Upper crustal low-frequency seismicity at Mount St. Helens detected with a dense geophone array

Glasgow, M. E.; Schmandt, Brandon; Hansen, S. M.

doi:10.1016/j.jvolgeores.2018.06.006

Cited by 20 publications

(6 citation statements)

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“…Machine learning algorithms have shown positive results for discriminating earthquakes from rockslides, quarry blasts, and avalanches (Glasgow et al, ; Hammer et al, ; Kuyuk et al, ; Rubin et al, ), and are being tested to enhance earthquake detection using regional networks (Aguiar & Beroza, ; Barrett & Beroza, ; Hammer et al, ; Perol et al, ; Reynen & Audet, ; Ruano et al, ; Yoon et al, ). Such techniques with time‐ and frequency‐domain data features to target varying noise signals can help evaluate the detections and reduce the rate of false positives.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

et al. 2019

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Analysis of continuous seismic waveforms from a temporary deployment at Sage Brush Flats on the San Jacinto fault reveals earthquake‐ and tremor‐like signals generated by the interaction of wind with obstacles above the surface. Tremor‐like waveforms are present at the site during wind velocities above 2 m/s, which occur for 70% of the deployment duration. The response to the wind has significant spatial variability with highest ground motions near large surface objects. The wind‐related signals show ground velocities that exceed the average ground motions of M1.0–1.5 earthquakes for 6–31% of the day. Waveform spectra indicate a modulation of amplitude that correlates with wind velocity and distance from local structures. Earthquake‐like signals are found to originate from local structures and vegetation, and are modified on length scales of tens of meters. Transient signals originating beyond the study area are also observed with amplitudes greater than some microseismic events. The wind‐related ground motions contribute to local high‐frequency seismic noise. Some of these signals may be associated with small failures of the subsurface material. During elevated wind conditions a borehole seismometer at a depth of 148 m shows increased energy in the 1–8‐Hz band that is commonly used for earthquake and tremor detection. The wind‐related earthquake‐ and tremor‐like signals should be accounted for in earthquake detection algorithms due to the similar features in both time and frequency domains. Proper recognition of wind‐related ground motions can contribute to understanding the composition of continuous seismic waveforms and characterize mechanical properties of the shallow crust.

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Section: Discussionmentioning

confidence: 99%

Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

et al. 2019

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“…In California, temporary deployments near the San Jacinto Fault (Ben‐Zion et al., 2015; Y. Cheng et al., 2020; C. W. Johnson et al., 2019, 2020; H. Meng & Ben‐Zion, 2018b; Roux et al., 2015; Share et al., 2017; Sheng et al., 2021; Y. Wang et al., 2019) and among the Ridgecrest aftershocks (Catchings et al., 2020) captured high rates of local seismicity, as did analogous deployments in areas of induced seismicity in Oklahoma (Dougherty et al., 2019; Sweet et al., 2018) and Alberta, Canada (Eaton et al., 2018). Array analysis has also played a foundational role in the detection and characterization of non‐tectonic events such as low frequency earthquakes (Asano et al., 2008; Hutchison & Ghosh, 2017; Sweet et al., 2014, 2019; Thomas et al., 2013; Ueno et al., 2010) and volcano‐tectonic seismicity (Glasgow et al., 2018; J. Han et al., 2018; Hansen & Schmandt, 2015; H. Shen & Shen, 2021).…”

Section: Science Enabled By Big Data Seismologymentioning

confidence: 99%

Big Data Seismology

Arrowsmith

Trugman

MacCarthy

et al. 2022

Reviews of Geophysics

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The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Today, Big Data Seismology is an emergent revolution involving the use of large, data‐dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data‐dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long‐duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up‐to‐speed with how the Big Data revolution is advancing the field of seismology.

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“…Larry Brown (Cornell University) showed how dense arrays allow reflection seismic methods, normally relying on expensive explosive seismic sources, to be applied by using natural earthquake sources (Quiros et al, 2017). Brandon Schmandt (University of New Mexico) highlighted the varied use of these networks, looking at temporal changes in river and groundwater transport (Schmandt et al, 2017), seismic imaging (Ranasinghe et al, 2018),monitoring volcanoes (Glasgow et al, 2018) (Figure 2) and imaging on crustal and lithospheric scales. John Hole (Virginia Tech) showed a drastic improvement in earthquake location, improving accuracy and magnitude completeness in experiments in Virginia (Davenport et al, 2015).…”

Section: History Of Passive Seismologymentioning

confidence: 99%

The Future of Broadband Passive Seismic Acquisition

Hammond¹,

England²,

Rawlinson³

et al. 2019

Preprint

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It is an exciting time to be a seismologist. In November 2018, the InSight lander touched down on Mars and the first seismometer was deployed on another planet. This incredible feat means planetary seismologists are currently searching for marsquakes and will hopefully soon be providing images of its interior and helping us to understand how rocky planets form. However, we have been doing this for a long time in more familiar territory back home on Earth, where the field of terrestrial seismology has reached a turning point with significant developments in instrumentation and the manner of their deployment in recent years. However, equipment available to the UK community has not kept pace and needs urgent regeneration if the UK is to lead in the field of passive seismology in the future. To begin the process of redesigning the UK’s equipment for the next few decades, the British Geophysical Association sponsored a meeting in Edinburgh in late 2018 to discuss the future of passive seismic acquisition. What follows is a historical account of how and why we arrived at the present day UK seismological research and resource base, a summary of the Edinburgh meeting, and a vision for the passive seismic facilities required to support the next 20 years of seismological research.

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Upper crustal low-frequency seismicity at Mount St. Helens detected with a dense geophone array

Cited by 20 publications

References 51 publications

Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

Characteristics of Ground Motion Generated by Wind Interaction With Trees, Structures, and Other Surface Obstacles

Big Data Seismology

The Future of Broadband Passive Seismic Acquisition

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