Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses
The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorologica...
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2025-01-01
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| author | Masashi Hayakawa Shinji Hirooka Koichiro Michimoto Stelios M. Potirakis Yasuhide Hobara |
| author_facet | Masashi Hayakawa Shinji Hirooka Koichiro Michimoto Stelios M. Potirakis Yasuhide Hobara |
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| description | The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological “open” data of AMeDAS (Automated Meteorological Data Acquisition System), which is a very dense “ground-based” network of meteorological stations with higher temporal and spatial resolutions than the satellite remote sensing open data. In order to obtain a clearer identification of any seismogenic effect, we have used the AMeDAS station data at local midnight (LT = 01 h) and our initial target EQ was chosen to be the famous 1995 Kobe EQ of 17 January 1995 (M = 7.3). Initially, we performed conventional statistical analysis with confidence bounds and it was found that the Kobe station (very close to the EQ epicenter) exhibited conspicuous anomalies in both physical parameters on 10 January 1995, just one week before the EQ, exceeding m (mean) + 3σ (standard deviation) in T/Hum and well above m + 2σ in ACP within the short-term window of one month before and two weeks after an EQ. When looking at the whole period of over one year including the day of the EQ, in the case of T/Hum only we detected three additional extreme anomalies, except in winter, but with unknown origins. On the other hand, the anomalous peak on 10 January 1995 was the largest for ACP. Further, the spatial distributions of the anomaly intensity of the two quantities have been presented using about 40 stations to provide a further support to the close relationship of this peak with the EQ. The above statistical analysis has been compared with an analysis with recent machine/deep learning methods. We have utilized a combinational use of NARX (Nonlinear Autoregressive model with eXogenous inputs) and Long Short-Term Memory (LSTM) models, which was successful in objectively re-confirming the anomalies in both parameters on the same day prior to the EQ. The combination of these analysis results elucidates that the meteorological anomalies on 10 January 1995 are considered to be a notable precursor to the EQ. Finally, we suggest a joint examination of our two meteorological quantities for their potential use in real short-term EQ prediction, as well as in the future lithosphere–atmosphere–ionosphere coupling (LAIC) studies as the information from the bottom part of LAIC. |
| format | Article |
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| institution | Kabale University |
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| spelling | doaj-art-15777a6ef76c4dff81dd53f75fa942ae2025-01-24T13:21:59ZengMDPI AGAtmosphere2073-44332025-01-011618810.3390/atmos16010088Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based AnalysesMasashi Hayakawa0Shinji Hirooka1Koichiro Michimoto2Stelios M. Potirakis3Yasuhide Hobara4Hayakawa Institute of Seismo Electromagnetics, Co., Ltd. (Hi-SEM), UEC Alliance Center #521, 1-1-1 Kojima-cho, Chofu, Tokyo 182-0026, JapanHiSR Lab LLC, 2-2-15 Minami-Aoyama, Minato-ku, Tokyo 107-0062, JapanHayakawa Institute of Seismo Electromagnetics, Co., Ltd. (Hi-SEM), UEC Alliance Center #521, 1-1-1 Kojima-cho, Chofu, Tokyo 182-0026, JapanDepartment of Electronics and Computer Engineering, Ancient Olive Grove Campus, University of West Attica, 12244 Athens, GreeceDepartment of Computer and Network Engineering, The University of Electro-Communications (UEC), 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, JapanThe purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological “open” data of AMeDAS (Automated Meteorological Data Acquisition System), which is a very dense “ground-based” network of meteorological stations with higher temporal and spatial resolutions than the satellite remote sensing open data. In order to obtain a clearer identification of any seismogenic effect, we have used the AMeDAS station data at local midnight (LT = 01 h) and our initial target EQ was chosen to be the famous 1995 Kobe EQ of 17 January 1995 (M = 7.3). Initially, we performed conventional statistical analysis with confidence bounds and it was found that the Kobe station (very close to the EQ epicenter) exhibited conspicuous anomalies in both physical parameters on 10 January 1995, just one week before the EQ, exceeding m (mean) + 3σ (standard deviation) in T/Hum and well above m + 2σ in ACP within the short-term window of one month before and two weeks after an EQ. When looking at the whole period of over one year including the day of the EQ, in the case of T/Hum only we detected three additional extreme anomalies, except in winter, but with unknown origins. On the other hand, the anomalous peak on 10 January 1995 was the largest for ACP. Further, the spatial distributions of the anomaly intensity of the two quantities have been presented using about 40 stations to provide a further support to the close relationship of this peak with the EQ. The above statistical analysis has been compared with an analysis with recent machine/deep learning methods. We have utilized a combinational use of NARX (Nonlinear Autoregressive model with eXogenous inputs) and Long Short-Term Memory (LSTM) models, which was successful in objectively re-confirming the anomalies in both parameters on the same day prior to the EQ. The combination of these analysis results elucidates that the meteorological anomalies on 10 January 1995 are considered to be a notable precursor to the EQ. Finally, we suggest a joint examination of our two meteorological quantities for their potential use in real short-term EQ prediction, as well as in the future lithosphere–atmosphere–ionosphere coupling (LAIC) studies as the information from the bottom part of LAIC.https://www.mdpi.com/2073-4433/16/1/88meteorological parameterstemperature/HumACP (atmospheric chemical potential)the 1995 Kobe EQAMeDAS (Automated Meteorological Data Acquisition System)machine learning/deep learning |
| spellingShingle | Masashi Hayakawa Shinji Hirooka Koichiro Michimoto Stelios M. Potirakis Yasuhide Hobara Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses Atmosphere meteorological parameters temperature/Hum ACP (atmospheric chemical potential) the 1995 Kobe EQ AMeDAS (Automated Meteorological Data Acquisition System) machine learning/deep learning |
| title | Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses |
| title_full | Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses |
| title_fullStr | Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses |
| title_full_unstemmed | Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses |
| title_short | Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses |
| title_sort | meteorological anomalies during earthquake preparation a case study for the 1995 kobe earthquake m 7 3 based on statistical and machine learning based analyses |
| topic | meteorological parameters temperature/Hum ACP (atmospheric chemical potential) the 1995 Kobe EQ AMeDAS (Automated Meteorological Data Acquisition System) machine learning/deep learning |
| url | https://www.mdpi.com/2073-4433/16/1/88 |
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