# Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake

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## Abstract

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_{w}7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.

## 1. Introduction

_{w}≥ 6 during the time period of 2000–2014 [15]. Still, there are many well-published studies about the atmospheric and ionospheric anomalies associated with earthquakes. For example, both positive and negative TEC anomalies are detected by utilizing statistical analysis on ground receiver data for the M

_{w}7.8 Nepal earthquake [16]. Furthermore, significant TEC variations were also observed before and after the 2014 M

_{w}6.9 Samothrace earthquake [17]. An anomalous variation of more than 30 W/m

^{2}was observed in OLR for the Japan earthquakes and other events [5,18,19,20,21]. The reflection, emission, and absorption of OLR are due to a complicated system of aerosols, clouds, ocean surface temperature, and land surface temperature variations during the main shock preparation period [22]. Both the negative and positive anomalies are observed in the SST variable, which is considered to be the most important atmospheric precursor [23]. The emission of radon resulted in anomalous variations of AT and RH over the epicenter, as explained in the LAIC model [9]. Similarly, stress and tectonic blocks’ movement activate the thermodynamics phenomena and ionization process that result in the anomalous variations of AT and RH. Recent earthquakes showed clear enhancement in the atmospheric and ionospheric variations associated with main shocks [24,25,26,27,28,29,30,31,32]. GNSS and RS have played a vital role in understanding and finding the variations in atmospheric and ionospheric parameters from various space observations and ground stations [33,34,35,36,37]. The use of different algorithm on time series data is also very common these days [38,39,40,41,42]. Nevertheless, there is a gigantic gap of applications and knowledge related to the science of earthquake precursors.

_{w}7.1 Namie, Japan earthquake by implementing statistical as well as machine learning procedures (NARX and LSTM) on TEC and other remote sensing indices, including RH, OLR, and SST. The main aim is to find a synchronized window of the atmospheric and ionospheric anomalies within the same occurrence days. Moreover, another aim is to find the presence of pre-and/or post-seismic anomalies. The structure of this paper is as follows: Study area and brief description of earthquake data are described in Section 2 and Section 3, respectively. Methodology is explained in Section 4. Results are described in Section 5. Discussion is explained in Section 6. Section 7 is dedicated to the conclusion.

## 2. Study Area

_{w}7.1 struck the east coast of Honshu, Japan on 13 February 2021, at 14:07:49 UTC (LT = UTC + 09:00 = 23:07:49). The epicenter was located at 73 km of Namie, Japan (37.7°N, 141.7°E). The United States Geological Survey (USGS) announced the maximum felt intensity of IX (heavy) and shaking intensity of VIII (severe) on a modified Mercalli scale. One person died in Fukushima and around 187 people were injured. There was a massive destruction to property with an approximate value of the damages being 9137 houses and 311 schools. The earthquake occurred as a result of thrust faulting near the subduction zone interface plate boundary between the North American and Pacific plates (Figure 1). Subduction was beneath the Japan Trench. The Pacific plate moves westward relative to the North America plate, at a velocity of 70 mm/yr. The several micro plates in this region describe the relative motions among Pacific, North American, and Eurasian plates. The February 13, 2021 earthquake struck in the vicinity of the rapture area of the March 11, 2011 great Tohoku earthquake, which was widely felt along many islands of Japan and killed almost 16,000 people. The epicenter of the February 13 earthquake was located approximately 74 km from the epicenter of the Tohoku earthquake. After the March 2011 M

_{w}9.1 Tohoku earthquake, six earthquakes of M

_{w}> 7 have occurred within 250 km of the February 13 earthquake. The USGS website contains more tectonic and technical information about this earthquake (https://earthquake.usgs.gov/earthquakes/eventpage/us6000dher/executive; accessed on 11 November 2022).

## 3. Materials and Datasets

#### 3.1. Outgoing Longwave Radiation

^{2}) data for both the day and night are acquired at spatial resolution of 1° of AIRS/Aqua from Goddard Earth Sciences Data and Information Services Center (GES DISC). The area averaged data of OLR time series covering the earthquake epicenter within a seismogenic area (139.4°E–143.9°E, 35.6°N–39.5°N) was retrieved from GIOVANNI with a web-based application developed by (GES DISC).

#### 3.2. Relative Humidity

#### 3.3. Air Temperature

#### 3.4. Sea Surface Temperature

#### 3.5. Total Electron Content

^{16}el/m

^{2}). The STEC values were used to calculate the VTEC values, as shown below [47].

## 4. Methodology

#### 4.1. Anomaly Detection Using Statistical Method

#### 4.2. Anomaly Detection Using Wavelet Transformation

#### 4.3. Anomaly Detection Using Artificial Neural Network (ANN)

#### 4.4. Nonlinear Autoregressive Network with Exogenous Inputs (NARX)

#### 4.5. Long Short-Term Memory (LSTM)

## 5. Results

#### 5.1. Outgoing Longwave Radiation

^{2}on six days before the earthquake (Figure 2a).

^{2}was also observed on the fifth day before the main shock. The time series analysis of OLR nighttime also showed a positive anomaly of 16 W/m

^{2}on the sixth day before the earthquake (Figure 2b). On the other hand, the wavelet transformation method implemented on OLR values for daytime and nighttime has demonstrated more evidence about the earthquake-induced anomalies. A clear OLR daytime anomaly of high magnitude occurred on the sixth and fifth days before the main shock (Figure 2e) and a significant nighttime OLR on the fifth day before the main shock (Figure 2f). Moreover, the deviations of NARX-predicted OLR daytime values showed clear anomalies within the five-day window before the seismic event (Figure 3c). Figure 3g showed a clear variation in LSTM-predicted OLR daytime values within the five-day window before the main event. NARX- and LSTM-predicted OLR nighttime values also endorse the anomalies within the 10-day window of the pre-seismic event (Figure 3d–h).

#### 5.2. Relative Humidity

#### 5.3. Air Temperature

#### 5.4. Sea Surface Temperature

#### 5.5. Total Electron Content

_{w}7.1, Japan earthquake showed clear seismo-ionospheric anomalies. IGS station (HYDE) variation was also observed outside the seismic preparation zone to illustrate the earthquake- and storm-induced TEC anomalies. Figure 10 illustrates the associated VTEC variations recorded at two IGS stations, which operate within the seismic preparation zone. Prominent positive deviations were observed −6 and −5 days before the main shock in both USUD and MTKA stations during quiet storm days. These anomalies are synchronized with the atmospheric anomalies and occurred in the same window as atmospheric anomalies. Similarly, NARX- and LSTM-predicted values also showed clear deviations in VTEC values on the 7th and 8th February for the two IGS stations i.e., USUD and MTKA (Figure 11 and Figure 12). USUD VTEC showed positive anomalies of 7.5 and 1.8 TECU on seven and nine days after the major event (Figure 10c). Figure 10d showed positive variations of 6.6, 1.6, and 6.7 TECU for MTKA VTEC on seven, eight, and nine days after the earthquake day, respectively. Positive TEC deviations were also observed seven and eight days after the seismic event for HYDE station (Figure 10e). During active storm days, VTEC variations for three IGS stations (USUD, MTKA, and HYDE) showed positive anomalies from 7–10 days after the earthquake day. NARX- and LSTM-predicted VTEC also showed clear deviations from 20th to 23rd February for three IGS stations during active storm days (Figure 11d and Figure 12d). All the atmospheric and ionospheric anomalies are listed in Table 1, Table 2 and Table 3.

## 6. Discussion

_{w}7.1, Japan earthquake. However, there is still a need for observations and analysis for detailed calculations of different earthquake precursors.

Parameters | Anomalous Day | Deviations from NARX Predicted Values | |
---|---|---|---|

Pre-EQ | Post-EQ | ||

OLR (Daytime) | −6, −5 | 58, 19.8 W/m^{2} | Nil |

OLR (Nighttime) | −6, −5 | 48, 6 W/m^{2} | Nil |

RH (Daytime) | −6 | −15% | Nil |

RH (Nighttime) | −6, −5 | −6, −27% | Nil |

AT | −5 | 8 °K | Nil |

SST | −7, −6 | −2.9, −9.7 °C | Nil |

VTEC (USUD) | −6, −5, 7, 9 | 3.2, 2.9 TECU | 7.6, 1.7 TECU |

VTEC (MTKA) | −6, −5, 7, 8, 9 | 3.3, 1.48 TECU | 7.3, 1.8, 6.3 TECU |

VTEC (HYDE) | 7, 8 | Nil | 9.7, 6.47 TECU |

Parameters | Anomalous Day | Deviations from LSTM Predicted Values | |
---|---|---|---|

Pre-EQ | Post-EQ | ||

OLR (Daytime) | −6, −5 | 53.8, 26 W/m^{2} | Nil |

OLR (Nighttime) | −6, −5 | 54, 9 W/m^{2} | Nil |

RH (Daytime) | −6 | −15.7% | Nil |

RH (Nighttime) | −6, −5 | −13.3, −34.5% | Nil |

AT | −5 | 7 °K | Nil |

SST | −7, −6 | −2, −8 °C | Nil |

VTEC (USUD) | −6, −5, 7, 9 | 3.18, 2 TECU | 7.4, 2.1 TECU |

VTEC (MTKA) | −6, −5, 7, 8, 9 | 3.23, 1.39 TECU | 6.5, 1.6, 5.8 TECU |

VTEC (HYDE) | 7, 8 | Nil | 9.63, 6.32 TECU |

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Geographical location of the 2021 M

_{w}7.1, Japan earthquake (Lat = 37.7°N, Long = 141.7°E) with tectonic plates and fault lines. The epicenter is indicated by the green star and the GNSS stations are indicated by black stars and the shaded circle area is the earthquake-impacted area estimated by the Dobrovolsky formula.

**Figure 2.**(

**a**) Daytime OLR averaged time series data with confidence bounds, (

**b**) Time series data of OLR nighttime with confidence bounds, (

**c**) The deviation of daytime OLR values from confidence bounds, (

**d**) Nighttime OLR deviation from the confidence bounds, (

**e**) Wavelet transformation of the daytime OLR time series data, (

**f**) Wavelet transformation analysis of nighttime OLR time series data. The white dashed line marks cone of influence and red dashed line shows the earthquake day. The * is for multiplication.

**Figure 3.**(

**a**) Time series data variations between the observed OLR daytime values and NARX-predicted OLR daytime values, (

**b**) Nighttime variations between the observed OLR values and NARX-predicted OLR, (

**c**) Deviation of NARX-predicted OLR daytime values from the observed daytime OLR values, (

**d**) Deviation of NARX-predicted OLR nighttime values from the observed nighttime OLR values, (

**e**) Time series data variations between the observed OLR daytime values and LSTM train OLR daytime values, (

**f**) Nighttime variations between observed OLR values and LSTM train OLR, (

**g**) Deviation of LSTM-predicted OLR daytime values from Observed daytime OLR values, (

**h**) Deviation of LSTM-predicted OLR nighttime values from Observed daytime OLR values. The red dashed line shows earthquake day.

**Figure 4.**(

**a**) Daytime RH averaged time series data with confidence bounds, (

**b**) Time series data of RH nighttime with confidence bounds, (

**c**) The deviation of daytime RH values from confidence bounds, (

**d**) Nighttime RH deviation from pre-defined bounds, (

**e**) Wavelet transformation of daytime RH data, (

**f**) Wavelet transformation analysis of nighttime RH time series data. The white dashed line in Figure 5e,f marks cone of influence and red dashed line shows the earthquake day. The * is for multiplication.

**Figure 5.**(

**a**) Data variations between the observed RH daytime values and NARX-predicted RH daytime values, (

**b**) Nighttime variations between the observed RH values and NARX-predicted RH, (

**c**) Deviation of NARX-predicted RH daytime values from Observed daytime RH values, (

**d**) Deviation of NARX-predicted RH nighttime values from Observed nighttime RH values, (

**e**) Time series data variations between observed RH daytime values and LSTM train RH daytime values, (

**f**) Nighttime variations between observed RH values and LSTM train RH, (

**g**) Deviation of LSTM-predicted RH daytime values from Observed daytime RH values, (

**h**) Deviation of LSTM-predicted RH nighttime values from Observed daytime RH values. The red dashed line shows the earthquake day.

**Figure 6.**(

**a**) Averaged time series data of AT with confidence bounds, (

**b**) The deviation of AT values from confidence bounds, (

**c**) Wavelet transformation of AT time series data. The white dashed line marks cone of influence and red dashed line shows the earthquake day. The * is for multiplication.

**Figure 7.**(

**a**) Variations between the observed AT data and NARX-predicted AT values, (

**b**) Deviation of NARX-predicted AT values from Observed AT values, (

**c**) Variations between observed AT values and LSTM train AT values, (

**d**) Deviation of LSTM-predicted AT values from Observed AT data. The red dashed line shows the earthquake day.

**Figure 8.**(

**a**) The observation of SST data within the bounds, (

**b**) SST data deviation from the bounds, (

**c**) Continuous wavelet analysis of MODIS-SST time series data. The white dashed line marks cone of influence and red dashed line shows the earthquake day. The * is for multiplication.

**Figure 9.**(

**a**) Data variations between observed time series MODIS-SST and NARX-predicted SST values, (

**b**) Deviation of NARX-predicted SST values from Observed SST values, (

**c**) Variations between observed MODIS-SST values and LSTM train SST values, (

**d**) Deviation of LSTM-predicted SST values from Observed SST data. The red dashed line shows earthquake day.

**Figure 10.**The solar and geomagnetic storm indices (

**a**) Kp and (

**b**) Dst of Japan earthquake, (

**c**) Statistical analysis of USUD (IGS station Japan) VTEC with IQR bounds, (

**d**) Analysis of MTKA (IGS station Japan) VTEC with pre-defined IQR bounds, (

**e**) Time series analysis of HYDE (IGS station India) VTEC with IQR bounds, (

**f**) Deviation of time series statistical analysis of VTEC for all available stations (USUD, MTKA, and HYDE). The red dashed line shows the earthquake day. The * is for multiplication.

**Figure 11.**(

**a**) Variation of USUD VTEC with NARX-predicted VTEC, (

**b**) Time series variation of MTKA VTEC with NARX-predicted VTEC, (

**c**) Variation of HYDE VTEC with NARX-predicted VTEC, (

**d**) Deviation of NARX-predicted VTEC from USUD, MTKA, and HYDE VTEC. The red dashed line shows the earthquake day.

**Figure 12.**(

**a**) Variation of USUD VTEC with LSTM train VTEC, (

**b**) Time series variation of MTKA VTEC with LSTM-predicted VTEC, (

**c**) Variation of HYDE VTEC with LSTM-predicted VTEC, (

**d**) Deviation of LSTM train VTEC from USUD, MTKA, and HYDE VTEC. The red dashed line shows earthquake day.

Parameters | Anomalous Day | Deviations from UB and LB | |
---|---|---|---|

Pre-EQ | Post-EQ | ||

OLR (Daytime) | −6, −5 | 36, 11.4 W/m^{2} | Nil |

OLR (Nighttime) | −6 | 18 W/m^{2} | Nil |

RH (Daytime) | −6 | −8% | Nil |

RH (Nighttime) | −5 | −6% | Nil |

AT | −5 | 2 °K | Nil |

SST | −6 | −4.5 °C | Nil |

VTEC (USUD) | −6, −5, 7, 9 | 3, 1.5 TECU | 7.5, 1.8 TECU |

VTEC (MTKA) | −6, −5, 7, 8, 9 | 2.32, 0.5 TECU | 6.6, 1.5, 6.7 TECU |

VTEC (HYDE) | 7, 8 | Nil | 5.95, 0.8 TECU |

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## Share and Cite

**MDPI and ACS Style**

Draz, M.U.; Shah, M.; Jamjareegulgarn, P.; Shahzad, R.; Hasan, A.M.; Ghamry, N.A.
Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake. *Remote Sens.* **2023**, *15*, 1904.
https://doi.org/10.3390/rs15071904

**AMA Style**

Draz MU, Shah M, Jamjareegulgarn P, Shahzad R, Hasan AM, Ghamry NA.
Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake. *Remote Sensing*. 2023; 15(7):1904.
https://doi.org/10.3390/rs15071904

**Chicago/Turabian Style**

Draz, Muhammad Umar, Munawar Shah, Punyawi Jamjareegulgarn, Rasim Shahzad, Ahmad M. Hasan, and Nivin A. Ghamry.
2023. "Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake" *Remote Sensing* 15, no. 7: 1904.
https://doi.org/10.3390/rs15071904