A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms

Kriegerowski, Marius; Petersen, Gesa; Vasyura‐Bathke, Hannes; Ohrnberger, Matthias

doi:10.1785/0220180320

Cited by 112 publications

(77 citation statements)

References 17 publications

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“…In seismology, CNN models have been recently applied for detection (Ross et al 2018a,b;Zhu & Beroza 2018;Dokht et al 2019;Woollam et al 2019) and association (McBrearty et al 2019) of P and S wave arrivals, as well as for earthquake localization (Kriegerowski et al 2019;Perol et al 2018;Zhang et al 2020). RNN-based networks have been used for predicting approximate earthquake times and locations (Panakkat & Adeli 2009), and for seismic phase association (Ross et al 2019b).…”

Section: Overcome This Constraint By Simplisticallymentioning

confidence: 99%

DeepPhasePick: A method for Detecting and Picking Seismic Phases from Local Earthquakes based on highly optimized Convolutional and Recurrent Deep Neural Networks

Soto¹,

Schurr²

2020

Preprint

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Seismic phase detection, identification and first-onset picking are basic but essential routines to analyse earthquake data. As both the number of seismic stations, globally and regionally, and the number of experiments greatly increase due to ever greater availability of instrumentation, automated data processing becomes more and more essential. E.g., for modern seismic experiments involving 100s to even 1,000s instruments, conventional human analyst-based identification and picking of seismic phases is becoming unfeasible, and the introduction of automatic algorithms mandatory. In this paper, we introduce DeepPhasePick, an automatic two-stage method that detects and picks P and S seismic phases from local earthquakes. The method is entirely based on highly optimized deep neural networks, consisting of a first stage that detects the phases using a convolutional neural network, and a second stage that uses two recurrent neural networks to pick both phases. Detection is performed on three-component seismograms. P-and S-picking is then conducted on the vertical and the two-horizontal components, respectively. Systematic hyperparameter optimization was applied to select the best model architectures and to define both the filter applied to preprocess the seismic data as well as the characteristics of the window sample used to feed the models. We trained DeepPhasePick using seismic records extracted from two sets of manually-picked event waveforms originating from northern Chile (∼39,000 records for detection and ∼36,000 records for picking). In dif-Page 1 of 60 Geophysical Journal International 2 H. Soto & B. Schurr ferent tectonic regimes, DeepPhasePick demonstrated the ability to both detect P and S phases from local earthquakes with high accuracy, as well as predict P-and S-phase time onsets with an analyst level of precision. DeepPhasePick additionally computes onset uncertainties based on the Monte Carlo Dropout technique as an approximation of Bayesian inference. This information can then further feed an associator algorithm in an earthquake location procedure.

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Section: Overcome This Constraint By Simplisticallymentioning

confidence: 99%

DeepPhasePick: A method for Detecting and Picking Seismic Phases from Local Earthquakes based on highly optimized Convolutional and Recurrent Deep Neural Networks

Soto¹,

Schurr²

2020

Preprint

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“…This approach is also able to return estimates of location errors. Kriegerowski et al [122] trained a neural network to locate earthquakes from multiple stations during an earthquake swarm in West Bohemia. The network takes as input the orthogonal components of all stations, and returns the depth, east and north source coordinates.…”

Section: Event Locationmentioning

confidence: 99%

Machine learning and fault rupture: a review

Ren¹,

Hulbert²,

Johnson³

et al. 2020

Preprint

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Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to Solid Earth.

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“…The network in [31], handles three component seismic records of multiple stations, and after training the first convolutional layer becomes sensitive to characteristic features of seismic waveforms. Thus, this layer can behave as an event detector by itself.…”

Section: Cnnmentioning

confidence: 99%

Artificial Neural Networks as Emerging Tools for Earthquake Detection

Rojas

Otero

Alvarado

et al. 2019

CyS

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As seismic networks continue to spread and monitoring sensors become more efficient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identification, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recent advances are related to the application of Artificial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.

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A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms

Cited by 112 publications

References 17 publications

DeepPhasePick: A method for Detecting and Picking Seismic Phases from Local Earthquakes based on highly optimized Convolutional and Recurrent Deep Neural Networks

DeepPhasePick: A method for Detecting and Picking Seismic Phases from Local Earthquakes based on highly optimized Convolutional and Recurrent Deep Neural Networks

Machine learning and fault rupture: a review

Artificial Neural Networks as Emerging Tools for Earthquake Detection

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