Global S&T Development Trend Analysis Platform of Resources and Environment
Machine Learning Model Generates Realistic Seismic Waveforms | |
admin | |
2021-04-22 | |
发布年 | 2021 |
语种 | 英语 |
国家 | 美国 |
领域 | 气候变化 |
正文(英文) | A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the e?cacy of our generative model, we applied it to seismic ?eld data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets. In recent years, automatic detection methods based on machine learning have improved the accuracy and efficiency of data collection; however, the accuracy of those methods relies on access to a large amount of high?quality, labeled training data, often tens of thousands of records or more. To resolve this data dilemma, the research team developed SeismoGen based on a generative adversarial network (GAN), which is a type of deep generative model that can generate high?quality synthetic samples in multiple domains. In other words, deep generative models train machines to do things and create new data that could pass as real. Once trained, the SeismoGen model is capable of producing realistic seismic waveforms of multiple labels. When applied to real Earth seismic datasets in Oklahoma, the team saw that data augmentation from SeismoGen?generated synthetic waveforms could be used to improve earthquake detection algorithms in instances when only small amounts of labeled training data are available.
make a difference: sponsored opportunity
Story Source: Materials provided by DOE/Los Alamos National Laboratory. Note: Content may be edited for style and length. Journal Reference:
Cite This Page: DOE/Los Alamos National Laboratory. "Machine learning model generates realistic seismic waveforms: New research could reduce manual labor and improve earthquake detection." ScienceDaily. ScienceDaily, 22 April 2021.
DOE/Los Alamos National Laboratory. (2021, April 22). Machine learning model generates realistic seismic waveforms: New research could reduce manual labor and improve earthquake detection. ScienceDaily. Retrieved April 23, 2021 from www.sciencedaily.com/releases/2021/04/210422181851.htm
DOE/Los Alamos National Laboratory. "Machine learning model generates realistic seismic waveforms: New research could reduce manual labor and improve earthquake detection." ScienceDaily. www.sciencedaily.com/releases/2021/04/210422181851.htm (accessed April 23, 2021).
|
URL | 查看原文 |
来源平台 | Science Daily |
文献类型 | 新闻 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/323298 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | admin. Machine Learning Model Generates Realistic Seismic Waveforms. 2021. |
条目包含的文件 | 条目无相关文件。 |
个性服务 |
推荐该条目 |
保存到收藏夹 |
查看访问统计 |
导出为Endnote文件 |
谷歌学术 |
谷歌学术中相似的文章 |
[admin]的文章 |
百度学术 |
百度学术中相似的文章 |
[admin]的文章 |
必应学术 |
必应学术中相似的文章 |
[admin]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论