Seismic waveforms are essential for seismology but are clipped when their actual amplitudes are too high to be faithfully recorded by seismometers. The clipping effects are popular for both big earthquakes and small earthquakes within a short epicentral distance. Here, we illustrate potential risks of direct usage of clipped waveforms by examining the frequency leakage and show the failure of bandpass filtering for different clipping levels; then we summarize two characteristics of clipped records: (1) The temporal gradient is unusually large around the clipped segment compared with the unclipped portions, and (2) the clipped samples cluster into one segment or several if many samples are involved. Next, we propose three criteria for distinguishing clipped samples from the perfect samples based on these two characteristics. Finally, we design a numerical algorithm for automatic detection of clipped samples using constraints on the gradient, amplitude, and gradient‐varying range. Numerical experiments show the excellent performance of our algorithm on automatically detecting the clipped samples. Our algorithm seamlessly integrates all necessary constraints for both flat‐top type and back‐to‐zero type and thus can correctly recognize these two types simultaneously; in addition, it is basically data driven and thus can work well without considering seismometer configuration and instrument type, which would be helpful for real‐time detection of clipped records without interruption from human operations. As a robust and swift tool of automatic detection on amplitude‐clipped samples, our algorithm could identify most typical clipped records and reduce potential risks due to using unrecognizable clipped waveforms; furthermore, it would be helpful for fast detection and possible restoration of clipped waveforms in the presence of huge volumes of data.

中文翻译：

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从限幅地震波形中自动检测振幅失真的样本

地震波形对于地震学是必不可少的，但当其实际振幅过高而无法由地震仪准确记录时，会被削波。在短震中距离内，大地震和小地震都具有削波效应。在这里，我们通过检查频率泄漏来说明直接使用削波波形的潜在风险，并展示了在不同削波电平下带通滤波的失败。然后我们总结了裁剪记录的两个特征：（1）与未裁剪部分相比，裁剪片段周围的时间梯度异常大；（2）裁剪后的样本分为一个片段或多个片段（如果涉及多个样本）。接下来，我们提出了基于这两个特征的三个标准，用于将修剪样本与完美样本区分开。最后，我们设计了一种使用梯度，幅度和梯度变化范围的约束条件自动检测修剪样本的数值算法。数值实验表明，我们的算法在自动检测剪裁后的样本上具有出色的性能。我们的算法无缝集成了平顶类型和归零类型的所有必要约束，因此可以同时正确识别这两种类型；此外，它基本上是数据驱动的，因此可以在不考虑地震仪配置和仪器类型的情况下很好地工作，这将有助于在不中断人工操作的情况下实时检测剪切的记录。作为一种强大且快速的工具，可以自动检测振幅限制的样本，我们的算法可以识别最典型的限幅记录并减少由于使用无法识别的限幅波形而导致的潜在风险；此外，这对于在存在大量数据的情况下快速检测并可能恢复削波波形很有帮助。