Earthquakes Predictable?

Examined this, even tried some existing test systems. 

Earthquakes Seem to Come in a More Predictable Pattern Than We Thought  By New Scientist, August 30, 2022

A machine learning model developed by researchers at the University of California, Davis (UC Davis) can analyze decades of earthquake data to predict future earthquakes.

The algorithm can determine the probability of a 6.75-magnitude or greater earthquake occurring over a three year-period in a region of California that covers slightly less than 1,000 square kilometers, using records of earthquake since 1970.

The model determined that earthquake data is not as random as previously believed., The researchers used the number of earthquakes per unit of time and had the model identify patterns in the data.

They also applied statistical techniques used in economics to give recent events greater significance than older ones.

The resulting model provides forecast-like probabilities of earthquakes occurring in a short period of time in the future.

Said UC Davis' John Rundle, "What we've done is show that you can see a regional cycle of activity."

From New Scientist  

Earthquake Detection Algorithms

Relates to our previous examination of work  done on earthquake detection.  Makes sense such work is out of Japan.

Gravity signals could detect earthquakes at the speed of light

Algorithm set for deployment in Japan could identify giant temblors faster and more reliably

11 MAY 202211:30 AMBYZACK SAVITSKY

Two minutes after the world’s biggest tectonic plate shuddered off the coast of Japan, the country’s meteorological agency issued its final warning to about 50 million residents: A magnitude 8.1 earthquake had generated a tsunami that was headed for shore. But it wasn’t until hours after the waves arrived that experts gauged the true size of the 11 March 2011 Tohoku quake. Ultimately, it rang in at a magnitude 9—releasing more than 22 times the energy experts predicted and leaving at least 18,000 dead, some in areas that never received the alert. Now, scientists have found a way to get more accurate size estimates faster, by using computer algorithms to identify the wake from gravitational waves that shoot from the fault at the speed of light.

“This is a completely new [way to recognize] large-magnitude earthquakes,” says Richard Allen, a seismologist at the University of California, Berkeley, who was not involved in the study. “If we were to implement this algorithm, we’d have that much more confidence that this is a really big earthquake, and we could push that alert out over a much larger area sooner.”

Scientists typically detect earthquakes by monitoring ground vibrations, or seismic waves, with devices called seismometers. The amount of advance warning they can provide depends on distance between the earthquake and the seismometers, and the speed of the seismic waves, which travel less than 6 kilometers per second. Networks in Japan, Mexico, and California provide seconds or even minutes of advance warning, and the approach works well for relatively small temblors. But beyond magnitude 7, the earthquake waves can saturate seismometers. This makes the most destructive earthquakes, like Japan’s Tohoku quake, the most challenging to identify, Allen says.  ...

Predicting Impact of Earthquakes

Not predict occurrence, but predict impact.  Quite useful in preparation.  Addressing during an event.  Recall we had looked at  feasibility of prediction.  More training samples needed.  

Neural Network Model Helps Predict Site-Specific Impacts of Earthquakes

Hiroshima University (Japan),  April 15, 2022

A neural network model developed by researchers at Japan's Hiroshima University can calculate how soil layers can amplify the seismic waves of large earthquakes. The researchers analyzed microtremor data from 105 sites in the Chugoku district from 2012 to 2020 using a generalized spectral inversion method. Data from each site was split into a training set to educate the neural network, a validation set applied to iterative model optimization, and a test set to assess the model's performance. The model performed well on the test dataset, although, said Hiroshima's Hiroyuki Miura, ”the number of training samples analyzed in this study sites is still limited.” Miura said more training samples needed to be considered “before assuming that the neural network model applies nationwide or globally.”

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