A multitask encoder–decoder to separate earthquake and ambient noise signal in seismograms
A multitask encoder–decoder to separate earthquake and ambient noise signal in seismograms
复制标题
用于分离地震图中的地震和环境噪声信号的多任务编码器和解码器
DOI：
10.1093/gji/ggac290
复制
发表时间：
2022
影响因子：
2.8
通讯作者：
He, Bing
中科院分区：
文献类型：
--
作者：
Yin, Jiuxun;Denolle, Marine A.;He, Bing
地震图包含多个地震波源，从不同的瞬态信号（如地震）到连续的环境地震振动（如微震）。环境振动污染了地震信号，而地震信号污染了环境噪声地震学分析所必需的环境噪声的统计特性。因此，从地震信号中分离出环境噪声将有利于多地震学分析。本工作开发了一个多任务编码器-解码器网络WaveDecompNet，用于在时域中直接分离三分量地震记录的瞬态信号和环境信号。我们选择夏威夷的活火山大岛作为天然实验室，因为它具有丰富的瞬变（构造和火山地震）和弥漫的环境噪声（强烈的微震）。该方法以含噪三分量地震记录为输入，独立预测三分量地震波形和噪声波形。该模型使用STandford EArthquake数据集（STEAD）的地震和噪声波形以及地震台站IU. POHA的本地噪声进行训练。我们估计网络的性能，通过使用解释方差度量地震和噪声波形。我们探索了WaveDecompNet的不同神经网络设计，发现具有长短期记忆（LSTM）的模型比其他结构表现得更好。总的来说，我们发现WaveDecompNet在信噪比（SNR）为0.1时提供了令人满意的性能。该方法的潜力在于：（1）提高瞬态（地震）波形的宽带信噪比;（2）改善局部环境噪声，以利用环境噪声信号监测地球结构。为了测试这一点，我们将短时间平均值应用于长时间平均值过滤器，并提高检测到的事件数量。我们还测量了恢复的环境噪声的单站互相关函数，并通过时间和不同的频带建立其改进的相干性。我们的结论是，WaveDecompNet是一个很有前途的工具，广泛的地震学研究。
Seismograms contain multiple sources of seismic waves, from distinct transient signals such as earthquakes to continuous ambient seismic vibrations such as microseism. Ambient vibrations contaminate the earthquake signals, while the earthquake signals pollute the ambient noise’s statistical properties necessary for ambient-noise seismology analysis. Separating ambient noise from earthquake signals would thus benefit multiple seismological analyses. This work develops a multitask encoder–decoder network named WaveDecompNet to separate transient signals from ambient signals directly in the time domain for 3-component seismograms. We choose the active-volcanic Big Island in Hawai’i as a natural laboratory given its richness in transients (tectonic and volcanic earthquakes) and diffuse ambient noise (strong microseism). The approach takes a noisy 3-component seismogram as input and independently predicts the 3-component earthquake and noise waveforms. The model is trained on earthquake and noise waveforms from the STandford EArthquake Dataset (STEAD) and on the local noise of seismic station IU.POHA. We estimate the network’s performance by using the explained variance metric on both earthquake and noise waveforms. We explore different neural network designs for WaveDecompNet and find that the model with long-short-term memory (LSTM) performs best over other structures. Overall, we find that WaveDecompNet provides satisfactory performance down to a signal-to-noise ratio (SNR) of 0.1. The potential of the method is (1) to improve broad-band SNR of transient (earthquake) waveforms and (2) to improve local ambient noise to monitor the Earth’s structure using ambient noise signals. To test this, we apply a short-time average to a long-time average filter and improve the number of detected events. We also measure single-station cross-correlation functions of the recovered ambient noise and establish their improved coherence through time and over different frequency bands. We conclude that WaveDecompNet is a promising tool for a broad range of seismological research.