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Possible atmospheric-ionospheric precursors of the 2020 Hotan China earthquake from various satellites
(2024-10-01)
Hameed, Amna
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Shah, Munawar
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Ghaffar, Bushra
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Riaz, Salma
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Jamjareegulgarn, Punyawi
The earthquake (EQ) precursors from satellites data portray an image of the energy propagation from the lithosphere to atmosphere and then to the ionosphere. Previous studies have often presented detailed discussion on different precursors at various altitudes. However, this study aimed to investigate the anomalies at various altitudes associated with the Hotan China EQ (hypocentral depth: 10 km, latitude 35.5°N, longitude 82.4°E). The goal was to identify pre-and post-seismic anomalies statistically in the conjunction with the wavelet transformation. We observed possible precursors in the atmosphere such as variations in aerosol optical depth, tropopause pressure, relative humidity, latent heat flux, and outgoing longwave radiation in a window of 5–10 days before the seismic event. Moreover, the total electron content had precursors during quiet geomagnetic storm conditions (−20 < Dst ≤ − 40 nT, Kp ≤ 3) beyond the bound within 5–10 days. These findings highlight the potential of using atmospheric and ionospheric parameters to detect seismic anomalies as EQ precursors for improved EQ early warning systems.
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Performance of multi-source remote sensing soil moisture products over Punjab Pakistan during 2022–2023
(2024-08-01)
Hassan, Saba ul
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Shah, Munawar
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Shahzad, Rasim
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Ghaffar, Bushra
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Li, Bofeng
The Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a valuable tool for terrestrial remote sensing applications, particularly in the context of land Surface Soil Moisture (SSM) detection. The high-resolution capability of GNSS-R complements traditional satellite-based active and passive missions but the product reliability and robustness evaluations are still absent due to an efficient retrieval algorithms. In this study, we addressed this lack of reliability and robustness by comprehensively assessing the SSM retrievals from CYclone Global Navigation Satellite System (CYGNSS) data with the satellite-based microwave radiometry products Soil Moisture Active Passive (SMAP) and Modern Era Retrospective-Analysis for Research and Applications (MERRA2) over Punjab in various seasons. ERA5 model-based products for the same period in 2022–2023. Our study reveals a distinct seasonal average SSM variation during autumn (0.20 cm3/cm3), followed by winter values of 0.19 cm3/cm3. Subsequently, the minimum SSM values are observed during summer (0.11 cm3/cm3) and an increase in spring to 0.13 cm3/cm3. Moreover, a strong positive linear relationship (0.74) is evident between SMAP and ERRA 5 in contrast to a low correlation (0.03) between MERRA2 and both the SMAP and ERRA 5. Additionally, SMAP demonstrates moderate and weak correlation of 0.53 and 0.03 with CYGNSS and MERRA2, respectively. The CYGNSS exhibits moderate correlations (0.46) with ERRA 5 and SMAP and a weaker association (0.14) with MERRA2. Our analysis concluded that MERRA2 (Bias = 0.20 cm³/cm³, ubRMSD = 0.25 cm³/cm³, RMSE = 0.12 cm³/cm³, SD = 0.13 cm³/cm³, MAE = 0.04 cm³/cm, R = 0.03) SSM product performs poorly as compared to SMAP (Bias = 0.03 cm³/cm³, ubRMSD = 0.03 cm³/cm³, RMSE = 0.04 cm³/cm³, SD = 0.05 cm³/cm³, MAE = 0.03 cm³/cm³, R = 0.74) and CYGNSS (Bias = -0.01 cm³/cm³, ubRMSD = 0.09 cm³/cm³, RMSE = 0.07 cm³/cm³, SD = 0.06 cm³/cm³, MAE = 0.05 cm³/cm³, R = 0.46) products. This study provides accurate future predictions of SSM with delineating the limitations of GNSS-R in comparison to remote sensing and model values. The findings from this study have also significant implications for the advancement of GNSS-R applications in agriculture and crop management.
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Atmospheric precursors associated with two Mw > 6.0 earthquakes using machine learning methods
(2024-06-01)
Khalid, Zaid
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Shah, Munawar
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Riaz, Salma
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Ghaffar, Bushra
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Jamjareegulgarn, Punyawi
The advancements in remote sensing (RS) satellite applications have revolutionized natural disaster surveillance and prediction in the earthquake monitoring by delineating various precursors at the Earth’s surface and in atmosphere. In this paper, the earthquake precursors comprising land surface temperature, outgoing longwave radiations, relative humidity, and air temperature for both the daytime and nighttime are investigated for two Mw > 6.0 events in USA. Interestingly, we noticed surface and atmospheric parameters anomalies in 6–8 days window prior to both the events by using standard deviation method. Moreover, these abrupt deviations are also validated by the recurrent neural networks like autoregressive network with exogenous inputs and long short-term memory inputs. The findings of this study demonstrate the potential of using modern analysis tools to further develop our knowledge of the linked dynamics of the lithosphere and atmosphere preceding seismic occurrences. This study implements substantially the developing of natural hazard surveillance and earthquake prediction capabilities for future researches as a valuable addition of reference in the field of RS.
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Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America
(2023-08-01)
Shah, Munawar
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Shahzad, Rasim
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Jamjareegulgarn, Punyawi
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Ghaffar, Bushra
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Oliveira-Júnior, José Francisco de
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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Seismo Ionospheric Anomalies around and over the Epicenters of Pakistan Earthquakes
(2023-03-01)
Shah, Munawar
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Shahzad, Rasim
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Ehsan, Muhsan
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Ghaffar, Bushra
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Ullah, Irfan
Global Navigation Satellite System (GNSS)-based ionospheric anomalies are nowadays used to identify a possible earthquake (EQ) precursor and hence a new research topic in seismic studies. The current study also aims to provide an investigation of ionospheric anomalies associated to EQs. In order to study possible pre-and post-seismic perturbations during the preparation phase of large-magnitude EQs, statistical and machine learning algorithms are applied to Total Electron Content (TEC) from the Global Positioning System (GPS) and Global Ionosphere Maps (GIMs). We observed TEC perturbation from the Sukkur (27.8° N, 68.9° E) GNSS station near the epicenter of Mw 5.4 Mirpur EQ within 5–10 days before the main shock day by implementing machine learning and statistical analysis. However, no TEC anomaly occurred in GIM-TEC over the Mirpur EQ epicenter. Furthermore, machine learning and statistical techniques are also implemented on GIM TEC data before and after the M<inf>w</inf> 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.
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