Infrasound single-channel noise reduction: application to detection and localization of explosive volcanism in Alaska using backprojection and array processing
Infrasound single-channel noise reduction: application to detection and localization of explosive volcanism in Alaska using backprojection and array processing
复制标题
次声单通道降噪：使用反投影和阵列处理检测和定位阿拉斯加爆发性火山活动
DOI：
10.1093/gji/ggac182
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发表时间：
2022
影响因子：
2.8
通讯作者：
Lyons, John J.
中科院分区：
文献类型：
--
作者：
Sanderson, Richard W.;Matoza, Robin S.;Fee, David;Haney, Matthew M.;Lyons, John J.
次声传感器以各种空间配置和规模部署，用于地球物理监测，包括单传感器网络和多传感器次声阵列网络。利用这些数据的次声信号检测策略通常利用传感器间的相关性和相干性（阵列处理，多通道相关性）;基于网络的信号特征跟踪（例如逆时迁移）;或这些的组合，例如多个阵列的后方位交叉方位。基于单传感器痕迹的去噪技术为改善所有这些不同的次声数据处理策略提供了巨大的潜力，但之前尚未进行详细研究。单传感器去噪代表了一个预处理步骤，可以减少次声信号关联和定位工作流程中环境次声和风噪声的影响。我们系统地研究了一系列用于次声数据处理的单传感器去噪方法的实用性，包括噪声门控、非负矩阵分解和数据自适应维纳滤波。对于数据测试平台，我们使用阿拉斯加相对密集的区域次声网络，该网络记录了高速率的火山喷发，信号的功率，持续时间，波形和频谱特征各不相同。我们主要使用2016-2017年博戈斯洛夫火山爆发的数据，其中包括多次爆炸和合成数据。Bogoslof火山序列提供了一个机会，调查区域次声检测，协会和位置的一组真实的源不同的源谱受各向异性大气传播和不同的噪声水平（不相干的风噪声和相干的环境次声，主要是微气压）。我们说明了不同的去噪方法的优点和缺点的类别，如事件检测，波形失真，需要手动数据标签，和计算成本。对于所有方法，去噪通常对于具有较高信噪比以及信号和噪声之间的频谱和时间重叠较少的信号表现得更好。微气压是全球最普遍和重复的相干环境次声噪声源，这种噪声通常被称为杂波或干扰。我们发现，去噪提供了显着的潜力微气压杂波减少。在标准阵列处理之前对微气压进行单通道去噪，可提高可检测火山事件的数量和带宽。我们发现，使用我们研究的去噪方法来减少非相干风噪声更具挑战性;因此，站硬件（风噪声降低系统）和站点选择仍然至关重要，并且不能被当前可用的数字去噪方法所取代。总的来说，我们发现在处理工作流程中添加单通道去噪作为一个组件可以使各种次声信号检测，关联和定位方案受益。去噪方法还可以隔离噪声本身，在统计上表征环境次声噪声方面具有实用性。
Infrasound sensors are deployed in a variety of spatial configurations and scales for geophysical monitoring, including networks of single sensors and networks of multisensor infrasound arrays. Infrasound signal detection strategies exploiting these data commonly make use of intersensor correlation and coherence (array processing, multichannel correlation); network-based tracking of signal features (e.g. reverse time migration); or a combination of these such as backazimuth cross-bearings for multiple arrays. Single-sensor trace-based denoising techniques offer significant potential to improve all of these various infrasound data processing strategies, but have not previously been investigated in detail. Single-sensor denoising represents a pre-processing step that could reduce the effects of ambient infrasound and wind noise in infrasound signal association and location workflows. We systematically investigate the utility of a range of single-sensor denoising methods for infrasound data processing, including noise gating, non-negative matrix factorization, and data-adaptive Wiener filtering. For the data testbed, we use the relatively dense regional infrasound network in Alaska, which records a high rate of volcanic eruptions with signals varying in power, duration, and waveform and spectral character. We primarily use data from the 2016–2017 Bogoslof volcanic eruption, which included multiple explosions, and synthetics. The Bogoslof volcanic sequence provides an opportunity to investigate regional infrasound detection, association, and location for a set of real sources with varying source spectra subject to anisotropic atmospheric propagation and varying noise levels (both incoherent wind noise and coherent ambient infrasound, primarily microbaroms). We illustrate the advantages and disadvantages of the different denoising methods in categories such as event detection, waveform distortion, the need for manual data labelling, and computational cost. For all approaches, denoising generally performs better for signals with higher signal-to-noise ratios and with less spectral and temporal overlap between signals and noise. Microbaroms are the most globally pervasive and repetitive coherent ambient infrasound noise source, with such noise often referred to as clutter or interference. We find that denoising offers significant potential for microbarom clutter reduction. Single-channel denoising of microbaroms prior to standard array processing enhances both the quantity and bandwidth of detectable volcanic events. We find that reduction of incoherent wind noise is more challenging using the denoising methods we investigate; thus, station hardware (wind noise reduction systems) and site selection remain critical and cannot be replaced by currently available digital denoising methodologies. Overall, we find that adding single-channel denoising as a component in the processing workflow can benefit a variety of infrasound signal detection, association, and location schemes. The denoising methods can also isolate the noise itself, with utility in statistically characterizing ambient infrasound noise.