Preprints
https://doi.org/10.5194/nhess-2020-391
https://doi.org/10.5194/nhess-2020-391

  17 Dec 2020

17 Dec 2020

Review status: a revised version of this preprint is currently under review for the journal NHESS.

Fault distance-based approach in thermal anomaly detection before strong Earthquakes

Arash Karimi Zarchi and Mohammad Reza Saradjian Maralan Arash Karimi Zarchi and Mohammad Reza Saradjian Maralan
  • Remote Sensing Department, School of Surveying and Geospatial Engineering, Collage of Engineering, University of Tehran, Tehran, Iran

Abstract. The recent scientific studies in the context of earthquake precursors reveal some processes connected to seismic activity including thermal anomaly before earthquakes which is a great help for making a better decision regarding this disastrous phenomenon and reducing its casualty to a minimum. This paper represents a method for grouping the proper input data for different thermal anomaly detection methods using the land surface temperature (LST) mean in multiple distances from the corresponding fault during the 40 days (i.e. 30 days before and 10 days after impending earthquake) of investigation. Six strong earthquakes with Ms > 6 that have occurred in Iran have been investigated in this study. We used two different approaches for detecting thermal anomalies. They are mean-standard deviation method also known as standard method and interquartile method which is similar to the first method but uses different parameters as input. Most of the studies have considered thermal anomalies around the known epicentre locations where the investigation can only be performed after the earthquake. This study is using fault distance-based approach in predicting the earthquake regarding the location of the faults as the potential area. This could be considered as an important step towards actual prediction of earthquake’s time and intensity. Results show that the proposed input data produces less false alarms in each of the thermal anomaly detection methods compared to the ordinary input data making this method much more accurate and stable considering the easy accessibility of thermal data and their less complicated algorithms for processing. In the final step, the detected anomalies are used for estimating earthquake intensity using Artificial Neural Network (ANN). The results show that estimated intensities of most earthquakes are very close to the actual intensities. Since the location of the active faults are known a priori, using fault distance-based approach may be regarded as a superior method in predicting the impending earthquakes for vulnerable faults. In spite of the previous investigations that the studies were only possible aftermath, the fault distance-based approach can be used as a tool for future unknown earthquakes prediction. However, it is recommended to use thermal anomaly detection as an initial process to be jointly used with other precursors to reduce the number of investigations that require more complicated algorithms and data processing.

Arash Karimi Zarchi and Mohammad Reza Saradjian Maralan

 
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Arash Karimi Zarchi and Mohammad Reza Saradjian Maralan

Arash Karimi Zarchi and Mohammad Reza Saradjian Maralan

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Short summary
This paper represents a proper input by combining temperature, fault and time for different thermal anomaly detection methods and then using them for estimating earthquake intensity. Results show that the proposed input data has better outcome in each of the thermal anomaly detection methods compared to the ordinary input data and the estimated intensities of most earthquakes are very close to the actual intensities. This approach can be used as a tool for future unknown earthquakes prediction.
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