Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve
In order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/3/515 |
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| author | Zining Yu Katsumi Hattori Kaiguang Zhu Mengxuan Fan Dedalo Marchetti Xiaodan He Chengquan Chi |
| author_facet | Zining Yu Katsumi Hattori Kaiguang Zhu Mengxuan Fan Dedalo Marchetti Xiaodan He Chengquan Chi |
| author_sort | Zining Yu |
| collection | DOAJ |
| description | In order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In this study, we further evaluate the possibility of strain anomalies containing earthquake precursors by using Receiver Operating Characteristic (ROC) prediction. First, strain network anomalies were extracted in the borehole strain data recorded in Western China during 2010–2017. Then, we proposed a new prediction strategy characterized by the number of network anomalies in an anomaly window, <inline-formula><math display="inline"><semantics><msub><mi>N</mi><mrow><mi>a</mi><mi>n</mi><mi>o</mi></mrow></msub></semantics></math></inline-formula>, and the length of alarm window, <inline-formula><math display="inline"><semantics><msub><mi>T</mi><mrow><mi>a</mi><mi>l</mi><mi>m</mi></mrow></msub></semantics></math></inline-formula>. We assumed that clusters of network anomalies indicate a probability increase of an impending earthquake, and consequently, the alarm window would be the duration during which a possible earthquake would occur. The Area Under the ROC Curve (AUC) between true predicted rate, <inline-formula><math display="inline"><semantics><mrow><mi>t</mi><mi>p</mi><mi>r</mi></mrow></semantics></math></inline-formula>, and false alarm rate, <inline-formula><math display="inline"><semantics><mrow><mi>f</mi><mi>p</mi><mi>r</mi></mrow></semantics></math></inline-formula>, is measured to evaluate the efficiency of the prediction strategies. We found that the optimal strategy of short-term forecasts was established by setting the number of anomalies greater than 7 within 14 days and the alarm window at one day. The results further show the prediction strategy performs significantly better when there are frequent enhanced network anomalies prior to the larger earthquakes surrounding the strain network region. The ROC detection indicates that strain data possibly contain the precursory information associated with major earthquakes and highlights the potential for short-term earthquake forecasting. |
| first_indexed | 2024-03-09T06:12:48Z |
| format | Article |
| id | doaj.art-91e6b07dfda940eeb8558272b1d4181c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T06:12:48Z |
| publishDate | 2021-02-01 |
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| spelling | doaj.art-91e6b07dfda940eeb8558272b1d4181c2023-12-03T11:57:18ZengMDPI AGRemote Sensing2072-42922021-02-0113351510.3390/rs13030515Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic CurveZining Yu0Katsumi Hattori1Kaiguang Zhu2Mengxuan Fan3Dedalo Marchetti4Xiaodan He5Chengquan Chi6Key Laboratory of Geo-Exploration Instrumentation, Ministry of Education, Jilin University, Changchun 130061, ChinaGraduate School of Science, Chiba University, Inage, Chiba 263-8522, JapanKey Laboratory of Geo-Exploration Instrumentation, Ministry of Education, Jilin University, Changchun 130061, ChinaKey Laboratory of Geo-Exploration Instrumentation, Ministry of Education, Jilin University, Changchun 130061, ChinaThe College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, ChinaKey Laboratory of Geo-Exploration Instrumentation, Ministry of Education, Jilin University, Changchun 130061, ChinaSchool of Information Science and Technology, Hainan Normal University, Haikou 571158, ChinaIn order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In this study, we further evaluate the possibility of strain anomalies containing earthquake precursors by using Receiver Operating Characteristic (ROC) prediction. First, strain network anomalies were extracted in the borehole strain data recorded in Western China during 2010–2017. Then, we proposed a new prediction strategy characterized by the number of network anomalies in an anomaly window, <inline-formula><math display="inline"><semantics><msub><mi>N</mi><mrow><mi>a</mi><mi>n</mi><mi>o</mi></mrow></msub></semantics></math></inline-formula>, and the length of alarm window, <inline-formula><math display="inline"><semantics><msub><mi>T</mi><mrow><mi>a</mi><mi>l</mi><mi>m</mi></mrow></msub></semantics></math></inline-formula>. We assumed that clusters of network anomalies indicate a probability increase of an impending earthquake, and consequently, the alarm window would be the duration during which a possible earthquake would occur. The Area Under the ROC Curve (AUC) between true predicted rate, <inline-formula><math display="inline"><semantics><mrow><mi>t</mi><mi>p</mi><mi>r</mi></mrow></semantics></math></inline-formula>, and false alarm rate, <inline-formula><math display="inline"><semantics><mrow><mi>f</mi><mi>p</mi><mi>r</mi></mrow></semantics></math></inline-formula>, is measured to evaluate the efficiency of the prediction strategies. We found that the optimal strategy of short-term forecasts was established by setting the number of anomalies greater than 7 within 14 days and the alarm window at one day. The results further show the prediction strategy performs significantly better when there are frequent enhanced network anomalies prior to the larger earthquakes surrounding the strain network region. The ROC detection indicates that strain data possibly contain the precursory information associated with major earthquakes and highlights the potential for short-term earthquake forecasting.https://www.mdpi.com/2072-4292/13/3/515receiver operating characteristica new prediction strategyfrequent network anomaliesprediction efficiencyshort-term earthquake forecasting |
| spellingShingle | Zining Yu Katsumi Hattori Kaiguang Zhu Mengxuan Fan Dedalo Marchetti Xiaodan He Chengquan Chi Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve Remote Sensing receiver operating characteristic a new prediction strategy frequent network anomalies prediction efficiency short-term earthquake forecasting |
| title | Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve |
| title_full | Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve |
| title_fullStr | Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve |
| title_full_unstemmed | Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve |
| title_short | Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve |
| title_sort | evaluation of pre earthquake anomalies of borehole strain network by using receiver operating characteristic curve |
| topic | receiver operating characteristic a new prediction strategy frequent network anomalies prediction efficiency short-term earthquake forecasting |
| url | https://www.mdpi.com/2072-4292/13/3/515 |
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