High-resolution seismic event detection using local similarity for Large-N arrays

Li, Zefeng; Peng, Zhigang; Hollis, Dan; Zhu, Lijun; McClellan, J.H.

doi:10.1038/s41598-018-19728-w

Cited by 83 publications

(65 citation statements)

References 32 publications

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“…For example, the use of probabilistic graphical models and graph theory in seismology has become increasingly prevalent. The deployment of large-N arrays (Karplus and Schmandt, 2018) provides one such opportunity, in which weak event signals can be extracted using graph clustering (Riahi and Gerstoft, 2017) or similarity theory (Li, Peng, et al, 2018). Separately, Trugman and Shearer (2017) use graph theory and hierarchical cluster analysis to obtain high-precision earthquake location estimates using differential travel times from pairs of earthquakes observed at a set of common stations.…”

Section: Other Applications and Future Directionsmentioning

confidence: 99%

Machine Learning in Seismology: Turning Data into Insights

Kong

Trugman

Ross

et al. 2018

Seismological Research Letters

397

197

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This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground-motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data-driven ML with traditional physical modeling.

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Section: Other Applications and Future Directionsmentioning

confidence: 99%

Machine Learning in Seismology: Turning Data into Insights

Kong

Trugman

Ross

et al. 2018

Seismological Research Letters

397

197

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“…Recent advances in large N processing approaches (e.g. 36 ) also offer strategies for leveraging dense arrays for detecting small seismic events.…”

Section: Discussionmentioning

confidence: 99%

Using Dark Fiber and Distributed Acoustic Sensing for Near-Surface Characterization and Broadband Seismic Event Detection

Ajo‐Franklin¹,

Dou²,

Lindsey³

et al. 2018

Preprint

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We present the first case study demonstrating the use of regional unlit fiber-optic telecommunication infrastructure (dark fiber) and distributed acoustic sensing for broadband seismic monitoring of both near-surface soil properties and earthquake seismology. We recorded 7 months of passive seismic data on a 27 km section of dark fiber stretching from West Sacramento, CA to Woodland, CA, densely sampled at 2 m spacing. This dataset was processed to extract surface wave velocity information using ambient noise interferometry techniques; the resulting V s profiles were used to map both shallow structural profiles and groundwater depth, thus demonstrating that basin-scale variations in hydrological state can be resolved using this technique. The same array was utilized for detection of regional and teleseismic earthquakes and evaluated for long period response using records from the M8.1 Chiapas, Mexico 2017, Sep 8th event. The combination of these two sets of observations conclusively demonstrates that regionally extensive fiber-optic networks can effectively be utilized for a host of geoscience observation tasks at a combination of scale and resolution previously inaccessible. by 9 and 10 ; other examples include using social media proxies as sensors (e.g. 11 ) or MEMS accelerometers in pervasive stationary devices such as personal computers ( 12 ). Broader efforts to leverage networking and sensor technologies related to the Internet-of-Things (IoT) for seismology are developing but still in their infancy (e.g. 13 ).An alternative approach is to exploit components of the built environment to serve as distributed sensor networks. In this case we explore the use of unlit subsurface fiber-optic cables, commonly referred to as "dark fiber", and distributed acoustic sensing (DAS) to provide such a spatially extensive sensing platform. The vast majority of fiber-optic cables in the earth's near-surface were installed exclusively for the purpose of telecommunications. Due to high cost of fiber-optic installation, typical commercial practice is to deploy significantly more capacity, as measured by fiber count, than required; this practice, combined with advances in bandwidth available per fiber, have yielded a surplus of available fibers that remain unused. The US footprint of such unused fiber networks is massive with tens of thousands of linear kilometers of long distance fiber-optic cables available for lease or purchase in the current environment. One notable aspect of such dark fiber network components is that they tend to utilize existing "right-of-way" corridors along roads and rail connections ( 14 ), environments rich in ambient noise. Given the ubiquitous nature of installed telecom fibers, few studies have explored use of this resource for sensing applications. A single experiment explored the use of Brillouin Optical Time Domain Analysis (BOTDA) to monitor temperature over previously installed telecom fiber ( 15 ); however, these studies were conducted primarily to provide network integrity information rather...

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“…. , k do Once we obtain the estimates of the delays we can then use (7) with τ 1i replaced byτ 1i and then apply the Subspace-CUSUM as described above with x t replaced byx t .…”

Section: B Unknown Delaysmentioning

confidence: 99%

“…The tremor signals are useful for geophysical study and prediction of potential earthquakes. Usually, the tremor signals are very weak to detect using data at any individual sensor; therefore, network detection methods have been developed which essentially use covariance information of the data [7]. This network-based detection problem can also be solved by our Subspace-CUSUM scheme discussed in III-B.…”

Section: B Seismic Datamentioning

confidence: 99%

Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors

Xie

Moustakides

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD. We consider the sequential change-point detection for asynchronous multi-sensors, where each sensor observe a signal (due to change-point) at different times. We propose an asynchronous Subspace-CUSUM procedure based on jointly estimating the unknown signal waveform and the unknown relative delays between the sensors. Using the estimated delays, we can align signals and use the subspace to combine the multiple sensor observations. We derive the optimal drift parameter for the proposed procedure, and characterize the relationship between the expected detection delay, average run length (of false alarms), and the energy of the time-varying signal. We demonstrate the good performance of the proposed procedure using simulation and real data. We also demonstrate that the proposed procedure outperforms the well-known "one-shot procedure" in detecting weak and asynchronous signals.

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High-resolution seismic event detection using local similarity for Large-N arrays

Cited by 83 publications

References 32 publications

Machine Learning in Seismology: Turning Data into Insights

Machine Learning in Seismology: Turning Data into Insights

Using Dark Fiber and Distributed Acoustic Sensing for Near-Surface Characterization and Broadband Seismic Event Detection

Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors

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