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Generative self-supervised learning for seismic event classification
DOI:10.1016/j.engappai.2025.113355.png)
摘要
En 中文
• 用于高效提取地震事件特征的生成式自监督模型。
• 一种最小化对大量标注数据需求的半监督学习方法。
• 基于 SOM(自组织映射)的方法,可显著提升专家手动数据标注的效率。
期刊
IF:
8
论文数:
5.7K
被引数:
3.5W
机构
引用论文
Ensemble and Self-supervised Learning for Improved Classification of Seismic Signals from the Åknes Rockslope集成学习与自监督学习在Åknes岩石边坡地震信号分类性能提升中的应用
Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings基于图的特征权重优化和连续地震传感器阵列记录的分类
SENSORS
IF3.5
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI斯坦福地震数据集 (STEAD): 人工智能地震信号的全球数据集
IEEE ACCESS
IF3.6
Evaluation of road blockage induced by seismic landslides under 2021 MS6.4 Yangbi earthquake2021年6.4级漾濞地震下地震滑坡引起的道路堵塞评估
Intelligent Recognition of Valid Microseismic Events Based on Self-supervised Learning
MEASUREMENT
IF5.6
Classification of clustered microseismic events in a coal mine using machine learning基于机器学习的煤矿微震事件聚类分类

