We present a new method to discriminate between earthquakes and buried explosions using observed seismic data. The method is different from previous seismic discrimination algorithms in two main ways. First, we use seismic spatial gradients, as well as the wave attributes estimated from them (referred to as gradiometric attributes), rather than the conventional three‐component seismograms recorded on a distributed array. The primary advantage of this is that a gradiometer is only a fraction of a wavelength in aperture compared with a conventional seismic array or network. Second, we use the gradiometric attributes as input data into a machine learning algorithm. The resulting discrimination algorithm uses the norms of truncated principal components obtained from the gradiometric data to distinguish the two classes of seismic events. Using high‐fidelity synthetic data, we show that the data and gradiometric attributes recorded by a single seismic gradiometer performs as well as a conventional distributed array at the event type discrimination task.

中文翻译：

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使用地震梯度测量法和数据光谱滤波进行爆炸鉴别

我们提出了一种使用观测到的地震数据区分地震和埋藏爆炸的新方法。该方法在两个主要方面不同于以前的地震判别算法。首先，我们使用地震空间梯度以及从它们估计的波浪属性（称为梯度属性），而不是记录在分布式阵列上的传统三分量地震图。这样做的主要优点是，与传统地震阵列或网络相比，梯度仪的孔径仅为波长的一小部分。其次，我们使用梯度属性作为机器学习算法的输入数据。由此产生的鉴别算法使用从梯度数据获得的截断主成分的范数来区分两类地震事件。