accurate fracture zone extraction in complex terrain conditions.(3)Comparative experiments on different datasets further demonstrate the proposed method's generalization ability.It is effective not only in extracting fracture zones under single terrain conditions but also in maintaining stable performance across multiple terrain conditions.This adaptability enhances the extraction of fracture zones in various terrain scenarios.In conclusion,the method proposed in this paper offers a novel approach to fracture zone extraction.It achieves higher classification accuracy compared to existing traditional and machine learning methods,effectively addressing the challenge of fracture zone extraction in complex terrain areas.
LiDAR point cloudmulti-scale neighborhood point setsdeep learningfracture zone extraction
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