Deep Neural Networks Based Denoising of Regional Seismic Waveforms and Impact on Analysis of North Korean Nuclear Tests

IF 1.9 4区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
Andreas Steinberg, Peter Gaebler, Gernot Hartmann, Johanna Lehr, Christoph Pilger
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Abstract

We test a deep learning based denoising autoencoder algorithm on regional and teleseismic seismological and hydroacoustic datasets, which we compile from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on stations which can be relevant to investigate North Korean nuclear tests. Denoising of waveform records using autoencoder techniques potentially enables improved signal detection and processing due to lowered signal-to-noise ratios. We train and compare the performance of several different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if they can still be reliably used in downstream analysis for further inferences on the seismic source type, i.e. seismic moment tensor analysis. The declared North Korean nuclear tests are a suitable benchmark test set, as they have extensively been researched and their source type and location might be assumed known. Verification of the source type is of particular interest for potential nuclear tests under international law. We find that care needs to be taken using the denoised waveform data, as a slight bias is introduced in the seismic moment tensor analysis. However we also find promising results hinting at possible future use of the technique for standard analyses, as it improves the investigation of smaller events. Autoencoder based denoising techniques could be employed in future routine frameworks to increase earthquake catalog completeness and possibly aid in detecting smaller potential treaty relevant events.

Abstract Image

基于深度神经网络的区域地震波形去噪及其对朝鲜核试验分析的影响
我们在区域和远震地震学及水声数据集上测试了基于深度学习的去噪自动编码器算法,这些数据集是我们从全面禁止核试验条约组织的国际监测系统中整理出来的。我们将重点放在与调查朝鲜核试验相关的台站上。使用自动编码器技术对波形记录进行去噪处理可降低信噪比,从而改进信号检测和处理。我们训练并比较了几种不同的去噪自动编码器模型的性能,这些模型适用于短波形和长波形周期,在整个台站网络和单个台站上进行训练。我们研究了去噪波形信号是否可用于震源分析,以及是否仍可可靠地用于下游分析,以进一步推断震源类型,即地震力矩张量分析。已宣布的朝鲜核试验是一个合适的基准测试集,因为已对其进行了广泛研究,其震源类型和位置可能是已知的。对于根据国际法可能进行的核试验,验证震源类型尤其重要。我们发现,在使用去噪波形数据时需要小心谨慎,因为在地震力矩张量分析中会引入轻微的偏差。不过,我们也发现了一些有希望的结果,暗示着未来可能将该技术用于标准分析,因为它能改善对较小事件的调查。基于自动编码器的去噪技术可用于未来的常规框架中,以提高地震目录的完整性,并可能有助于检测较小的潜在条约相关事件。
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来源期刊
pure and applied geophysics
pure and applied geophysics 地学-地球化学与地球物理
CiteScore
4.20
自引率
5.00%
发文量
240
审稿时长
9.8 months
期刊介绍: pure and applied geophysics (pageoph), a continuation of the journal "Geofisica pura e applicata", publishes original scientific contributions in the fields of solid Earth, atmospheric and oceanic sciences. Regular and special issues feature thought-provoking reports on active areas of current research and state-of-the-art surveys. Long running journal, founded in 1939 as Geofisica pura e applicata Publishes peer-reviewed original scientific contributions and state-of-the-art surveys in solid earth and atmospheric sciences Features thought-provoking reports on active areas of current research and is a major source for publications on tsunami research Coverage extends to research topics in oceanic sciences See Instructions for Authors on the right hand side.
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