TraceNet: An Effective Deep-Learning-Based Method for Baseline Correction of Near-Field Acceleration Records
TraceNet: An Effective Deep-Learning-Based Method for Baseline Correction of Near-Field Acceleration Records
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DOI：
10.1785/0220220272
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发表时间：
2023-02
影响因子：
3.3
通讯作者：
Sheng Dong;Zhengbo Li;F. Hu;Zhenjiang Yu;Xiaofei Chen
中科院分区：
文献类型：
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作者：
Sheng Dong;Zhengbo Li;F. Hu;Zhenjiang Yu;Xiaofei Chen
在强震观测中，加速度记录是地震研究和地震工程中的重要资料。然而，普遍存在的基线漂移在近场加速度记录有很大的影响，积分速度和双积分位移线性和抛物线漂移，分别。为了获得可靠的强震记录，通常采用高通滤波和两阶段基线拟合的方法进行基线校正。然而，这些滤波方法从加速度记录中排除了低频分量，并导致意外的波形损失。基于经验选取交点矩的基线拟合方法易受外界因素影响，运算时间长。目前，随着加速度计数量的增长，传统方法在处理大量加速度记录的效率和精度方面都不足。在这里，我们提出了TraceNet，一种基于深度学习的方法，用于校正从加速度记录中集成的速度记录中的基线漂移。训练数据集的开发与融合的人工基线和非漂移速度从校正的加速度和位移的事件。TraceNet从输入速度轨迹中提取基线。在TraceNet预测之后，可以通过减去提取的基线来校正漂移。此外，潜在的同震地面位移可以恢复从积分的校正速度。在这项研究中，我们使用加速度记录和连续的全球定位系统观测，从2008年汶川地震，以证明地面偏移恢复。作为一个深度学习应用程序，TraceNet可以自动提取和纠正基线漂移，而不受主观因素的影响。从加速度记录估计的同震位移可以提供对地面变形的额外了解。
In strong ground-motion observations, accelerograms are an important material in both seismic research and earthquake engineering. However, the ubiquitous baseline drift in near-field acceleration records has a large impact on the integrated velocity and double-integrated displacement with linear and parabolic drift, respectively. Conventionally, high-pass filtering and two-stage baseline fitting methods are commonly applied in baseline corrections to obtain reliable strong-motion records. However, these filtering methods exclude low-frequency components from acceleration records and cause unexpected waveform loss. The baseline fitting method, which is based on the experiential selection of intersection moments, is easily affected by external factors and requires a large amount of time for operations. Currently, as the number of accelerometers grows, conventional methods are insufficient in both efficiency and precision to process vast acceleration records. Here, we propose TraceNet, a deep-learning-based method, to correct baseline drifts in velocity records integrated from accelerograms. The training data set is developed with the fusion of artificial baselines and nondrift velocities from corrected accelerations and displacements from events. TraceNet extracts the baseline from the input velocity trace. After TraceNet prediction, the drift can be corrected by subtracting the extracted baseline. In addition, the potential coseismic ground displacement can be recovered from the integration in the corrected velocity. In this study, we used acceleration records and continuous Global Positioning System observations from the 2008 Wenchuan earthquake to demonstrate the ground offset recovery. As a deep learning application, TraceNet can extract and correct the baseline drifts automatically without subjective factors. The coseismic displacements estimated from accelerograms can provide additional insight into the ground deformation.