scholarly journals MINING SEISMIC THERMAL ANOMALIES FROM MASSIVE SATELLITE PASSIVE MICROWAVE IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 281-286
Author(s):  
Y. Qi ◽  
L. X. Wu ◽  
Y. F. Ding ◽  
M. He ◽  
W. F. Mao ◽  
...  
Keyword(s):  
Ground Surface ◽  
Thermal Anomaly ◽  
Passive Microwave ◽  
Step Method ◽  
Thermal Anomalies ◽  
Microwave Brightness Temperature ◽  
Microwave Dielectric ◽  
Evolution Characteristics ◽  
Large Earthquakes ◽  
Insight Into

Abstract. Satellite passive microwave radiative signals are considered to reflect thermal radiation and energy exchange of the Earth’s surface, and the microwave brightness temperature (MBT) has been preliminary adopted for pre-earthquake thermal anomaly monitoring in recent decades. Based on the spatio-temporally weighted two-step method (STW-TSM), this paper aims to uncover the evolution characteristics of MBT anomaly prior to typical earthquakes (EQs), i.e. the Mw7.9 Wenchuan EQ in May 2008, the Nepal EQs in April and May 2015, and the Mw5.8 Yibin EQ in June 2019, and to explore and recognize their differences and commonalities. The results are summarized as: 1) significant MBT positive anomalies appeared east and southwest close to the epicenter before the Wenchuan EQ, and the east anomaly migrated northeastward along Longmenshan faults with aftershocks, then the two anomalies dissipated subsequently with the ceasing of large aftershocks (Mw > 5.5). 2) the MBT positive anomalies of Nepal EQs firstly appeared along the Himalayas and became most obvious 1 day before the main shock, and dissipated subsequently after the first shock, and that of the second shock behaved in the same spatiotemporal patten. 3) regional positive MBT anomalies appeared around the epicenter a half month before the Yibin EQ and diminished over time, and the most obvious abnormal area transferred from the central and northwest to the southwest study area. It exhibited that MBT positive anomalies prefer to appear at the mechanically relaxed zones, such as the loose Quaternary with Wenchuan EQ, the cliff peaks with Nepal EQs, and the mountains surrounding Yibin EQ’s epicenter, which can be attributed to the declining of ground surface microwave dielectric caused by stress activated P-holes during the period of seismogenic preparation. This research provides a novel insight into mining MBT anomalies associate with large earthquakes and a possibility to explore the potential mechanism of such abnormal phenomena.

scholarly journals Discriminating the Multi-Frequency Microwave Brightness Temperature Anomalies Relating to 2017 Mw 7.3 Sarpol Zahab (Iran-Iraq Border) Earthquake

2021 ◽  
Vol 9 ◽  
Author(s):  
Yifan Ding ◽  
Yuan Qi ◽  
Lixin Wu ◽  
Wenfei Mao ◽  
Yingjia Liu
Keyword(s):  
Brightness Temperature ◽  
Low Frequency ◽  
Satellite Orbit ◽  
Urmia Lake ◽  
Arabian Plate ◽  
Positive Anomaly ◽  
Eurasian Plate ◽  
Step Method ◽  
Microwave Brightness Temperature ◽  
Microwave Dielectric

A Mw 7.3 earthquake occurred near the Iran-Iraq border on November 12, 2017, as the result of oblique-thrust squeezing of the Eurasian plate and the Arabian plate. By employing the spatio-temporally weighted two-step method (STW-TSM) and microwave brightness temperature (MBT) data from AMSR-2 instrument on board Aqua satellite, this paper investigates carefully the spatiotemporal features of multi-frequency MBT anomalies relating to the earthquake. Soil moisture (SM), satellite cloud image, regional geological map and surface landcover data are utilized to discriminate the potential MBT anomalies revealed from STW-TSM. The low-frequency MBT residual images shows that positive anomalies mainly occurred in the mountainous Urmia lake and the plain region, which were 300 km north and 200 km southwest about to the epicenter, respectively. The north MBT anomaly firstly appeared 51 days before the mainshock and its magnitude increased over time with a maximum of about +40K. Then the anomaly disappeared 3 days before, reappeared 1d after and diminished completely 10 days after the mainshock. Meanwhile, the southwest MBT anomaly firstly occurred 18 days before and peaked 3 days before the mainshock with a maximum of about +20K, and then diminished gradually with aftershocks. It is speculated that the positive MBT anomaly in the Urmia lake was caused by microwave dielectric property change of water body due to gas bubbles leaking from the bottom of the lake disturbed by local crust stress alteration, while the southwest MBT positive anomaly was caused by microwave dielectric constant change of shallow surface due to accumulation of seismically-activated positive charges originated at deep crust. Besides, some accidental abnormal residual stripes existed in line with satellite orbit, which turned out to be periodic data errors of the satellite sensor. High-frequency MBT residual images exhibit some significant negative anomalies, including a narrow stripe pointing to the forthcoming epicenter, which were confirmed to be caused by synchronous altostratus clouds. This study is of guidance meaning for distinguishing non-seismic disturbances and identifying seismic MBT anomaly before, during and after some large earthquakes.

Thermoelectric self-potential and resistivity data localize the burning front of underground coal fires

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. B259-B273 ◽  
Author(s):  
A. Revil ◽  
M. Karaoulis ◽  
S. Srivastava ◽  
S. Byrdina
Keyword(s):  
Coal Seam ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Ground Surface ◽  
Thermal Anomaly ◽  
Silica Sand ◽  
Geophysical Field ◽  
Burning Front ◽  
Resistivity Data ◽  
Self Potential

Self-potential signals and resistivity data can be jointly inverted or analyzed to track the position of the burning front of an underground coal-seam fire. We first investigate the magnitude of the thermoelectric coupling associated with the presence of a thermal anomaly (thermoelectric current associated with a thermal gradient). A sandbox experiment is developed and modeled to show that in presence of a heat source, a negative self-potential anomaly is expected at the ground surface. The expected sensitivity coefficient is typically on the order of [Formula: see text] in a silica sand saturated by demineralized water. Geophysical field measurements gathered at Marshall (near Boulder, CO) show clearly the position of the burning front in the electrical resistivity tomogram and in the self-potential data gathered at the ground surface with a negative self-potential anomaly of about [Formula: see text]. To localize more accurately the position of the burning front, we developed a strategy based on two steps: (1) We first jointly invert resistivity and self-potential data using a cross-gradient approach, and (2) a joint interpretation of the resistivity and self-potential data is made using a normalized burning front index (NBI). The value of the NBI ranges from 0 to 1 with 1 indicating a high probability to find the burning front (strictly speaking, the NBI is, however, not a probably density). We validate first this strategy using synthetic data and then we apply it to the field data. A clear source is localized at the expected position of the burning front of the coal-seam fire. The NBI determined from the joint inversion is only slightly better than the value determined from independent inversion of the two geophysical data sets.

EnKF estimation of the viscoelastic deformation and the viscosity

2020 ◽  
Author(s):  
Makiko Ohtani
Keyword(s):  
Data Assimilation ◽  
Crustal Deformation ◽  
Surface Deformation ◽  
Synthetic Data ◽  
Ground Surface ◽  
Inelastic Strain ◽  
Large Earthquake ◽  
Forward Model ◽  
Viscoelastic Deformation ◽  
Large Earthquakes

<p>Following large earthquakes, postseismic crustal deformations are often observed for more than years. They include the afterslip and the viscoelastic deformation of the crust and the upper mantle, activated by the coseismic stress change. The viscoelastic deformation gives the stress change on the neighboring faults, hence affects the seismic activity of the surrounding area, for a long period after the large earthquake. So, estimating the viscoelastic deformation after the large earthquakes is important.</p><p>In order to estimate the time evolution of the viscoelastic deformation after a large earthquake, we also need to know the viscoelastic structure around the area. Recently, the Ensemble Kalman filter method (EnKF), a sequential data assimilation method, starts to be used for the crustal deformation data to estimate the physical variables (van Dinther et al., 2019, Hirahara and Nishikiori, 2019). With data assimilation, we get a more provable estimation by combining the data and the time-forward model than only using the data. Hirahara and Nishikiori (2019) used synthetic data and showed that EnKF could effectively estimate the frictional parameters on the SSE (slow slip event) fault, addition to the slip velocity. In the present study, I applied EnKF to estimate the viscosity and the inelastic strain after a large earthquake, both the physical property and the variables.</p><p>First, I constructed the forward model simulating the evolution of the viscoelastic deformation, following the equivalent body force method (Barbot and Fialko, 2010; Barbot et al., 2017). This method is appropriate for applying EnKF, because the ground surface deformation rate is represented by the inelastic strain at the moment, and the history of the strain is not required. Then, we applied EnKF based on the forward model and executed some numerical experiments using a synthetic postseismic crustal deformation data.</p><p>In this presentation, I show the result of a simple setting. I assumed the medium to be two layers with a homogeneous viscoelastic region underlying an elastic region. The synthetic data is made by giving a slip on a fault at time <em>t</em> = 0 and simulating the time evolution of the ground surface deformation. For assimilation, I assumed that the slip on the fault and the stress distribution just after the large earthquake is known. Then we executed the assimilation every 30 days after the large earthquake. I found that I can get a good estimation of the viscosity after <em>t</em> > 150 days.</p>

scholarly journals The observed thermal anomaly as an earthquake Precursor: A case study from the 1993 Latur earthquake prone area in Western India

2021 ◽  
Author(s):  
Vijay P Dimri ◽  
Simanchal Padhy ◽  
N C Mondal ◽  
G K Reddy ◽  
G G. Ramacharyulu ◽  
...  
Keyword(s):  
Continuous Monitoring ◽  
Seismic Hazard Assessment ◽  
Thermal Anomaly ◽  
Atmospheric Temperature ◽  
Research Area ◽  
Earthquake Precursor ◽  
Western India ◽  
Monsoon Period ◽  
Prone Area ◽  
Insight Into

Abstract We report and discuss monitoring of short-term variations of widely used multi-geophysical parameters in Latur-Killari area in western India, the region that witnessed a major devastating earthquake in 1993. An abnormal rise in atmospheric temperature of more than 20°C at 11200 m height was observed in the air-flight just 100 km away from Latur during a monsoon period. We investigated the cause of such abnormal rise in temperature in relation to the seismicity of the area for the 1993 Latur earthquake along with the continuous monitoring of ground water level and soil Helium gas for a week under a precursory 'quick please' operation in the study area. There were no seismic signals associated with this precursor rise that led to the suspension of the operation after a week time. It is also observed that this thermal anomaly is not followed by any major earthquake over the area, which has larger implications in atmosphere research area, suggesting a detailed investigation of such anomaly that may provide a better insight into the precursory behavior of the observed thermal anomaly by overcoming the constraints of accurate retrieval of temperature due to inadequate penetration of Satellite based thermal sensor into thick clouds. Findings of this study certainly call for continuous monitoring of temperature over the earthquake prone areas to gain insight into the physics of short-lived variation in temperature over spatially limited extent, especially over the earthquake prone areas for improved seismic hazard assessment.

scholarly journals Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

2020 ◽  
Author(s):  
Xiongxin Xiao ◽  
Shunlin Liang ◽  
Tao He ◽  
Daiqiang Wu ◽  
Congyuan Pei ◽  
...  
Keyword(s):  
North America ◽  
Land Cover ◽  
Brightness Temperature ◽  
Snow Cover ◽  
Temperature Data ◽  
Passive Microwave ◽  
Retrieval Models ◽  
Snow Cover Area ◽  
Microwave Brightness Temperature ◽  
Fractional Snow Cover

Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, climate system, and ecosystem function. Although optical satellite remote sensing has proved to be an effective tool for monitoring global and regional variations of snow cover, it is still problematic to accurately capture the snow dynamics characteristics at a finer spatiotemporal resolution, because the observations from optical satellite sensors are seriously affected by clouds and solar illumination. Besides, traditional methods of mapping snow cover from passive microwave data only provide binary information with a 25-km spatial resolution. In this study, we first present an approach to predict fractional snow cover over North America under all-weather conditions, derived from the enhanced resolution passive microwave brightness temperature data (6.25 km). This estimation algorithm used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2010 and 2017 to create the reference fractional snow cover data as the "true" observations. Further, the influence of many factors, including land cover, topography, and location, were incorporated into the retrieval models. The results show that the proposed retrieval models based on random forest regression technique perform much better using independent test data for all land cover classes, with higher accuracy and no out-of-range estimated values, when compared to the other three approaches (linear regression, artificial neural networks (ANN), and multivariate adaptive regression splines (MARS)). The results of the output evaluated by using independent data indicate that the root-mean-square error (RMSE) of the estimated fractional snow cover ranges from 16.7 % to 19.8 %. In addition, the estimated fractional snow cover is verified in the snow mapping aspect by using snow cover observation data from meteorological stations (more than 0.31 million records). The result shows that the binary snow cover obtained by the proposed retrieval algorithm is in a good agreement with the ground measurements (kappa: 0.67). The accuracy of our algorithm estimation in the snow cover identification shows significant improvement when benchmarked against the Grody’s snow cover mapping algorithm: overall accuracy is increased by 18 % (from 0.71 to 0.84), and omission error is reduced by 71 % (from 0.48 to 0.14). Daily time-series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. According to our experiment results, we can conclude that it is feasible for estimating fractional snow cover from passive microwave brightness temperature data, and this strategy also has a great advantage in detecting snow cover area.

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