Machine Learning in Seismology: Turning Data into Insights

Kong, Qingkai; Trugman, Daniel T.; Ross, Zachary E.; Bianco, Michael J.; Meade, Brendan J.; Gerstoft, Peter

doi:10.1785/0220180259

Cited by 358 publications

(194 citation statements)

References 86 publications

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“…Deep Neural Networks are now being used across many areas of seismological research, from earthquake detection to earthquake early warning systems, ground-motion prediction, seismic tomography, and even earthquake geodesy (Kong et al, 2018). However, no effort has been made to date to use deep neural networks to build a seismogram similarity metric.…”

Section: Deep Seismogram Similaritymentioning

confidence: 99%

Beyond Correlation: A Path‐Invariant Measure for Seismogram Similarity

Dickey

Borghetti

Junek

et al. 2019

Seismological Research Letters

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Similarity search is a popular technique for seismic signal processing, with template matching, matched filters and subspace detectors being utilized for a wide variety of tasks, including both signal detection and source discrimination. Traditionally, these techniques rely on the cross-correlation function as the basis for measuring similarity. Unfortunately, seismogram correlation is dominated by path effects, essentially requiring a distinct waveform template along each path of interest. To address this limitation, we propose a novel measure of seismogram similarity that is explicitly invariant to path. Using Earthscope's USArray experiment, a path-rich dataset of 207,291 regional seismograms across 8,452 unique events is constructed, and then employed via the batch-hard triplet loss function, to train a deep convolutional neural network which maps raw seismograms to a low dimensional embedding space, where nearness on the space corresponds to nearness of source function, regardless of path or recording instrumentation. This path-agnostic embedding space forms a new representation for seismograms, characterized by robust, source-specific features, which we show to be useful for performing both pairwise event association as well as templatebased source discrimination with a single template.

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Section: Deep Seismogram Similaritymentioning

confidence: 99%

Beyond Correlation: A Path‐Invariant Measure for Seismogram Similarity

Dickey

Borghetti

Junek

et al. 2019

Seismological Research Letters

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“…Machine learning (ML) techniques 8,9 have enabled broad advances in automated data processing and pata) mbianco@ucsd.edu tern recognition capabilities across many fields, including computer vision, image processing, speech processing, and (geo)physical science. 10,11 ML in acoustics is a rapidly developing field, with many compelling solutions to the aforementioned acoustics challenges. The potential impact of ML-based techniques in the field of acoustics, and the recent attention they have received, motivates this review.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

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352

116

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Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes.

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“…Nabian and Meidani (2018) presented a deep learning framework for seismic reliability analysis of transportation network. Kong et al (2018) provided an overview of the development and application of machine learning in seismology modeling and analysis. Woollam, Rietbrock, Bueno, and De Angelis (2019) applied the convolutional neural network to classify seismic location and severity.…”

Section: Introductionmentioning

confidence: 99%

“…Woollam, Rietbrock, Bueno, and De Angelis (2019) applied the convolutional neural network to classify seismic location and severity. Kong et al (2018) provided an overview of the development and application of machine learning in seismology modeling and analysis.…”

Section: Introductionmentioning

confidence: 99%

Broad learning for nonparametric spatial modeling with application to seismic attenuation

Kuok

Yuen

2019

Computer aided Civil Eng

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Spatial modeling is a core element in geographical information science. It incorporates geographic information to construct the relationship for interpreting the behavior of spatial phenomena. In this paper, a broad learning framework for nonparametric spatial modeling is presented. Broad learning overcomes the obstacle of expensive computational consumption in deep learning and provides a powerful computationally efficient alternative. In contrast to the deep learning architecture that is configured with stacks of hierarchical layers, broad learning networks are established in a flat manner that can be flexibly reconfigured with the inherited information from the trained network. To develop the broad learning network, a simple prototype network is established as the initial trial and it is modified incrementally to enhance its data fitting capacity. Consequently, complex relationship of unstructured spatial data can be modeled efficiently. To demonstrate the efficacy and applicability of the broad learning framework, we will present a simulated example and a real application using the strong ground motion records on the 2008 great Wenchuan earthquake.

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Machine Learning in Seismology: Turning Data into Insights

Cited by 358 publications

References 86 publications

Beyond Correlation: A Path‐Invariant Measure for Seismogram Similarity

Beyond Correlation: A Path‐Invariant Measure for Seismogram Similarity

Machine learning in acoustics: Theory and applications

Broad learning for nonparametric spatial modeling with application to seismic attenuation

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