Earthquakes are a major concern in increasingly populated regions, however their prediction is a difficult task. Researchers have recently made progress in the use of complex simulation and modeling techniques to better forecast the occurrences of earthquakes.
In a recent study, researchers used Gradient Boosted Regression Trees, a machine learning technique for regression and classification problems that incorporates training data, to better determine spatiotemporally complex loading histories within subduction zones. The researchers simulated tens of earthquakes using a small‐scale experimental replica of a subduction zone and show that machine learning predicts well the timing and size of laboratory earthquakes by reconstructing and properly interpreting the spatiotemporally complex loading history of the system. These results promise substantial progress in real earthquake forecasting as they suggest that the complex motion recorded by geodesists at…
read moreSpanish and Ecuadorian researchers have developed a new methodology to estimate faults and volcanoes that can be activated in a region after an earthquake. The approach consist in evaluating changes of static stress on the surrounding faults and volcanoes and producing maps of potentially activated faults and volcanoes.
The main goal of the study is to achieve an effective transfer of knowledge and scientific techniques to non-expert users who are responsible for the management of disasters and risks.
The study was led by researchers from the European Telecommunications Standards Institute (ETSI) in Topography, Geodesy and Cartography of the Polytechnic University of Madrid (UPM), along with the Complutense (UCM) and the Geological and Mining Institute of Spain (IGME).
These institutions are trying to improve the management of earthquakes and volcanoes through scientific methods using Interferometric Synthetic Aperture Radar…
read moreWelcome to the fascinating world of Radar tomography. This tutorial presents a set of training resources for the introduction into this advanced Radar remote sensing technique. The material consists of the following components
A theory unit comprising Slides on Basics, Concepts & Techniques
An interactive Python tutorial including a Jupyter Notebook
Free Test data from the airborne F-SAR platform
An explanation video on how to use the tutorial
The module SAR Tomography introduces the advanced technique of combining multiple radar images from several viewing angles into a new dimension of information. Although tomogoraphic measuring systems and applications are recently in an experimental stage, this technology has a large potential to enhance the information content of radar data. This module gives an overview of the basics, limitations, mathematical foundations, advanced techniques and recent case studies.
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