Study of Land Surface Temperature Anomalies Associated to Earthquakes Using GOES Data

Semlali, B.E. Boudriki; Molina, Carlos; Park, H.; Camps, Adriano

doi:10.1109/igarss46834.2022.9884887

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…This study analyzed the LST anomalies for 1350 earthquakes that occurred in 2020 using time series data of ABI/GOES [47] and MODIS/AQUA. To gain a wider perspective, this period could be extended to a few years to enhance the quality of correlation and to cover global extents with more different land covers and regions.…”

Section: Discussionmentioning

confidence: 99%

First Results on the Systematic Search of Land Surface Temperature Anomalies as Earthquakes Precursors

et al. 2023

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Every year, earthquakes cause thousands of casualties and high economic losses. For example, in the time frame from 1998 to 2018, the total number of casualties due to earthquakes was larger than 846 thousand people, and the recorded economic losses were about USD 661 billion. At present, there are no earthquake precursors that can be used to trigger a warning. However, some studies have analyzed land surface temperature (LST) anomalies as a potential earthquake precursor. In this study, a large database of global LST data from the Geostationary Operational Environmental Satellite (GOES) and AQUA satellites during the whole year 2020 has been used to study the LST anomalies in the areas affected by earthquakes. A total of 1350 earthquakes with a magnitude larger than M4 were analyzed. Two methods widely used in the literature have been used to detect LST anomalies in the detrended LST time series: the interquartile (IQT) method and the standard deviation (STD). To the authors’ knowledge, it is the first time that the confusion matrix (CM), the receiver operating characteristic curve (ROC), and some other figures of merit (FoM) are used to assess and optimize the performance of the methods, and to select the optimum combination that could be used as a proxy for their occurrence. A positive anomaly was found a few days before the studied earthquakes, followed by the LST decrease after the event. Further studies over larger regions and more extended periods will be needed to consolidate these encouraging results.

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Section: Discussionmentioning

confidence: 99%

First Results on the Systematic Search of Land Surface Temperature Anomalies as Earthquakes Precursors

et al. 2023

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“…The test performance is assessed from six essential parameters used to compute the confusion matrix (CM): condition positive (CP), condition negative (CN), true positives (TP), false negatives (FN), false positives (FP), and true negatives (TN) [26]. The explanation for each of them is as follows:…”

Section: = Deviation Meanmentioning

confidence: 99%

“…Additional metrics can be derived from them, such as the specificity, the precision, the recall, and the accuracy. Table 1 summarizes the different formulas used to compute these parameters, including the true positive rate (TPR), the false positive rate (FPR), the false negative rate (FNR), the true negative rate (TNR), the likelihood ratio (LR), the positive predictive value (PPV), the false omission rate (FOR), the negative predictive value, (NPV), the false discovery rate (FDR), the area under the curve (AUC), and the diagnostic odds ratio (DOR) [26].…”

Section: = Deviation Meanmentioning

confidence: 99%

Potential Earthquake Proxies from Remote Sensing Data

Boudriki Semlali,

Molina,

Carvajal Librado

et al. 2024

New Insights on Disaster Risk Reduction [Working Title]

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At present, there is no clear scientific evidence of reliable earthquake precursors that can be used as an early warning system. However, many studies have also reported the existence of faint signatures that appear to be coupled to the occurrence of earthquakes. These anomalies have traditionally been detected using data from in-situ sensors near high-seismicity regions. On the other hand, remote sensors offer the potential of large spatial coverage and frequent revisit time, allowing the observation of remote areas such as deserts, mountains, polar caps, or the ocean. This chapter revises the state-of-the-art of the understanding of lithosphere–atmosphere–ionosphere coupling. It also presents recent studies by the authors’ ongoing investigation on short-to-midterm earthquake precursors. The Earth observation variables discussed are (1) surface temperature anomalies from thermal infrared or microwave radiometer measurements, (2) atmospheric signatures, (3) ionospheric total electron density fluctuations or scintillation measured from GNSS signals, and (4) other geophysical variables, including geomagnetic field fluctuations, changes in the Schumann resonance frequency, or low-frequency electromagnetic radiation. However, despite the seismic hazard risk models that exist and the results shown by these studies, it is still very difficult to predict the occurrence of earthquakes.

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“…Many other studies correlated earthquake occurrences with other variables, such as the Land Surface Temperature (LST) [15]- [17], the Surface Latent Heat Flux (SLHF) [18], the Outgoing Longwave Radiation (OLR) [19], the geomagnetic and electric fields [19]- [21], and the Schumann resonance [22], [23].…”

Section: Introductionmentioning

confidence: 99%

On the Correlation Between Earthquakes and Prior Ionospheric Scintillations Over the Ocean: A Study Using GNSS-R Data Between 2017 and 2021

Boudriki Semlali,

Molina,

Park

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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From 1980 to 2021, earthquakes have caused more than 846,000 casualties and about US$ 661 billion in economic losses. At present, there are no reliable earthquake precursors to generate alerts. Currently, the link between earthquakes and Total Electron Content (TEC) variations measured by Global Navigation Satellite Systems (GNSS) monitoring ground stations has been studied. However, GNSS ground-based monitoring stations are irregularly disseminated around the globe with significant gaps, particularly in the ocean's regions. In this study, we analyze ionospheric intensity scintillation anomalies as potential proxies of earthquakes. NASA CYGNSS GNSS-R (Reflectometry) acquired by CYGNSS/DDMI (Delay Doppler Mapping Instrument) from 2017 to 2021 have been used to calculate and analyze the anomalies in the S4 parameter over ocean areas affected by earthquakes. More than 30,000 ocean earthquakes within ±40° in latitude and with a magnitude larger than M4 have been examined.The daily planetary geomagnetic index Kp was used to discard data that may be disturbed due to space weather conditions. Additionally, a daily sea wind speed mask was used to eliminate sea states that impact the signal reflectivity. The Standard Deviation (STD) and the Interquartile (IQT) time series methods have been used to detect these S4 anomalies. The confusion matrix, the Receiver Operating Curve (ROC), and some other Figures of Merit (FoM) have been used for the first time to evaluate and improve the performance of the prediction parameters and identify the optimum configuration to be used as a potential proxy of earthquake occurrence. As a result, a small, but detectable positive S4 anomaly was detected between 1 and 7 days before the earthquakes under study.

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Study of Land Surface Temperature Anomalies Associated to Earthquakes Using GOES Data

Cited by 6 publications

References 14 publications

First Results on the Systematic Search of Land Surface Temperature Anomalies as Earthquakes Precursors

First Results on the Systematic Search of Land Surface Temperature Anomalies as Earthquakes Precursors

Potential Earthquake Proxies from Remote Sensing Data

On the Correlation Between Earthquakes and Prior Ionospheric Scintillations Over the Ocean: A Study Using GNSS-R Data Between 2017 and 2021

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