机器能像人类一样学会选择第一次休息吗?

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI:10.3997/2214-4609.201803026

L. Yalcinoglu, C. Stotter

{"title":"机器能像人类一样学会选择第一次休息吗?","authors":"L. Yalcinoglu, C. Stotter","doi":"10.3997/2214-4609.201803026","DOIUrl":null,"url":null,"abstract":"Machine learning is a well-suited tool for first break picking since the process relies on detecting similar features between the seismic traces and thus is a kind of pattern recognition problem. The method we present in this paper applies support vector machine (SVM) as a machine learning algorithm for first break picking which achieve high accuracy.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Can Machines Learn To Pick First Breaks As Humans Do?\",\"authors\":\"L. Yalcinoglu, C. Stotter\",\"doi\":\"10.3997/2214-4609.201803026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning is a well-suited tool for first break picking since the process relies on detecting similar features between the seismic traces and thus is a kind of pattern recognition problem. The method we present in this paper applies support vector machine (SVM) as a machine learning algorithm for first break picking which achieve high accuracy.\",\"PeriodicalId\":231338,\"journal\":{\"name\":\"First EAGE/PESGB Workshop Machine Learning\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First EAGE/PESGB Workshop Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.201803026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First EAGE/PESGB Workshop Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201803026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

机器学习是一种非常适合的工具，因为该过程依赖于检测地震轨迹之间的相似特征，因此是一种模式识别问题。本文提出的方法将支持向量机作为一种机器学习算法，实现了高准确度的首断口拾取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Can Machines Learn To Pick First Breaks As Humans Do?

Machine learning is a well-suited tool for first break picking since the process relies on detecting similar features between the seismic traces and thus is a kind of pattern recognition problem. The method we present in this paper applies support vector machine (SVM) as a machine learning algorithm for first break picking which achieve high accuracy.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

First EAGE/PESGB Workshop Machine Learning

自引率

0.00%

发文量