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Simulation of broad-band ground motions with consistent long-period and short-period components using the Wasserstein interpolation of acceleration envelopes
Geophysical Journal International ( IF 2.8 ) Pub Date : 2021-06-07 , DOI: 10.1093/gji/ggab225 Tomohisa Okazaki 1 , Hirotaka Hachiya 1, 2 , Asako Iwaki 3 , Takahiro Maeda 3 , Hiroyuki Fujiwara 3 , Naonori Ueda 1
Geophysical Journal International ( IF 2.8 ) Pub Date : 2021-06-07 , DOI: 10.1093/gji/ggab225 Tomohisa Okazaki 1 , Hirotaka Hachiya 1, 2 , Asako Iwaki 3 , Takahiro Maeda 3 , Hiroyuki Fujiwara 3 , Naonori Ueda 1
Affiliation
SUMMARY Practical hybrid approaches for the simulation of broad-band ground motions often combine long-period and short-period waveforms synthesized by independent methods under different assumptions for different period ranges, which at times can lead to incompatible time histories and frequency properties. This study explores an approach that generates consistent broad-band waveforms using past observation records, under the assumption that long-period waveforms can be obtained from physics-based simulations. Specifically, acceleration envelopes and Fourier amplitude spectra are transformed from long-period to short-period using machine learning methods, and they are combined to produce a broad-band waveform. To effectively obtain the relationship of high-dimensional envelopes from limited amount of data, we (1) formulate the problem as the conversion of probability distributions, which enables the introduction of a metric known as the Wasserstein distance, and (2) embed pairs of long-period and short-period envelopes into a common latent space to improve the consistency of the entire waveform. An experimental application to a past earthquake demonstrates that the proposed method exhibits superior performance compared to existing methods as well as neural network approaches. In particular, the proposed method reproduces global properties in the time domain, which confirms the effectiveness of the embedding approach as well as the advantage of the Wasserstein distance as a measure of dissimilarity of the envelopes. This method serves as a novel machine learning approach that maintains consistency both in the time-domain and frequency-domain properties of waveforms.
中文翻译:
使用加速度包络的 Wasserstein 插值法模拟具有一致的长周期和短周期分量的宽带地面运动
总结 用于模拟宽带地震动的实用混合方法通常将通过独立方法在不同周期范围的不同假设下合成的长周期和短周期波形结合起来,这有时会导致不兼容的时程和频率特性。本研究探索了一种方法,该方法使用过去的观测记录生成一致的宽带波形,假设可以从基于物理的模拟中获得长周期波形。具体来说,使用机器学习方法将加速度包络和傅立叶幅度谱从长周期转换为短周期,并将它们组合起来以产生宽带波形。为了有效地从有限的数据中获取高维包络关系,我们(1)将问题表述为概率分布的转换,这使得引入称为 Wasserstein 距离的度量成为可能,以及(2)将长周期和短周期包络对嵌入到一个共同的潜在空间中以改进整个波形的一致性。对过去地震的实验应用表明,与现有方法以及神经网络方法相比,所提出的方法表现出优越的性能。特别是,所提出的方法在时域中再现了全局属性,这证实了嵌入方法的有效性以及 Wasserstein 距离作为信封不同程度度量的优势。
更新日期:2021-06-07
中文翻译:
使用加速度包络的 Wasserstein 插值法模拟具有一致的长周期和短周期分量的宽带地面运动
总结 用于模拟宽带地震动的实用混合方法通常将通过独立方法在不同周期范围的不同假设下合成的长周期和短周期波形结合起来,这有时会导致不兼容的时程和频率特性。本研究探索了一种方法,该方法使用过去的观测记录生成一致的宽带波形,假设可以从基于物理的模拟中获得长周期波形。具体来说,使用机器学习方法将加速度包络和傅立叶幅度谱从长周期转换为短周期,并将它们组合起来以产生宽带波形。为了有效地从有限的数据中获取高维包络关系,我们(1)将问题表述为概率分布的转换,这使得引入称为 Wasserstein 距离的度量成为可能,以及(2)将长周期和短周期包络对嵌入到一个共同的潜在空间中以改进整个波形的一致性。对过去地震的实验应用表明,与现有方法以及神经网络方法相比,所提出的方法表现出优越的性能。特别是,所提出的方法在时域中再现了全局属性,这证实了嵌入方法的有效性以及 Wasserstein 距离作为信封不同程度度量的优势。