Constraining the Geometry of the Northwest Pacific Slab Using Deep Clustering of Slab Guided Waves

Guangcan Liu, Daoyuan Sun, Zefeng Li
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Abstract

Accurately defining slab geometry is crucial for unraveling the seismogenic mechanism and subduction dynamics. Guided wave, generated from deep earthquakes with a focal depth greater than 100 km, efficiently propagates along a continuous slab and offers an effective way to image the slab geometry. However, it is challenging to manually identify slab guided waves through a large dataset, hindering their application in determining slab geometry. We propose the use of a deep embedding clustering algorithm for identifying slab guided waves. Using waveform data for deep earthquakes within the northwestern Pacific slab recorded by the F-net in Japan, we first employ spectra clustering analysis to determine three classification types. Subsequently, we perform clustering analysis on the spectrogram, efficiently featuring guided wave characteristics by enhancing the high-frequency energy. Then, using the sampled region by slab guided wave as a proxy, we map out the boundaries of the northwest Pacific slab at different depths, particularly within the depth range of 200–400 km. Our inferred slab boundaries correlate well with those derived from other methods, validating the accuracy and efficiency of our clustering analysis. Evaluation of our proposed workflow on smaller earthquakes with a lower signal-to-noise ratio underscores its great potential in determining slab geometry, particularly in less-studied regions.
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利用板岩导波的深度聚类确定西北太平洋板岩的几何形状
准确界定板块几何形状对于揭示地震发生机制和俯冲动力学至关重要。导波由焦点深度大于 100 千米的深层地震产生,可沿着连续的板块有效传播,为板块几何成像提供了有效途径。然而,通过大量数据集手动识别板坯导波具有挑战性,阻碍了其在确定板坯几何形状方面的应用。我们建议使用深嵌入聚类算法来识别板岩导波。利用日本 F 网记录的西北太平洋板块内深层地震的波形数据,我们首先采用频谱聚类分析确定了三种分类类型。随后,我们对频谱图进行聚类分析,通过增强高频能量来有效地显示导波特征。然后,我们以板块导波采样区域为代表,绘制出西北太平洋板块在不同深度的边界,尤其是在 200-400 公里深度范围内。我们推断出的板块边界与其他方法得出的边界有很好的相关性,验证了我们聚类分析的准确性和效率。在信噪比较低的较小地震上对我们提出的工作流程进行评估,凸显了其在确定板块几何形状方面的巨大潜力,尤其是在研究较少的地区。
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