Large earthquakes can be destructive and quickly wreak havoc on a landscape.
To mitigate immediate threats, early warning systems have been developed to
alert residents, emergency responders, and critical infrastructure operators
seconds to a minute before seismic waves arrive. These warnings provide time to
take precautions and prevent damage. The success of these systems relies on
fast, accurate predictions of ground motion intensities, which is challenging
due to the complex physics of earthquakes, wave propagation, and their
intricate spatial and temporal interactions. To improve early warning, we
propose a novel AI-enabled framework, WaveCastNet, for forecasting ground
motions from large earthquakes. WaveCastNet integrates a novel convolutional
Long Expressive Memory (ConvLEM) model into a sequence to sequence (seq2seq)
forecasting framework to model long-term dependencies and multi-scale patterns
in both space and time. WaveCastNet, which shares weights across spatial and
temporal dimensions, requires fewer parameters compared to more
resource-intensive models like transformers and thus, in turn, reduces
inference times. Importantly, WaveCastNet also generalizes better than
transformer-based models to different seismic scenarios, including to more rare
and critical situations with higher magnitude earthquakes. Our results using
simulated data from the San Francisco Bay Area demonstrate the capability to
rapidly predict the intensity and timing of destructive ground motions.
Importantly, our proposed approach does not require estimating earthquake
magnitudes and epicenters, which are prone to errors using conventional
approaches; nor does it require empirical ground motion models, which fail to
capture strongly heterogeneous wave propagation effects.

我们提出了一种基于人工智能的框架 WaveCastNet，用于预测大地震引起的地面运动，该框架可以通过整合卷积长表示记忆模型 (ConvLEM) 和序列到序列 (seq2seq) 预测框架来模拟空间和时间上的长期依赖关系和多尺度模式，以提高预警能力并快速预测破坏性地面运动的强度和时机。

WaveCastNet: 一种基于人工智能的地震前警波场预测框架

WaveCastNet: An AI-enabled Wavefield Forecasting Framework for  Earthquake Early Warning

A Clinical Decision Support System (CDSS) is designed to enhance clinician
decision-making by combining system-generated recommendations with medical
expertise. Given the high costs, intensive labor, and time-sensitive nature of
medical treatments, there is a pressing need for efficient decision support,
especially in complex emergency scenarios. In these scenarios, where
information can be limited, an advanced CDSS framework that leverages AI
(artificial intelligence) models to effectively reduce diagnostic uncertainty
has utility. Such an AI-enabled CDSS framework with quantified uncertainty
promises to be practical and beneficial in the demanding context of real-world
medical care. In this study, we introduce the concept of Medical Entropy,
quantifying uncertainties in patient outcomes predicted by neural machine
translation based on the ICD-9 code of procedures. Our experimental results not
only show strong correlations between procedure and diagnosis sequences based
on the simple ICD-9 code but also demonstrate the promising capacity to model
trends of uncertainties during hospitalizations through a data-driven approach.

医学的不确定性的数值化和 AI 模型在决策支持系统中的应用。