A Seismic Shift in Scalable Acquisition Demands New Processing: Fiber-Optic Seismic Signal Retrieval in Urban Areas with Unsupervised Learning for Coherent Noise Removal

Martin, Eileen; Huot, Fantine; Ma, Yinbin; Cieplicki, Robert; Cole, Steve; Karrenbach, Martin; Biondi, Biondo

doi:10.1109/msp.2017.2783381

“…The high degree of human activities in urban environments results in a large level of background vibrations that has been received scientific interests in terms of subsurface characterization and hazards mapping (Díaz et al, 2017). Prior urban DAS studies have shown anthropogenic sources of noise such as pumping systems (plumbing, heating or air conditioning) and vehicle traffic (Martin et al, 2018a). While vehicle moving signals can be identified by the linear moveout events and the passing speed is calculated by the slope of the signal, the section will present the DAS recordings of other interesting anthropogenic sources not previously reported.…”

Section: Urban Anthropogenic Seismic Sourcesmentioning

confidence: 99%

Sensing earth and environment dynamics by telecommunication fiber-optic sensors: An urban experiment in Pennsylvania USA

Zhu¹,

Shen²,

Martin³

2020

Preprint

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Abstract. Continuous seismic monitoring of the Earth's near surface (top 100 meters), especially with improving the resolution and extent of data both in space and time, would yield more accurate insights about the effect of extreme weather events (e.g. flooding or drought) and climate change on the Earth's surface and subsurface systems. However, continuous long-term seismic monitoring, especially in urban areas, remains challenging. We describe the Fiber-Optic foR Environmental SEnsEing (FORESEE) project in Pennsylvania, United States, the first continuous monitoring distributed acoustic sensing (DAS) fiber array in the Eastern US. This array is made up of nearly 5 km of pre-existing dark telecommunications fiber underneath the Pennsylvania State University Campus. A major thrust of this experiment is the study of urban geohazard and hydrological systems through near-surface seismic monitoring. Here we detail the FORESEE experiment deployment, instrument calibration, and describe multiple observations of seismic sources in the first year. We calibrate the array by comparison to earthquake data from a nearby seismometer and to active-source geophone data. We observed a wide variety of seismic signatures in our DAS recordings: natural events (earthquakes and thunderstorms) and anthropogenic events (mining blasts, vehicles, music concerts, and walking steps). Preliminary analysis of these signals suggest DAS has the capability to sense broadband vibrations and discriminate between seismic signatures of different quakes and anthropogenic sources. With the success of collecting one-year of continuous DAS recordings, we conclude that DAS along with telecommunication fiber will potentially serve the purpose of continuous near-surface seismic monitoring in populated areas.

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“…Since September 2016, the Stanford Fiber Optic Seismic Observatory has been continuously collecting passive seismic data at a rate of 50 samples per second at 4 meter channel spacing on 2.5 km of fiber optic cables in existing telecommunications conduits underneath the Stanford University campus (Martin et al, 2018). It is the longest running, ultra-dense urban seismic experiment, and has been used to ambient noise interferometry, earthquake detection (Lindsey et al, 2017), and active seismic acquisition .…”

Section: Application To Passive Das Datamentioning

confidence: 99%

“…As technologies such as MEMS accelerometers (Evans et al, 2014) and Distributed Acoustic Sensing (DAS) (Martin et al, 2018) make long-term continuous recording easier than ever, the number of sensors has grown by orders of magnitude (particularly for DAS, which enables meter-scale channel spacing over multiple kilometers of fiber). The number of windows of data averaged in ambient noise interferometry is typically at least hundreds, although modern scalable algorithms must be able to work with streaming data.…”

Section: Introduction and Prior Workmentioning

confidence: 99%

A scalable algorithm for cross-correlations of compressed ambient seismic noise

Martin

¹

2019

SEG Technical Program Expanded Abstracts 2019

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Companies and academic geophysicists are increasingly collecting continuous seismic data on denser arrays, and are looking to a variety of lossy compression methods to store and quickly access this data. Some researchers turn to ambient noise interferometry for low-cost near-surface imaging to avoid to cost and permitting required for active source experiments, but the computation can be very expensive. For each window of time, typical ambient noise interferometry scales as the product of the number of time samples per window and the number of sensors squared. This paper proposes a new algorithm for data stored in a low-rank matrix factorized form, performing interferometry in compressed form, and separating scalability in sensors from time samples. Application to real data shows nearly identical results at orders of magnitude lower cost. The algorithm can be extended to tensor compressions, averaging cross-correlations over many time windows.

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“…Since September 2016, the Stanford Fiber Optic Seismic Observatory has been continuously collecting passive seismic data at a rate of 50 samples per second at 4 meter channel spacing on 2.5 km of fiber optic cables in existing telecommunications conduits underneath the Stanford University campus (Martin et al, 2018). It is the longest running, ultra-dense urban seismic experiment, and has been used to ambient noise interferometry, earthquake detection (Lindsey et al, 2017), and active seismic acquisition (Martin et al, 2017).…”

Section: Application To Passive Das Datamentioning

confidence: 99%

A Scalable Algorithm for Cross-correlations of Compressed Ambient Seismic Noise

Martin¹

2019

Preprint

Self Cite

0

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Companies and academic geophysicists are increasingly collecting continuous seismic data on denser arrays, and are looking to a variety of lossy compression methods to store and quickly access this data. Some researchers turn to ambient noise interferometry for low-cost near-surface imaging to avoid to cost and permitting required for active source experiments, but the computation can be very expensive. For each window of time, typical ambient noise interferometry scales as the product of the number of time samples per window and the number of sensors squared. This paper proposes a new algorithm for data stored in a low-rank matrix factorized form, performing interferometry in compressed form, and separating scalability in sensors from time samples. Application to real data shows nearly identical results at orders of magnitude lower cost. The algorithm can be extended to tensor compressions, averaging cross-correlations over many time windows.

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A Seismic Shift in Scalable Acquisition Demands New Processing: Fiber-Optic Seismic Signal Retrieval in Urban Areas with Unsupervised Learning for Coherent Noise Removal

Cited by 79 publications

References 24 publications

Sensing earth and environment dynamics by telecommunication fiber-optic sensors: An urban experiment in Pennsylvania USA

Sensing earth and environment dynamics by telecommunication fiber-optic sensors: An urban experiment in Pennsylvania USA

A scalable algorithm for cross-correlations of compressed ambient seismic noise

A Scalable Algorithm for Cross-correlations of Compressed Ambient Seismic Noise

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