原文網址：https://discover.lanl.gov/news/0511-tsunamis
經過訓練的深度學習模型可以即時評估超大型地震的規模
洛斯阿拉莫斯國家實驗室創造的深度學習模型能挑出超大型地震產生的重力波而偵測到它們，這種新方法可以即時預估地震的規模並加快海嘯警報發布的時間。
「我們的模型利用以往被視作噪訊的數據，成功估算出地震的規模，並且可以立刻轉換成海嘯警報，」洛斯阿拉莫斯地球物理團隊的科學家Bertrand
Rouet-Leduc表示。
在減輕大型地震造成的強烈震動與海嘯所帶來的風險時，對它們的規模做出迅速且可信的估計是很重要的。但是依據地震波的標準預警系統無法快速估計大型地震的強度——這種系統是透過地震產生的震動來直接估計地震的規模，它們無法區別規模8和9的地震，即便後者釋放出的能量與破壞程度是前者的30倍。
將重要的區別化為可能
在5月11日發表於《自然》(Nature)的新研究，研究團隊發現一項長久以來的理論——非常大的地震可以產生重力波——也能用來進行地震預警。不同於依據地震波的預警方式，依據重力的預警方式並不會隨著規模變大而飽和，意味它可以立刻區別規模8和9的地震。
另外一種預估地震規模的現行方式的原理為GPS。雖然它得出的估計結果比依據地震波的預警系統更好，但還是有不小的誤差以及延遲。
對大地震來說PEGS是更準確的方法
最近發現以光速傳遞的瞬時重力訊號(Prompt
Elasto-Gravity Signals)有望克服上述限制，但是在此之前都還沒被當作地震預警系統來測試。跟現有的方法相反，PEGS法在偵測大型地震時較為準確。
研究團隊顯示PEGS可以用來在地震到達一定大小之後即時追蹤它的後續成長與規模。日本的區域型寬頻地震網有PEGS的紀錄，借助其中攜帶的資訊，團隊研發出一個深度學習模型。
團隊利用合成波形的資料庫加上地震網實際測量到的噪訊來訓練他們的深度學習模型，接著展示此模型可以從實際的數據立即推出地震的來源，這是第一個成功的例子。
如果未來發生超大型的隱沒帶地震，其規模足以產生跨過海堤的海嘯而威脅到岸邊的居民，此模型配合即時數據便能讓人民收到警報的時間提前許多。
New research could provide earlier
warning of tsunamis
Deep-learning models can be trained to
assess the magnitude of mega earthquakes in real time
A new method of detecting mega
earthquakes, which picks up on the gravity waves they generate by using
deep-learning models created at Los Alamos National Laboratory, can estimate
earthquake magnitude in real time and provide earlier warning of tsunamis.
“Our model unlocks real-time estimation of earthquake
magnitude, using data routinely treated as noise, and can immediately be
transformative for tsunami early warning,” said Bertrand Rouet-Leduc, a
scientist in Los Alamos’ Geophysics group.
Rapid and reliable magnitude estimation for large
earthquakes is crucial to mitigate the risk associated with strong shaking and
tsunamis. Standard early warning systems based on seismic waves cannot rapidly
estimate the size of large earthquakes; the systems rely on estimating
earthquake magnitude directly from the shaking it produces. These systems
cannot distinguish between magnitude 8 and magnitude 9 earthquakes, even though
the latter is 30 times more energetic and destructive.
Important
distinctions possible
In new research, published May 11 in Nature, a research team found that a
long-theorized gravity wave associated with very large earthquakes can also be
used for earthquake early warning. Unlike seismic-based early warning,
gravity-based early warning does not saturate with magnitude, meaning that
gravity-based earthquake early warning can immediately distinguish between magnitude
8 and 9 earthquakes.
Other current approaches rely on GPS to estimate
earthquake magnitude. While this approach provides better estimations than
seismic-based earthquake early warning, it is also subject to large
uncertainties and latency.
PEGS approach
more accurate for larger earthquakes
The recently discovered, speed-of-light Prompt
Elasto-Gravity Signals approach raised hopes to overcome these limitations, but
until now, had never been tested for earthquake early warning. As opposed to
current methods, the PEGS approach to detection gets more accurate for larger
earthquakes.
The research team showed that PEGS can be used in
real time to track earthquake growth and magnitude immediately after it reaches
a certain size. The team developed a deep-learning model that leverages the
information carried by PEGS, which is recorded by regional broadband
seismometers in Japan.
After training the deep-learning model on a database
of synthetic waveforms augmented with empirical noise measured on the seismic
network, the team was able to show the first example of instantaneous tracking
of an earthquake source on real data.
This model, combined with real-time data, can alert
communities much earlier if a subduction mega earthquake is large enough to
create a tsunami that will breach the seawalls in place and endanger the
coastal populations.
原始論文：Andrea
Licciardi, Quentin Bletery, Bertrand Rouet-Leduc, Jean-Paul Ampuero, Kévin
Juhel. Instantaneous tracking of earthquake growth with elastogravity
signals. Nature, 2022; DOI: 10.1038/s41586-022-04672-7
引用自：DOE/Los Alamos National Laboratory. "New
research could provide earlier warning of tsunamis
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