Automatic classification of seismic events within a regional seismograph network

Kortström, Jari; Uski, Marja; Tiira, Timo

doi:10.1016/j.cageo.2015.11.006

Cited by 92 publications

(36 citation statements)

References 17 publications

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“…However, automatic identification remains challenging, also due to the fact that there is a range of explosion source types and wave propagation differences across the area. In Finland, Kortström et al (2016) have recently developed an automatic classification system based on a machine learning approach.…”

Section: Discrimination Of Explosionsmentioning

confidence: 99%

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Seismic Monitoring and Data Processing at the Norwegian National Seismic Network

Ottemöller¹,

Strømme²,

Storheim³

2018

Summ. Bull. Internatl. Seismol. Cent.

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Section: Discrimination Of Explosionsmentioning

confidence: 99%

“…Applying the same algorithm as Kortström et al (2016) for Norwegian data has so far only been partly successful. Instead, the focus is currently on the use of spectrogram plotting as part of the routine processing and this has been implemented into SEISAN ( Figure 5.9).…”

Section: Discrimination Of Explosionsmentioning

confidence: 99%

Seismic Monitoring and Data Processing at the Norwegian National Seismic Network

Ottemöller¹,

Strømme²,

Storheim³

2018

Summ. Bull. Internatl. Seismol. Cent.

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“…Additionally, the performance of our similarity-based discriminator is directly compared to that of two state-of-the-art methods: the SVM-based discriminator proposed in (Kortstrm et al, 2016) and the SRSpec-CNN discriminator adapted from the work of (Nakano et al, 2019). In particular, our SVM and CNN implementations both utilize the full 149,036 training waveforms from the training set.…”

Section: Evaluation Criteriamentioning

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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“…The self-learning approach presented here is flexible and easily adjustable to the requirements of a denser or wider high-frequency network. [20].…”

Section: Other's Applied Workmentioning

confidence: 99%

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

Ghorbani

Barari

Hosseini

2017

cej

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Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world. Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ،C4.5 decision tree and KNN near neighbourhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting pre-processing and separating training and testing samples, 4) generating relevant models with training samples and detecting and clustering test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.

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Automatic classification of seismic events within a regional seismograph network

Cited by 92 publications

References 17 publications

Seismic Monitoring and Data Processing at the Norwegian National Seismic Network

Seismic Monitoring and Data Processing at the Norwegian National Seismic Network

Beyond Correlation: A Path‐Invariant Measure for Seismogram Similarity

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

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