Land Surface Temperature and Earthquakes
20 case studies world-wide to search for a relation
For more information
E. Pavlidou, M. van der Meijde, H. van der Werff and Ch. Hecker e. pavlidou@utwente.nl
Department of Earth Systems Analysis, ITC, University of Twente, The Netherlands
References
Tronin, A.A. (2010) Satellite Remote Sensing in Seismology. A Review. Remote Sens. 2, pp. 124-150.
Pavlidou, E., van der Meijde, M., van der Werff, H.M.A. and Hecker, C.A. (2016) Finding a needle by removing the
haystack : a spatio - temporal normalization method for geophysical data. In: Computers and geosciences, 90A pp. 78-86.
Results
Fluctuations are found throughout all datasets, before and after the earthquakes. The largest peaks do not always coincide spatially and temporally with the earthquakes.
Example results are from California. Results and videos for all study areas in
http://elendar. altervista.org/pav/ 6.3 Mw, 12km, 11/8/2012 6.5 Mw, 11km, 11/8/2012 7.1 Mw, 18km, 23/10/2011 6 Mw , 6.3km, 20/5/2012 5.8 Mw , 10.2km, 29/5/2012 6.3 Mw , 114 km, 4/3/2010 6.3 Mw , 10 km, 27/2/2010 7.0 Mw , 18 km, 11/3/2010 8.8 Mw , 22.9 km, 27/2/2010 6.3 Mw , 42 km, 26/3/2010 5.9 Mw , 208.4 km, 28/1/2010 7.1 Mw , 206.7 km, 12/8/2010 7.2 Mw , 10 km, 4/4/2010 5.7 Mw, 8.8km, 15/6/2010 6.9 Mw , 8km, 24/3/2011 6.8 Mw, 13.7 km, 11/11/2012 7.0 Mw , 12km, 3/9/2010 6.9 Mw, 17km, 13/4/2010 5.5 Mw , 24.2km, 3/6/2010 5.8 Mw, 7km, 29/5/2010 9.1 Mw, 29km, 11/3/2011 7.1 Mw, 42km, 7/4/2011 6.6 Mw, 11km, 11/4/2011 7.3 Mw, 32km, 9/3/2011 5.8 Mw , 0.02 km, 23/8/2011 ≥9.0 Mw 8.0-8.9 Mw 7.0-7.9 Mw 6.0-6.9 Mw 5.0-5.9 Mw Magnitude Temporal Resolution
24 images per day
8 images per day
Focal Depth
0-30 km 30-70 km ≥70 km
Study areas
Nominal spatial resolution: 3x3km Land Surface Temperatures (http://land.copernicus.eu/global/products/lst)
1.3.2010 1.4.2010 1.5.2010 1.6.2010 30.6.2010 0 10 20 30 40 Nr of A nomalies Time
Baja, close to epicentre (Point A) Baja, close to epicentre (Point B) Chihuahuan desert (Point C)
Arizona, mountainous area (Point D) Earthquake
(a)
central pixel Y frame average (X1+..+Xn)/n 270 O riginal values (K ) 290 310 330 Time −>Thermal anomalies of +(2-10)°C in air, at-sensor and land
surface temperatures (LST) occur prior to earthquakes
[Tronin, 2010 and others]
We process LST datasets to find
anomalies prior to earthquakes
≥5.5 Mw
How?
[Pavlidou et al 2016]
Step 1: Suppress spatially common patterns
Normalize every pixel with its spatial neighbourhood to find only localized effects
Step 2: Find persistent fluctuations
A moving temporal window counts anomalies clustered in time
In graph (a): a peak is visible before the earthquake and close to the
epicenter (point A), but a similar peak is visible before the earthquake far from the epicenter (point D). The largest peak of the dataset is detected close to the epicenter, but after the earthquake (point B).
In graph (b): high counts of anomalies are dispersed throughout the image.
Conclusion
In our results, potential earthquake-related contributions to locally increased LST are obscured by other influences (topography, water bodies, urban areas, agriculture).
INPUT DATA:
(+) Atmospherically corrected, hyper-temporal LST
