Prabhat Man Singh Basnet, Aibing Jin, Shakil Mahtab
{"title":"基于可解释的机器学习方法,利用监测到的微地震开发可解释的岩爆风险预测方法","authors":"Prabhat Man Singh Basnet, Aibing Jin, Shakil Mahtab","doi":"10.1007/s11600-024-01338-y","DOIUrl":null,"url":null,"abstract":"<div><p>The short-term rockburst prediction in underground engineering plays a significant role in the safety of the workers and equipment. Due to the complex link between microseismicity and the rockburst occurrence, prediction of short-term rockburst severity is always challenging. It is, therefore, necessary to develop an intelligent model that can predict rockbursts with high accuracy. Besides the predicting capability, it is essential to understand the model’s interpretability regarding the decisions to ensure reliability, trust and accountability. Accordingly, this paper employs the knowledge of explainable artificial intelligences (XAI) by proposing a novel glass-box machine learning model: explainable boosting machine (EBM) to predict the short-term rockburst. Microseismic (MS) data obtained from the underground engineering projects are utilized to build the model, which is also compared with the black-box random forest (RF) model. The result shows that EBM can accurately predict the rockburst severity with high accuracy, while providing with the underlined reasoning behind the prediction from the global and local perspectives. The EBM global explanation reveals that MS energy followed by MS apparent volume and the MS events is the most contributing factor to determining the Rockburst severity. It also gives insights into the relationship between MS factors and rockburst risks, delivering how various MS parameters impact the model predictions. The local explanation extracts the understanding of wrongly predicted samples. The interpretability and transparency of the proposed method will facilitate understanding the model’s decision which adds effective guidance evaluating the short-term rockburst risks.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"72 4","pages":"2597 - 2618"},"PeriodicalIF":2.3000,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Developing an explainable rockburst risk prediction method using monitored microseismicity based on interpretable machine learning approach\",\"authors\":\"Prabhat Man Singh Basnet, Aibing Jin, Shakil Mahtab\",\"doi\":\"10.1007/s11600-024-01338-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The short-term rockburst prediction in underground engineering plays a significant role in the safety of the workers and equipment. Due to the complex link between microseismicity and the rockburst occurrence, prediction of short-term rockburst severity is always challenging. It is, therefore, necessary to develop an intelligent model that can predict rockbursts with high accuracy. Besides the predicting capability, it is essential to understand the model’s interpretability regarding the decisions to ensure reliability, trust and accountability. Accordingly, this paper employs the knowledge of explainable artificial intelligences (XAI) by proposing a novel glass-box machine learning model: explainable boosting machine (EBM) to predict the short-term rockburst. Microseismic (MS) data obtained from the underground engineering projects are utilized to build the model, which is also compared with the black-box random forest (RF) model. The result shows that EBM can accurately predict the rockburst severity with high accuracy, while providing with the underlined reasoning behind the prediction from the global and local perspectives. The EBM global explanation reveals that MS energy followed by MS apparent volume and the MS events is the most contributing factor to determining the Rockburst severity. It also gives insights into the relationship between MS factors and rockburst risks, delivering how various MS parameters impact the model predictions. The local explanation extracts the understanding of wrongly predicted samples. The interpretability and transparency of the proposed method will facilitate understanding the model’s decision which adds effective guidance evaluating the short-term rockburst risks.</p></div>\",\"PeriodicalId\":6988,\"journal\":{\"name\":\"Acta Geophysica\",\"volume\":\"72 4\",\"pages\":\"2597 - 2618\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-05-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Geophysica\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11600-024-01338-y\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Geophysica","FirstCategoryId":"89","ListUrlMain":"https://link.springer.com/article/10.1007/s11600-024-01338-y","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Developing an explainable rockburst risk prediction method using monitored microseismicity based on interpretable machine learning approach
The short-term rockburst prediction in underground engineering plays a significant role in the safety of the workers and equipment. Due to the complex link between microseismicity and the rockburst occurrence, prediction of short-term rockburst severity is always challenging. It is, therefore, necessary to develop an intelligent model that can predict rockbursts with high accuracy. Besides the predicting capability, it is essential to understand the model’s interpretability regarding the decisions to ensure reliability, trust and accountability. Accordingly, this paper employs the knowledge of explainable artificial intelligences (XAI) by proposing a novel glass-box machine learning model: explainable boosting machine (EBM) to predict the short-term rockburst. Microseismic (MS) data obtained from the underground engineering projects are utilized to build the model, which is also compared with the black-box random forest (RF) model. The result shows that EBM can accurately predict the rockburst severity with high accuracy, while providing with the underlined reasoning behind the prediction from the global and local perspectives. The EBM global explanation reveals that MS energy followed by MS apparent volume and the MS events is the most contributing factor to determining the Rockburst severity. It also gives insights into the relationship between MS factors and rockburst risks, delivering how various MS parameters impact the model predictions. The local explanation extracts the understanding of wrongly predicted samples. The interpretability and transparency of the proposed method will facilitate understanding the model’s decision which adds effective guidance evaluating the short-term rockburst risks.
期刊介绍:
Acta Geophysica is open to all kinds of manuscripts including research and review articles, short communications, comments to published papers, letters to the Editor as well as book reviews. Some of the issues are fully devoted to particular topics; we do encourage proposals for such topical issues. We accept submissions from scientists world-wide, offering high scientific and editorial standard and comprehensive treatment of the discussed topics.