Neural Networks in Seismic Discrimination

Dowla, Farid

doi:10.1007/978-94-011-0419-7_41

Cited by 17 publications

(7 citation statements)

References 5 publications

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“…As seismic signals are inherently nonlinear and nonstationary [12] techniques such as artificial neural networks (ANN) [13] are being incorporated to perform classification. In many works, ANNs have outperformed traditional methods of analysis.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Using Circular Statistics Applied to Volcano Monitoring

San-Martin

Melgarejo

Gallegos

et al. 2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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In this work, the applicability of the circular statistics to feature extraction on seismic signals is presented. The seismic signals are captured from Llaima Volcano, located in Southern Andes Volcanic Zone at 38 • 40'S 71 • 40'W. Typically, the seismic signals can be divided in longperiod, tremor, and volcano-tectonic earthquakes. The seismic signals are time-segmented using a rectangular window of 1 minute of duration. In each segment, the instantaneous phase is calculated using the Hilbert Transform, and then, one feature is obtained. Thus, the principal hypothesis of this work is that the instantaneous phase can be assumed as a circular random variable in [0, 2π) interval. A second feature is obtained using the wavelet transform due to the fact that seismic signals present high energy located in low frequency. Then, in the range 1.55 and 3.11 Hz the wavelet coefficients were obtained and their mean energy is calculated as the second feature. Real seismic data represented using this two features are classified using a linear discriminant with a 92.5% of correct recognition rate.

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Section: Introductionmentioning

confidence: 99%

Feature Extraction Using Circular Statistics Applied to Volcano Monitoring

San-Martin

Melgarejo

Gallegos

et al. 2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…The ANN was found to be especially useful for accurate detection of the arrival time of the first break (i.e., a burst wave on a noisy background) in seismograms. Dowla chose ANNs because they easily incorporate nonlinearities into a solution, and they are easily adaptable and generalize well [22]. His report discusses a wide variety of ANNs that are applicable to the discrimination and classification of seismic data.…”

Section: Related Work On Anns For Supervised Classification Of Seismomentioning

confidence: 99%

Classification of Seismic Windows Using Artificial Neural Networks

Diersen

Lee

Spears³

et al. 2011

Procedia Computer Science

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We examine the plausibility of using an Artificial Neural Network (ANN) and an Importance-Aided Neural Network (IANN) for the refinement of the structural model used to create full-wave tomography images. Specifically, we apply the machine learning techniques to classifying segments of observed data wave seismograms and synthetic data wave seismograms as either usable for iteratively refining the structural model or not usable for refinement. Segments of observed and synthetic seismograms are considered usable if they are not too different, a heuristic observation made by a human expert, which is considered a match. The use of the ANN and the IANN for classification of the data wave segments removes the human computational cost of the classification process and removes the need for an expert to oversee all such classifications. Our experiments on the seismic data for Southern California have shown this technique to be promising for both classification accuracy and the reduction of the time required to compute the classification of observed data wave segment and synthetic data wave segment matches.

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“…The first applications of ML in seismology focused on discriminating the amplitude spectra of seismic waveforms of natural earthquakes and nuclear and chemical explosions (Dowla et al, 1990;Dysart & Pulli, 1990;Ren et al, 2020). ML has successfully been applied to discriminate between natural earthquakes and other types of seismic events (underground nuclear explosions, underwater explosions, volcano-tectonic events) for events recorded at local or regional distances (Romeo, 1994).…”

Section: Introductionmentioning

confidence: 99%

“…ML has successfully been applied to discriminate between natural earthquakes and other types of seismic events (underground nuclear explosions, underwater explosions, volcano-tectonic events) for events recorded at local or regional distances (Romeo, 1994). These earlier studies typically use limited training labels (e.g., on the order of a few tens or hundreds), and Artificial Neural Networks (ANNs) with shallow fully-connected feed-forward neural networks and simple recurrent networks (Dowla et al, 1990;Del Pezzo et al, 2003), which tend to limit their performances (e.g., up to 90% of classification accuracy), limit spatial application, and can be computationally intensive for standard CPU-based computations. More recent developments in deep learning (LeCun et al, 2015;Rouet-Leduc et al, 2017) opened doors for scientists in many fields to be able to utilize historical and relatively small dataset for classifying and discriminating seismic event types or phase determinations (Nakano et al, 2019;Bergen et al, 2019;Kong et al, 2019) with encouraging results.…”

Section: Introductionmentioning

confidence: 99%

Global Nuclear Blast Discrimination using a Convolutional Neural Network

Barama

Williams

Newman

et al. 2022

Preprint

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Using P-wave seismograms by Barama et al. (2022), we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P-wave, nuclear P-wave, and noise. Seismograms with low signal to noise ratios (SNR) adversely affect the model performance, thus a threshold was applied, limiting the training set size. Our method can accurately characterize most events, finding over 95\% signals in the validation set, even with the SNR-limited training data. We applied the model on holdout datasets of the North Korean test blasts to evaluate the performance on unique region and station-source pairs. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical blasts to act as a surrogate for nuclear blasts. We anticipate that machine-learning models like our classifier system can have broad application for other seismic signals including volcanic and non-volcanic tremor, anomalous earthquakes, ice-quakes or landslide-quakes.

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Neural Networks in Seismic Discrimination

Cited by 17 publications

References 5 publications

Feature Extraction Using Circular Statistics Applied to Volcano Monitoring

Feature Extraction Using Circular Statistics Applied to Volcano Monitoring

Classification of Seismic Windows Using Artificial Neural Networks

Global Nuclear Blast Discrimination using a Convolutional Neural Network

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