# Seismo Ionospheric Anomalies around and over the Epicenters of Pakistan Earthquakes

^{1}

^{2}

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## Abstract

**:**

_{w}7.7 Awaran, where TEC anomalies can be clearly seen within 5–10 days before the seismic day and the subsequent rise in TEC during the 2 days after the main shock. These variations are also evident in GIM maps over the Awaran EQ epicenter. The findings point towards a large emission of EQ energy before and after the main shock during quiet storm days, which aid in the development of lithosphere ionosphere coupling. However, the entire analysis can be expanded to more satellite and ground-based measurements in Pakistan and other countries to reveal the pattern of air ionization from the epicenter through the atmosphere to the ionosphere.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Materials

_{w}6.8 on 28 September 2013. The affected areas were Awaran, Tirtej, Gashkore, Nok Jo, Parwar, Dandar, and Hoshab. The EQ occurred due to an oblique-strike slip type motion. The tremor occurred in all Pakistan and its neighboring countries: Iran, Afghanistan, and India. A cumulative moment of 5.4 × 10

^{20}Nm was released by this rupture.

_{1}and f

_{2}, the pseudo-range is denoted as L, the delay path of the signal of the carrier phase observations is P, the signal wavelength is λ, and the ray path uncertainty is N. Here, d and b denote the biases of the consequent signal pseudo-range and instrumental carrier phase, and ϵ is the random error in the signal. The STEC is converted to VTEC using the following equation [8].

#### 2.2. Methodology

_{j}·w

_{ij}is produced by multiplying a strength x

_{j}by a weight w

_{ij}. A neural network’s output is represented by the following: y

_{i}= f(x

_{j}w

_{ij}), where i and j are indexes of a neuron in the hidden layer and input, respectively [38].

_{1}, x

_{2}, …, x

_{N}as the training data, and the remaining observations x

_{N+1}, x

_{N+2}, …, x

_{N+m}as the test data. The time series underlying pattern is discovered using lagged observations that correspond to the input nodes (p). The network capability of the prediction or learning is highly affected by the various input nodes [38]. For our analysis, the number of nodes for the input, hidden, and output layers was three, two, and one, respectively. Specifically, each training set contains four observations that constitute one pattern vector, of which three are inputs and one is the output value.

_{N}= f(X

_{N−3,}X

_{N−2,}X

_{N−1}); N = 1, 2, 3….

_{N+m}= f (X

_{N+m−3}, X

_{N+m−2}, X

_{N+m−1})

_{1}, y

_{2}, …, y

_{N}and y

_{N+1}, y

_{N+2}, …, y

_{N+m}are selected as training and test sets, respectively. We can identify the underlying pattern of a time series by counting the lagged observations corresponding to input nodes. Neurons receive observed values from a variety of sources and feed them into the input layer. In this study, the corrected LST values, their accompanying times, and the deviation from the mean LST values are inputs. All neurons in the hidden layer are affected by the weights collected from the previous input layer. An effective combination of hidden neurons engages the transfer function.

_{N}= f(y

_{N−3}, y

_{N−2}, y

_{N−1}, t

_{N−3}, t

_{N−2}, t

_{N−1})

## 3. Results

#### 3.1. Case Study I

#### 3.2. CASE Study II

_{i}) between the actual value (X

_{i}) and the predicted value $(\widehat{{X}_{i}})$ outside the defined bounds.

## 4. Discussion

^{-}in the matrix of O

^{2-}within the rock columns of the sublattice of oxygen. As soon as these p-holes reach the Earth’s surface (lithosphere), they accumulate over the lithosphere at the ground–air interface at different topographic highs to produce electric fields. Furthermore, these electric fields over the epicentral regions are microscopic in nature, but they have the ability to reach and spread around millions of volts per centimeter. This abrupt electric field causes air ionization, mostly O

^{2}and many other molecules over the lithosphere in the nearby air. Over the EQ preparation region, the model of global electric current propagation, Earth electric charge carriers, and ionization of air raised to high altitude by the explanation of Freund et al. [48]. The high altitude of the seismic regions further intensifies the electric field due to its intrinsic nature [49], and the case studies in this paper also occurred in high and heterogeneous regions. Previous reports on various atmosphere and ionosphere precursors also highlighted the variations at different altitudes from satellite observation [50,51]. After the loss of an electron from O

^{2}to the ground state, it came with O

^{2+}as positive ions in the atmosphere. The air with more and more O

^{2+}in the seismic preparation period rose further upward as a result of the condensation of moist particles and latent heat release. The previously discussed moisture condensation further raises anomalous latent heat, and the air ionization causes an increase in air velocity and flow towards the upper atmosphere and ionosphere [52]. The enhancement in air buoyancy generates thermal up gradation, which aids in the transportation of ionized air to high altitudes over the EQ epicenters and culminates in the form of ionospheric anomalies.

## 5. Conclusions

- Ionospheric anomalies occurred before the Mirpur EQ as pre-SIA within 5 days before the main shock, and variations on the Awaran EQ day occurred as a seismic response on the main shock day. Moreover, the intensity of the VTEC anomaly for the Awaran EQ was higher than the Mirpur event due to the magnitude difference.
- The differential GIM maps showed no clear electron cloud over the epicenter of the Mirpur EQ due to the low magnitude of the event. On the other hand, the Awaran EQ induced significant TEC clouds over the epicenter during LT~10–12 h.
- The most apparent anomalies occurred before the Mirpur EQ and on the EQ day of the Awaran event as a pre-SIA and seismic response, respectively. Similarly, no clear post-EQ anomalies occurred in the case of both EQs. These results suggest that abrupt seismic variations triggered by the EQs appeared in the form of the emanation of energy from the EQ-prone region to the ionosphere in the seismic preparation period. In conformity with previous studies and our conclusions, we believe that the seismo-ionospheric and thermal anomalies can positively contribute to the prediction of EQs.
- Machine learning can only assist in enlarging the peak and variations of the existing abnormal VTEC values and can no longer help in finding new VTEC precursors.
- The geomagnetic indices also observed no effect of storms for the same observation period before and after the EQs. Thus, we can conclude that the observed TEC anomalies were triggered by the seismic events.
- The GIMs provide a clear picture of authentic ionosphere anomalies in the case of the Mirpur EQ. They show that low-magnitude EQs cannot propagate energy to ionospheric heights. However, we can monitor the response of TEC to seismic events by integrating observations based on the worldwide impact of such natural phenomena, and it might be utilized as a useful tool to forecast possible seismic activity. Ionosphere-seismic studies are a continuing process and one of the primary drivers of ionosphere variability, and such consequences can be noticed at least a few days before an EQ.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Study area map with red stars showing the location of the Awaran EQ (24 September 2013) epicenter and the Mirpur EQ (24 September 2013), while orange lines represent fault lines of Pakistan and green lines represent Pakistan regional boundaries.

**Figure 3.**Spatial variations of TEC from GIMs over the Mirpur EQ epicenter on 22 September 2019 (2 days before the main shock).

**Figure 4.**(

**a**) Comparison between GPS VTEC and NARX-predicted VTEC of Mirpur EQ. (

**b**) Values exceeding the bounds depicting the anomalous behavior. The ‘*’ is for multiplication.

**Figure 5.**(

**a**) Comparison between GPS VTEC and MLP-predicted VTEC of the Mirpur EQ. (

**b**) Values exceeding the bounds depicting the anomalous behavior. The ‘*’ is for multiplication.

**Figure 7.**Spatial variations of TEC from GIMs over the Awaran EQ epicenter on 24 September 2013 (1 day after the main shock).

**Figure 8.**(

**a**) Comparison between GPS VTEC and NARX-predicted VTEC of Awaran EQ. (

**b**) Values exceeding the bounds depicting the anomalous behavior. The ‘*’ is for multiplication.

**Figure 9.**(

**a**) Comparison between GPS VTEC and MLP-predicted VTEC of Awaran EQ. (

**b**) Values exceeding the bounds depicting the anomalous behavior.

**Figure 10.**Geomagnetic indices including Kp, Dst F10.7, and AE around the main shock day, where (

**a**–

**d**) represent Mirpur EQ and (

**a’**–

**d’**) for Awaran EQ.

Method | Pre-EQ Anomalies | Post-EQ Anomalies | ||||||
---|---|---|---|---|---|---|---|---|

Mirpur | Awaran | Mirpur | Awaran | |||||

Day | Deviation | Day | Deviation | Day | Deviation | Day | Deviation | |

IQR | −2 | 2 TECU | EQ day | 4 TECU | Nil | Nil | Nil | Nil |

NARX | −5, −2 | 4 TECU | EQ day | 5 TECU | 1–2 | 2 TECU | Nil | Nil |

MLP | −2 | 3.5 TECU | EQ day | 7 TECU | Nil | Nil | 1 & 3 | 3 TECU |

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## Share and Cite

**MDPI and ACS Style**

Shah, M.; Shahzad, R.; Ehsan, M.; Ghaffar, B.; Ullah, I.; Jamjareegulgarn, P.; Hassan, A.M.
Seismo Ionospheric Anomalies around and over the Epicenters of Pakistan Earthquakes. *Atmosphere* **2023**, *14*, 601.
https://doi.org/10.3390/atmos14030601

**AMA Style**

Shah M, Shahzad R, Ehsan M, Ghaffar B, Ullah I, Jamjareegulgarn P, Hassan AM.
Seismo Ionospheric Anomalies around and over the Epicenters of Pakistan Earthquakes. *Atmosphere*. 2023; 14(3):601.
https://doi.org/10.3390/atmos14030601

**Chicago/Turabian Style**

Shah, Munawar, Rasim Shahzad, Muhsan Ehsan, Bushra Ghaffar, Irfan Ullah, Punyawi Jamjareegulgarn, and Ahmed M. Hassan.
2023. "Seismo Ionospheric Anomalies around and over the Epicenters of Pakistan Earthquakes" *Atmosphere* 14, no. 3: 601.
https://doi.org/10.3390/atmos14030601