Groundwater Radon Precursor Anomalies Identification by EMD-LSTM Model
Groundwater radon concentrations can reflect the changes of crustal stress and strain. Scholars and scientific institutions have also recorded groundwater radon precursor anomalies before earthquakes. Therefore, groundwater radon monitoring is an effective means of predicting seismic activities. How...
| Main Authors: | , , , , , , |
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/1/69 |
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| author | Xiaobo Feng Jun Zhong Rui Yan Zhihua Zhou Lei Tian Jing Zhao Zhengyi Yuan |
| author_facet | Xiaobo Feng Jun Zhong Rui Yan Zhihua Zhou Lei Tian Jing Zhao Zhengyi Yuan |
| author_sort | Xiaobo Feng |
| collection | DOAJ |
| description | Groundwater radon concentrations can reflect the changes of crustal stress and strain. Scholars and scientific institutions have also recorded groundwater radon precursor anomalies before earthquakes. Therefore, groundwater radon monitoring is an effective means of predicting seismic activities. However, the variation of radon concentrations within groundwater is not only affected by structural factors, but also by environmental factors, such as air pressure, temperature, and rainfall. This causes difficulty in identifying the possible precursor anomalies. Therefore, the EMD-LSTM model is proposed to identify the radon anomalies. This study investigated the time series data of groundwater radon from well #32 located in Sichuan province. Three models (including the LSTM (Long Short-Term Memory) model with auxiliary data, the EMD-LSTM (Empirical Mode Decomposition Long Short-Term Memory) model with auxiliary data, and the EMD-LSTM model without auxiliary data) were developed in order to predict groundwater radon variations. The results indicated that the prediction accuracy of the EMD-LSTM model was much higher than that of the LSTM model, and the EMD-LSTM model without auxiliary data also can obtain an ideal prediction result. Furthermore, the different durations of seismic activities T (T = ±10, ±30, ±50, and ±100) were also investigated by comparing the identification results. The identification rate of the precursor anomalies was the highest when T = ±30. The EMD-LSTM model identified five possible radon anomalies among the seven selected earthquakes. Taking well #32 as an example, we provided a promising method, that was the EMD-LSTM model, to detect the groundwater radon anomalies. It also suggested that the EMD-LSTM model can be used to identify the possible precursor anomalies within future studies. |
| first_indexed | 2024-03-10T03:12:53Z |
| format | Article |
| id | doaj.art-efc0eb6d4e554d01ae84f7ab6d43b3db |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T03:12:53Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-efc0eb6d4e554d01ae84f7ab6d43b3db2023-11-23T12:32:23ZengMDPI AGWater2073-44412022-01-011416910.3390/w14010069Groundwater Radon Precursor Anomalies Identification by EMD-LSTM ModelXiaobo Feng0Jun Zhong1Rui Yan2Zhihua Zhou3Lei Tian4Jing Zhao5Zhengyi Yuan6China Earthquake Networks Center, Beijing 100045, ChinaChina Earthquake Networks Center, Beijing 100045, ChinaChina Earthquake Networks Center, Beijing 100045, ChinaChina Earthquake Networks Center, Beijing 100045, ChinaChina Earthquake Networks Center, Beijing 100045, ChinaChina Earthquake Networks Center, Beijing 100045, ChinaChina Earthquake Networks Center, Beijing 100045, ChinaGroundwater radon concentrations can reflect the changes of crustal stress and strain. Scholars and scientific institutions have also recorded groundwater radon precursor anomalies before earthquakes. Therefore, groundwater radon monitoring is an effective means of predicting seismic activities. However, the variation of radon concentrations within groundwater is not only affected by structural factors, but also by environmental factors, such as air pressure, temperature, and rainfall. This causes difficulty in identifying the possible precursor anomalies. Therefore, the EMD-LSTM model is proposed to identify the radon anomalies. This study investigated the time series data of groundwater radon from well #32 located in Sichuan province. Three models (including the LSTM (Long Short-Term Memory) model with auxiliary data, the EMD-LSTM (Empirical Mode Decomposition Long Short-Term Memory) model with auxiliary data, and the EMD-LSTM model without auxiliary data) were developed in order to predict groundwater radon variations. The results indicated that the prediction accuracy of the EMD-LSTM model was much higher than that of the LSTM model, and the EMD-LSTM model without auxiliary data also can obtain an ideal prediction result. Furthermore, the different durations of seismic activities T (T = ±10, ±30, ±50, and ±100) were also investigated by comparing the identification results. The identification rate of the precursor anomalies was the highest when T = ±30. The EMD-LSTM model identified five possible radon anomalies among the seven selected earthquakes. Taking well #32 as an example, we provided a promising method, that was the EMD-LSTM model, to detect the groundwater radon anomalies. It also suggested that the EMD-LSTM model can be used to identify the possible precursor anomalies within future studies.https://www.mdpi.com/2073-4441/14/1/69radon anomalyearthquake precursorEmpirical Mode DecompositionLong Short-Term Memorytrend prediction |
| spellingShingle | Xiaobo Feng Jun Zhong Rui Yan Zhihua Zhou Lei Tian Jing Zhao Zhengyi Yuan Groundwater Radon Precursor Anomalies Identification by EMD-LSTM Model Water radon anomaly earthquake precursor Empirical Mode Decomposition Long Short-Term Memory trend prediction |
| title | Groundwater Radon Precursor Anomalies Identification by EMD-LSTM Model |
| title_full | Groundwater Radon Precursor Anomalies Identification by EMD-LSTM Model |
| title_fullStr | Groundwater Radon Precursor Anomalies Identification by EMD-LSTM Model |
| title_full_unstemmed | Groundwater Radon Precursor Anomalies Identification by EMD-LSTM Model |
| title_short | Groundwater Radon Precursor Anomalies Identification by EMD-LSTM Model |
| title_sort | groundwater radon precursor anomalies identification by emd lstm model |
| topic | radon anomaly earthquake precursor Empirical Mode Decomposition Long Short-Term Memory trend prediction |
| url | https://www.mdpi.com/2073-4441/14/1/69 |
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