Rapid damage assessment for earthquakes from synthetic aperture radar and machine learning

Earthquakes strike suddenly and without warning and can impact a large geographic region. Rapid identification of the amount and distribution of damage is critical for local and international responders (1, 2). This PhD project will develop tools for rapid damage mapping following large earthquakes, exploiting recent advances in synthetic aperture radar imaging and machine learning.

Satellite observations can also be used to directly assess damage resulting from earthquakes and secondary hazards such as landslides (3). Methods that use high-resolution optical images are powerful but fail when there is cloud cover, and currently require significant human involvement to produce results; results from optical methods are typically not available on the timescales required for emergency response (4).

Satellite radar (SAR) imagery can see through clouds and can be acquired night and day. The recent proliferation of SAR missions (3) means we will soon be able to obtain new SAR imagery for most earthquakes in less than 24 hours (5).

In COMET, we have developed a system for processing large quantities of Sentinel-1 satellite radar imagery over tectonic and volcanic areas (6). The student will exploit this to produce RapidSAR coherence, phase and amplitude time series from Sentinel-1 SAR data. They will test the use of various machine learning and deep learning classifiers (e.g., boosted decision trees, convolutional neural networks) to identify areas where rapid changes have occurred during an earthquake.

This may involve use of individual observations or series of observations to construct a suitable feature set for change detection. The first step, which will involve the most research and experimentation, will be to test and develop algorithms using data from an earthquake with known distribution of damage, for example recent earthquakes in Nepal, New Zealand, and Italy.

The student will then apply the method systematically to a suite of earthquakes that occur during the timeframe of the project, with the ultimate aim of implementing a routine rapid damage assessment system.

1. M. Wendelbo et al., The crisis response to the Nepal earthquake: Lessons learned. European Institute for Asian Studies. Brussels, Belgium: Author, (2016).

2. D. Sanderson, B. Ramalingam, NEPAL EARTHQUAKE RESPONSE: Lessons for operational agencies. The active learning network for accountability and performance in Humanitarian action ALNAP, (2015).

3. J. Elliott, R. Walters, T. Wright, The role of space-based observation in understanding and responding to active tectonics and earthquakes. Nature communications 7, 13844 (2016).

4. J. G. Williams et al., Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes. Natural hazards and earth system sciences. 18, 185-205 (2018). 5. S.-H. Yun, E. J. Fielding, F. H. Webb, M. Simons. (Google Patents, 2015).

6. M. Lazecky et al., LiCSAR: An automatic InSAR tool for measuring and monitoring tectonic and volcanic activity. Remote Sensing 12, 2430 (2020).