DeepDetect: A Cascaded Region-Based Densely Connected Network for Seismic Event Detection

Wu, Yue; Lin, Youzuo; Zhou, Zheng; Bolton, David; Ji, Liu; Johnson, P. A.

doi:10.1109/tgrs.2018.2852302

Cited by 111 publications

(53 citation statements)

References 35 publications

(51 reference statements)

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“…follow the framework proposed by Gibbons and Ringdal [12] and the parameters are chosen similar to the work completed by Wu et al [1]. To reduce the effect of randomness, the technique of k-fold cross validation is used in the experimental stage.…”

Section: Time Domainmentioning

confidence: 99%

Earthquake Detection in 1D Time‐Series Data with Feature Selection and Dictionary Learning

Zhou

Lin

Zhang

et al. 2019

Seismological Research Letters

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Earthquakes can be detected by matching spatial patterns or phase properties from 1-D seismic waves. Current earthquake detection methods, such as waveform correlation and template matching, have difficulty detecting anomalous earthquakes that are not similar to other earthquakes. In recent years, machine-learning techniques for earthquake detection have been emerging as a new active research direction.In this paper, we develop a novel earthquake detection method based on dictionary learning. Our detection method first generates rich features via signal processing and statistical methods, and further employs feature selection techniques to choose features that carry the most significant information. Based on these selected features, we build a dictionary for classifying earthquake events from non-earthquake events. To evaluate the performance of our dictionary-based detection methods, we test our method on a labquake dataset from Penn State University, which contains 3,357,566 time series data points with a 400 MHz sampling rate. 1,000 earthquake events are manually labeled in total, and the length of these earthquake events varies from 74 to 7151 data points. Through comparison to other detection methods, we show that our feature selection and dictionary learning incorporated earthquake detection method achieves a 80.1% prediction accuracy and outper-

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Section: Time Domainmentioning

confidence: 99%

Earthquake Detection in 1D Time‐Series Data with Feature Selection and Dictionary Learning

Zhou

Lin

Zhang

et al. 2019

Seismological Research Letters

Self Cite

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“…Recent ANN applications to subsurface imaging claim to overcome these deficiencies, using seismic data as input to identify important structures. In particular, [39] uses earthquake data to accurately predict 1-D velocity models, and applications in [5,34,27,53] employ data collected in seismic surveys for structural model building with interest in hydrocarbon exploration. In addition, the study in [37] applies a ANN to infer the prior distribution of acoustic properties of a geological model, that is later improved by full waveform inversion.…”

Section: Machine Learning Applications To Earthquake Datamentioning

confidence: 99%

“…A densely connected CNN to capture laboratory slip events of different durations is given in [53]. This network presents a cascaded architecture to generate multi-scale slip proposals and detect events with various lengths.…”

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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“…Deep learning is a subdiscipline of machine learning that is based on training neural networks to learn generalized representations of extremely large data sets and has become state of the art in numerous domains of artificial intelligence (LeCun et al, ), including natural language processing (Sutskever et al, ), computer vision (Krizhevsky et al, ), and speech recognition (Amodei et al, ). It has been recently introduced to seismology and has already shown considerable promise in performing various tasks including similarity‐based earthquake detection and localization (Perol et al, ), generalized seismic phase detection (Ross, Meier, Hauksson, & Heaton, ), phase picking (Zhu & Beroza, ), first‐motion polarity determination (Ross, Meier, & Hauksson, ), detection of events in laboratory experiments (Wu et al, ), seismic image sharpening (Lu et al, ), wavefield simulation (Moseley et al, ), and predicting aftershock spatial patterns (DeVries et al, ).…”

Section: Introductionmentioning

confidence: 99%

PhaseLink: A Deep Learning Approach to Seismic Phase Association

et al. 2019

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Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid-free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences of P and S wave arrival times generated using a 1-D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real-time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state-of-the-art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real-time seismic monitoring, and streamline automated processing of large seismic data sets.

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DeepDetect: A Cascaded Region-Based Densely Connected Network for Seismic Event Detection

Cited by 111 publications

References 35 publications

Earthquake Detection in 1D Time‐Series Data with Feature Selection and Dictionary Learning

Earthquake Detection in 1D Time‐Series Data with Feature Selection and Dictionary Learning

Artificial Neural Networks as Emerging Tools for Earthquake Detection

PhaseLink: A Deep Learning Approach to Seismic Phase Association

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