2023年12月2日菲律宾棉兰老岛附近海域7.6级地震的快速产出参数
Progress in Earthquake Sciences（2024）
中国地震局地球物理研究所
Abstract
北京时间2023年12月2日22时37分在菲律宾棉兰老岛(Mindanao)东部海域发生7.6级地震.中国地震局地球物理研究所在震后启动快速响应,组织相关领域研究人员对此次地震的震源参数、震源机制、破裂过程和地震辐射能量等进行了估计,基于震源模型进行了震动图模拟、同震形变场模拟.结果表明,此次地震发生在菲律宾海板块与欧亚板块/巽他地块碰撞的俯冲带板片上,以逆冲机制为主,能量集中在前约40 s内释放,断层破裂最大滑动量达到7 m;震源辐射地震能量的效率偏低,慢度系数略低于全球平均水平,与同样矩震级大小的地震相比,震感不强烈;极震区震动烈度可能达Ⅸ度以上,可能的受灾范围近27000 km2;此次地震引起了显著的同震位移,最大水平向位移达到0.6 m、垂直向位移达到1.2 m.综合分析可知,此次地震不会产生大规模海啸.
MoreTranslated text
Key words
the Mindanao,Philippines earthquake,source parameters,rupture process,Shakemap prediction,coseismic deformation simulation,earthquake radiation energy,InSAR,3-dimensional deformation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined