<i>P</i> Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning

Ross, Zachary E.; Meier, Men‐Andrin; Hauksson, Egill

doi:10.1029/2017jb015251

Cited by 366 publications

(203 citation statements)

References 36 publications

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“…Qian et al () used unsupervised deep convolutional autoencoder in seismic facies clustering. Ross et al () trained CNNs for earthquake P wave arrival picking and first‐motion polarity determination. Z. Li et al () constructed an earthquake P wave discriminator by using generative adversarial networks and Random Forests.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Chen

Zhang

Bai

et al. 2019

Earth and Space Science

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Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking.

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Section: Introductionmentioning

confidence: 99%

“…Qian et al (2018) used unsupervised deep convolutional autoencoder in seismic facies clustering. Ross et al (2018) trained CNNs for earthquake P wave arrival picking and first-motion polarity determination. Z.…”

Section: Introductionmentioning

confidence: 99%

Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Chen

Zhang

Bai

et al. 2019

Earth and Space Science

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“…The effort in creating an annotated data subset, despite being time and labor consuming, is an overhead but as we show can be outweighed by the benefits of better analysis results. For data annotation, two distinct approaches can be followed: Ruano et al (2014) and Kislov and Gravirov (2017), inherently contain a limitation as this approach requires that events as well as influencing factors must be identified and identifiable in the signal of concern. This is especially hard where no ground truth information except (limited) experience by professionals is available.…”

Section: Classification Of Negative Examplesmentioning

confidence: 99%

Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks

et al. 2019

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Passive monitoring of ground motion can be used for geophysical process analysis and natural hazard assessment. Detecting events in microseismic signals can provide responsive insights into active geophysical processes. However, in the raw signals, microseismic events are superimposed by external influences, for example, anthropogenic or natural noise sources that distort analysis results. In order to be able to perform event-based geophysical analysis with such microseismic data records, it is imperative that negative influence factors can be systematically and efficiently identified, quantified and taken into account. Current identification methods (manual and automatic) are subject to variable quality, inconsistencies or human errors. Moreover, manual methods suffer from their inability to scale to increasing data volumes, an important property when dealing with very large data volumes as in the case of long-term monitoring.In this work, we present a systematic strategy to identify a multitude of external influence sources, characterize and quantify their impact and develop methods for automated identification in microseismic signals. We apply the strategy developed to a real-world, multi-sensor, multi-year microseismic monitoring experiment performed at the Matterhorn Hörnligrat (Switzerland). We develop and present an approach based on convolutional neural networks for microseismic data to detect external influences originating in mountaineers, a major unwanted influence, with an error rate of less than 1 %, 3 times lower than comparable algorithms. Moreover, we present an ensemble classifier for the same task, obtaining an error rate of 0.79 % and an F1 score of 0.9383 by jointly using time-lapse image and microseismic data on an annotated subset of the monitoring data. Applying these classifiers to the whole experimental dataset reveals that approximately one-fourth of events detected by an event detector without such a preprocessing step are not due to seismic activity but due to anthropogenic influences and that time periods with mountaineer activity have a 9 times higher event rate. Due to these findings, we argue that a systematic identification of external influences using a semi-automated approach and machine learning techniques as presented in this paper is a prerequisite for the qualitative and quantitative analysis of long-term monitoring experiments.Published by Copernicus Publications on behalf of the European Geosciences Union.

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“…In seismology, deep learning has recently been used to detect and roughly locate earthquakes in Oklahoma [20], and to determine P-wave arrival times and first-motion polarities [21]. Here we develop a convolutional neural network that can detect and classify seismic body wave phases over a broad range of circumstances.…”

Section: Introductionmentioning

confidence: 99%

Generalized Seismic Phase Detection with Deep Learning

Ross

Meier

Hauksson

et al. 2018

Bulletin of the Seismological Society of America

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336

231

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To optimally monitor earthquake-generating processes, seismologists have sought to lower detection sensitivities ever since instrumental seismic networks were started about a century ago. Recently, it has become possible to search continuous waveform archives for replicas of previously recorded events (template matching), which has led to at least an order of magnitude increase in the number of detected earthquakes and greatly sharpened our view of geological structures. Earthquake catalogs produced in this fashion, however, are heavily biased in that they are completely blind to events for which no templates are available, such as in previously quiet regions or for very large magnitude events. Here we show that with deep learning we can overcome such biases without sacrificing detection sensitivity. We trained a convolutional neural network (ConvNet) on the vast hand-labeled data archives of the Southern California Seismic Network to detect seismic body wave phases. We show that the ConvNet is extremely sensitive and robust in detecting phases, even when masked by high background noise, and when the ConvNet is applied to new data that is not represented in the training set (in particular, very large magnitude events). This generalized phase detection (GPD) framework will significantly improve earthquake monitoring and catalogs, which form the underlying basis for a wide range of basic and applied seismological research.

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P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning

Cited by 366 publications

References 36 publications

Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks

Generalized Seismic Phase Detection with Deep Learning

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