Watching the Cryosphere Thaw: Seismic Monitoring of Permafrost Degradation Using Distributed Acoustic Sensing During a Controlled Heating Experiment

Cheng, Feng; Lindsey, Nathaniel J.; Sobolevskaia, Valeriia; Dou, Shan; Freifeld, Barry; Wood, Todd; James, S. R.; Wagner, A. M.; Ajo‐Franklin, Jonathan

doi:10.1029/2021gl097195

Cited by 20 publications

(7 citation statements)

References 67 publications

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“…This opens new possibilities to utilize single station data for monitoring purposes, especially in environments with many source and medium processes such as permafrost (e.g., Köhler & Weidle, 2019) or volcanoes. AI‐based strategies could complement other passive seismic methods used for permafrost monitoring (e.g., Cheng et al., 2022; James et al., 2019; Lindner et al., 2021). This could give new insight into the response of permafrost to climate change given the decade‐long availability of single seismic stations near permafrost areas.…”

Section: Discussionmentioning

confidence: 99%

AI‐Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns

Steinmann

Seydoux

Campillo

2022

Geophysical Research Letters

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Continuous seismograms are time series of the ground motion recorded at a single location and provide a vast amount of information about processes occurring at the Earth's surface and interior. The recorded ground motion at a given location results from the convolution of the medium's impulse response-expressed as the Green's function-and the seismic waves emitted by various sources, often simultaneously. Thus, continuous seismograms are goldmines to study the medium's properties or sources in time. However, unmixing source or medium changes is often not easy, especially if source and medium changes coincide. For instance, seismic recordings in the vicinity of volcanoes, where many different source and medium effects occur, are challenging and complex datasets to analyze.To better explore continuous seismic data, seismologists developed many data processing tools to extract valuable information for the task at hand. For example, the short-term-average to long-term-average energy ratio (STA/ LTA) scans the continuous recordings for impulsive signals (Allen, 1978). On the other hand, passive image interferometry can interrogate the medium regularly by exploiting the ambient seismic signals of a data set (Sens-Schönfelder & Wegler, 2006). Undoubtedly, these tools delivered many new insights into the processes happening at and inside the Earth. However, it is important to note that the design of the tools and the related preprocessing favors certain processes in the seismic data. This can be a problem if the source or medium processes encoded in the seismic data are poorly understood. For example, non-volcanic tremors were detected about 20 years ago (Obara, 2002), and still today, the physical mechanism and signal properties of such events are not well apprehended. Therefore, it remains unclear if these signals do not exist in specific environments or if the detection tools are not adapted to the task (Bocchini et al., 2021;Pfohl et al., 2015).Artificial intelligence (AI) can help overcome those blind spots and discover new signals or hidden patterns within the data. Recently, clustering gained attention as a method to identify families of signals in the continuous seismograms (

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Section: Discussionmentioning

confidence: 99%

AI‐Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns

Steinmann

Seydoux

Campillo

2022

Geophysical Research Letters

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“…This opens new possibilities to utilize single station data for monitoring purposes, especially in environments with many source and medium processes such as permafrost (e.g., Köhler & Weidle, 2019) or volcanoes. AI-based strategies could complement other passive seismic methods used for permafrost monitoring (e.g., Cheng et al, 2022;James et al, 2019;Lindner et al, 2021). This could give new insight into the response of permafrost to climate change given the decade-long availability of single seismic stations near permafrost areas.…”

Section: Discussionmentioning

confidence: 99%

AI-based unmixing of medium and source signatures from seismograms: ground freezing patterns

Steinmann

Seydoux

Campillo

2022

Preprint

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Seismograms always result from mixing many sources and medium changes that are complex to disentangle, witnessing many physical phenomena within the Earth. With artificial intelligence (AI), we isolate the signature of surface freezing and thawing in continuous seismograms recorded in a noisy urban environment. We perform a hierarchical clustering of the seismograms and identify a pattern that correlates with ground frost periods. We further investigate the fingerprint of this pattern and use it to track the continuous medium change with high accuracy and resolution in time. Our method isolates the effect of the ground frost and describes how it affects the horizontal wavefield. Our findings show how AI-based strategies can help to identify and understand hidden patterns within seismic data caused either by medium or source changes.Plain Language Summary Seismic waves, emitted by a seismic source and then traveling through the Earth, contain crucial information about the sources and the medium. However, often multiple sources emit simultaneously, while the elastic properties of the medium can change over time. Unmixing and identifying the different processes in the seismograms is a complex task, which we try to solve with methods of artificial intelligence. In a completely data-driven fashion, we are able to mute the variation in the seismograms due to anthropogenic seismic sources and reveal a continuous medium change due to freezing and thawing. This approach could reveal hidden information in complex environments such as volcanoes, where many different source and medium processes occur.

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“…Distributed fiber optic sensing is a family of techniques that utilizes standard optical fibers to make measurements of local physical parameters including temperature (Tyler et al., 2009), static strain (Masoudi & Newson, 2016), and most recently low amplitude dynamic strain or strain rate (Lindsey & Martin, 2021). The last approach, referred to as distributed acoustic sensing (DAS), is an emerging technology that repurposes a fiber‐optic cable as a dense array of seismic sensors and in some environments is transforming seismic acquisition (Ajo‐Franklin et al., 2019; Cheng, Chi, et al., 2021, 2022; Daley et al., 2013; Dou et al., 2017; Lindsey et al., 2017; Martin et al., 2021; Zhan, 2020). DAS utilizes laser pulses to interferometrically measure minute extensional strains (or strain rates) over spatially continuous intervals along an optical fiber (Hartog, 2017), and has advantages of fine spatial resolutions down to the meter scale with linear extents from tens to hundreds of km, and broad bandwidth from the kHz range to quasi‐static depending on interrogator unit and measurement parameters (Lindsey et al., 2020; Paitz et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

et al. 2023

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The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low‐carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber‐optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ∼28‐km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high‐resolution two‐dimensional (2D) S wave velocity (Vs) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high Vs and low Vp/Vs ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

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Watching the Cryosphere Thaw: Seismic Monitoring of Permafrost Degradation Using Distributed Acoustic Sensing During a Controlled Heating Experiment

Cited by 20 publications

References 67 publications

AI‐Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns

AI‐Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns

AI-based unmixing of medium and source signatures from seismograms: ground freezing patterns

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

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