Various earthquake early warning (EEW) methodologies have been proposed globally for speedily estimating information (i.e., location, magnitude, ground-shaking intensities, and/or potential consequences) about ongoing seismic events for real-time/near real-time earthquake risk management. Conventional EEW algorithms have often been based on the inferred physics of a fault rupture combined with simplified empirical models to estimate the source parameters and intensity measures of interest. Given the recent boost in computational resources, data-driven methods/models are now widely accepted as effective alternatives for EEW. This study introduces a highly accurate deep-learning-based computational framework named ROSERS (i.e., Real-time On-Site Estimation of Response Spectra) to estimate the acceleration response spectrum (S_a(T)) of the expected on-site ground-motion waveforms using early non-damage-causing early p-waves and site characteristics. The framework is trained using a carefully selected extensive database of recorded ground motions. Due to the well-known correlation of S_a(T) with structures’ seismic response and resulting damage/losses, rapid and accurate knowledge of expected on-site S_a(T) values is highly beneficial to various end-users to make well-informed real-time and near-real-time decisions. The framework is thoroughly assessed and investigated through multiple statistical tests under three historical earthquake events. These analyses demonstrate that the overall framework leads to excellent prediction power and, on average, has an accuracy above 85% for hazard-consistent early-warning trigger classification.

中文翻译：

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用于地震地震动加速度响应谱实时现场估计的深度神经网络框架

全球范围内提出了各种地震预警 (EEW) 方法，用于快速估算有关正在进行的地震事件的信息（即位置、震级、地面震动强度和/或潜在后果），用于实时/近实时地震风险管理. 传统的 EEW 算法通常基于断层破裂的推断物理学，并结合简化的经验模型来估计感兴趣的源参数和强度测量。鉴于最近计算资源的增加，数据驱动的方法/模型现在被广泛接受为 EEW 的有效替代方案。本研究引入了一个名为 ROSERS（即实时现场响应谱估计）的高精度基于深度学习的计算框架来估计加速度响应谱（S _a ( T )) 使用早期非破坏性早期p波和现场特征的预期现场地面运动波形。该框架使用精心挑选的大量记录地面运动的数据库进行训练。由于众所周知的S _a ( T ) 与结构的地震响应和由此产生的损坏/损失的相关性，快速准确地了解预期的现场S _a ( T) values 非常有利于各种最终用户做出明智的实时和近实时决策。通过在三个历史地震事件下进行的多次统计测试，对该框架进行了彻底的评估和调查。这些分析表明，整体框架具有出色的预测能力，并且平均而言，对灾害一致的预警触发分类的准确度超过 85%。