Earthquake Prediction Software on Global Scale

Shiraishi, Haruhiro

doi:10.4236/gep.2022.103003

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…It notes that much of the progress has been in developing the data catalogs to train the AI models on, such as their own STEAD dataset. In fact, there have been recent claims of success using AI models [1,8,10,11], where some accuracy quotes are over 90%. The paper [1] measures the upper atmosphere's Ionospheric total electron content, which originates in the rock, while the paper [8] considers water vapour in volcanic regions.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, there have been recent claims of success using AI models [1,8,10,11], where some accuracy quotes are over 90%. The paper [1] measures the upper atmosphere's Ionospheric total electron content, which originates in the rock, while the paper [8] considers water vapour in volcanic regions. Not only different methods, but also different aspects of the earthquake are now predicted and in practice there is still no generally accepted reliable method.…”

Section: Related Workmentioning

confidence: 99%

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Earthquake Footprints for Predicting Events

Greer¹

2023

Preprint

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This paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm measures the history of these events over preceding days, in particular the gaps when the events did not occur. The clusters with the time gaps can be used to create the footprint description and results suggest that seismic events can in fact be traced using this, and subsequently recognised again, if the same conditions occur. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Earthquake Footprints for Predicting Events

Greer¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It notes that much of the progress has been in developing the data catalogs to train the AI models on, such as their own STEAD dataset. In fact, there have been recent claims of success using AI models [1,9,11,12], where some accuracy quotes are over 90%. The paper [1] measures the upper atmosphere's Ionospheric total electron content, which originates in the rock, while the paper [9] considers water vapour in volcanic regions.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, there have been recent claims of success using AI models [1,9,11,12], where some accuracy quotes are over 90%. The paper [1] measures the upper atmosphere's Ionospheric total electron content, which originates in the rock, while the paper [9] considers water vapour in volcanic regions. Not only different methods, but also different aspects of the earthquake are now predicted and in practice there is still no generally accepted reliable method.…”

Section: Related Workmentioning

confidence: 99%

Earthquake Footprints for Predicting Events

Greer¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

Earthquake Prediction Software on Global Scale

Cited by 2 publications

References 14 publications

Earthquake Footprints for Predicting Events

Earthquake Footprints for Predicting Events

Earthquake Footprints for Predicting Events

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