Possible Atmosphere and Ionospheric Anomalies of the 2019 Pakistan Earthquake Using Statistical and Machine Learning Procedures on MODIS LST, GPS TEC, and GIM TEC

Hafeez, Amna; Shah, Munawar; Ehsan, Muhsan; Jamjareegulgarn, Punyawi; Ahmed, Junaid; Tariq, M. Arslan; Iqbal, Shahid; Naqvi, Najam Abbas

doi:10.1109/jstars.2021.3119382

Cited by 18 publications

(7 citation statements)

References 32 publications

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“…These are better captured and exposed by the albedo, which is a sensitive index to the Earth's surface reflectance. Darker surfaces such as water and forests have a much lower albedo, and, consequently, more incident solar radiation is absorbed [67][68][69][70][71][72]. However, in our study, it was observed that the highest albedo values were directly related to exposed soil, while lower albedo values were directly related to the Amazon forest (Figure 3), as this is a more intense active agent in the absorption of water in Amazon biome.…”

Section: Discussionmentioning

confidence: 51%

Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation

et al. 2022

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Burns are common practices in Brazil and cause major fires, especially in the Legal Amazon. This study evaluated the dynamics of the fire foci in the Legal Amazon in Brazil and their consequences on environmental degradation, particularly in the transformation of the forest into pasture, in livestock and agriculture areas, mining activities and urbanization. The fire foci data were obtained from the reference satellites of the BDQueimadas of the CPTEC/INPE for the period June 1998–May 2022. The data obtained were subjected to descriptive and exploratory statistical analysis, followed by a comparison with the PRODES data during 2004–2021, the DETER data (2016–2019) and the ENSO phases during the ONI index for the study area. Biophysical parameters were used in the assessment of environmental degradation. The results showed that El Niño’s years of activity and the years of extreme droughts (2005, 2010 and 2015) stand out with respect to significant increase in fire foci. Moreover, the significant numbers of fire foci indices during August, September, October and November were recorded as 23.28%, 30.91%, 15.64% and 10.34%, respectively, and these were even more intensified by the El Niño episodes. Biophysical parameters maps showed the variability of the fire foci, mainly in the south and west part of the Amazon basin referring to the Arc of Deforestation. Similarly, the states of Mato Grosso, Pará and Amazonas had the highest alerts from PRODES and DETER, and in the case of DETER, primarily mining and deforestation (94.3%) increased the environmental degradation. The use of burns for agriculture and livestock, followed by mining and wood extraction, caused the degradation of the Amazon biome.

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Section: Discussionmentioning

confidence: 51%

Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation

et al. 2022

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“…In this study, multiple LAIC anomalies were confirmed using a nonlinear autoregressive with exogenous inputs (NARX) network for both the EQs. In the artificial neural network, NARX represented a nonlinear dynamic time series system [14,44]. Using the NARX network architecture for a time series, the independent input signal and output signal were regressed against the dependent output signal y (t) estimation.…”

Section: Anomaly Detection Using Neural Networkmentioning

confidence: 99%

“…These disturbances can be observed generally within 20 days before and/or 10 days after the main shock in the atmosphere, and this is followed by a significant variation within 5 days before and after in the ionosphere, which serve as EQ precursors. In the main, possible atmosphere and ionosphere anomalies can be observed in three regions from the satellite observations over the epicenter of the Earth's surface [12][13][14][15][16][17][18]. However, statistical analyses have showed that positive anomalies are more prominent than negative ones that have shallow hypocentral depth and large magnitude [19].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America

et al. 2023

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The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data.

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“…However, there is still no clear demarcation of seismic precursors with remotely sensed data to clearly depict the location and occurrence of an earthquake event on a specific day. The seismic-induced anomalies exhibit positive and negative deviations beyond the prescribed bounds in various data of the lithosphere, atmosphere, and ionosphere [14,15]. The lithosphere experiences numerous geophysical changes as the earthquake's energy first interacts with this layer, generating possible precursors around the epicentral region of the earthquake [16].…”

Section: Introductionmentioning

confidence: 98%

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

Haider,

Shah,

et al. 2024

Remote Sensing

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Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon.

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Possible Atmosphere and Ionospheric Anomalies of the 2019 Pakistan Earthquake Using Statistical and Machine Learning Procedures on MODIS LST, GPS TEC, and GIM TEC

Cited by 18 publications

References 32 publications

Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation

Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation

Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

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