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Machine learning model generates realistic seismic waveforms

#artificialintelligence

LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the efficacy of our generative model, we applied it to seismic field data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.


Machine learning model generates realistic seismic waveforms

#artificialintelligence

LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the e?cacy of our generative model, we applied it to seismic?eld data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.


EarthquakeGen: Earthquake Simulation Using Generative Adversarial Networks

arXiv.org Machine Learning

Detecting earthquake events from seismic time series has proved itself a challenging task. Manual detection can be expensive and tedious due to the intensive labor and large scale data set. In recent years, automatic detection methods based on machine learning have been developed to improve accuracy and efficiency. However, the accuracy of those methods relies on a sufficient amount of high-quality training data, which itself can be expensive to obtain due to the requirement of domain knowledge and subject matter expertise. This paper is to resolve this dilemma by answering two questions: (1) provided with a limited number of reliable labels, can we use them to generate more synthetic labels; (2) Can we use those synthetic labels to improve the detectability? Among all the existing generative models, the generative adversarial network (GAN) shows its supreme capability in generating high-quality synthetic samples in multiple domains. We designed our model based on GAN. In particular, we studied several different network structures. By comparing the generated results, our GAN-based generative model yields the highest quality. We further combine the dataset with synthetic samples generated by our generative model and show that the detectability of our earthquake classification model is significantly improved than the one trained without augmenting the training set.


Model now predicts satellite-killing radiation storms TWO days before they strike

Daily Mail - Science & tech

Space scientists have successfully predicted satellite-killing radiation storms two days before they strike – beating out the previous model that alerted experts only one day in advance. The new model, called PreMevE 2.0, uses machine-learning to improve forecasts by incorporating upstream solar wind speeds from the Van Allen belts. The technology compiles existing data sets to'learn' patterns and predict future storms so satellite operators can take protective measures, including temporarily shutting down part of or even the whole satellite to avoid damage. The model's creators have also noted that it can be used to capture earthquake patterns on earth in order to predict when these natural disasters will strike. This new model, called PreMevE 2.0, uses machine learning to improve forecasts by incorporating upstream solar wind speeds from the Van Allen belts PreMevE 2.0 was developed by space scientists at Los Alamos National Laboratory, who are working in a NASA and National Oceanic and atmospheric Administration (NOAA).


New Machine Learning Model Can Predict Radiation Storms

#artificialintelligence

Researchers are now able to take preventive actions against energetic electrons through a two-day notice delivered by a sophisticated computer model. A new machine learning computer system precisely foresees harmful radiation storms triggered by the Van Allen belts two days before the storm takes place. It is the most developed notice that currently exists, as per a new study published in the journal Space Weather. "Radiation storms from the Van Allen belts can damage or even knock out satellites orbiting in medium and high altitudes above the Earth, but predicting these storms has always been a challenge," said Yue Chen, a space scientist at Los Alamos National Laboratory and principal investigator on the project funded by both NASA and NOAA. "Given that the Van Allen Probes, which provided important data about space weather, recently de-orbited, we no longer have direct measurements about what's happening in the outer electron radiation belt. Our new model uses existing data sets to'learn' patterns and predict future storms so satellite operators can take protective measures, including temporarily shutting down part of or even the whole satellite to avoid damage," Chen added.