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Library: WTW – How improvements in earth observation and machine learning are re-shaping earth hazard assessment

Executive summary

When you say the terms ‘earth hazards’ or ‘geological hazards’, you may assume that the best way to gather data and assess these would be through studies on the ground. This has, of course, been the norm for much of the time scientists have been studying the world beneath our feet, but in the past few decades there has been a significant shift in how geoscientists view and assess natural hazards.

 The Digital Insurer reviews WTW’s Report on How improvements in earth observation and machine learning are re-shaping earth hazard assessment

Earth hazard research takes a very high level view of data sources

Satellites offer a more complete picture

The use of ground-based observations alone come with a number of limitations, including accessibility, the speed at which large areas can be covered, and inconsistencies between different surveys. For large-scale features such as faults, or remote locations such as some volcanoes, this means it becomes difficult to fully and accurately assess the hazard posed. Satellite imagery can cover features such as faults, volcanoes and landslides on a global scale, allowing for a more complete picture than ground-based observations, and providing access to even the most remote or inhospitable locations.

The imagery is independent of ground-based monitoring methods such as tiltmeters, and by comparing multiple images through time using methods such as InSAR (Interferometric Satellite Aperture Radar) ground movement and deformation over a range of timescales can be observed. Comparison against ground-based observations can help to improve accuracy and account for instrumentation errors, and the combination of both ground and satellite monitoring can provide a more complete picture of features such as fault systems and magma complexes.

Onwards from upwards

One of the most exciting aspects of using satellite imagery for geohazard analysis is that there have been continuous improvements recently in the frequency, type, and availability. The launch of satellites such as Sentinel-1 mean it is becoming increasingly feasible to routinely study volcanic and seismic hazards in remote and inaccessible regions, however this comes with its own set of challenges.

The sheer amount of data produced by Sentinel-1 is too large to be manually analysed on a global scale, so work has been done to use machine learning algorithms and convolutional neutral networks (CNN) to automatically detect volcanic (e.g. Anantrasirichai et al. 2018, 2019) and co-seismic (e.g. Brengman & Barnhart 2021) ground deformation and differentiate it from atmospheric noise.

See the full report for more…

Link to Full Article:: click here

Link to Source:: click here

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