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Automatic Detection of Seismic Signals

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 32179

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Dr. Sergio Molina Palacios

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Department of Applied Physics, Faculty of Sciences, University of Alicante, Crta, San Vicente del Raspeig, s/n, 03080 Alicante, Spain
Interests: seismology; seismic hazard; vulnerability and seismic risk
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Juan Jose Galiana-Merino

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Guest Editor

Department of Physics, Systems Engineering and Signal Theory, University of Alicante, Crta. San Vicente del Raspeig, s/n, 03080 Alicante, Spain
Interests: seismology; seismic data acquisition; signal processing; near surface geophysics; wavelets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Automatic detection and picking of seismic signals is crucial for seismic networks, which continuously monitor and work with huge volumes of data. In this situation, manual picking is tedious work in which some small events can go unnoticed and others can produce false alarms.

Accordingly, automatic picking algorithms are in constant development. New methodologies based on energy analysis, artificial neural networks, maximum likelihood methods, fuzzy logic theory, polarization analysis, hidden Markov models, autoregressive techniques, higher order statistics, wavelet transform, or template matching, among others, are continuously being investigated.

Accurate and reliable identification and detection of seismic phases is essential for subsequent real-time analysis. The information contained in the different seismic phases allows the expected magnitude, the epicentral location of an event, and other parameters that might be used by earthquake early-warning systems to be estimated.

The aim of this Special Issue is to present the most recent advances in the automatic detection and phase picking of seismic signals. Topics related to this Special Issue of Sensors include, but are not limited to:

Automatic seismic event detection;
Accurate seismic phase picking;
Real-time processing of seismic signals;
New methodologies for the automatic estimation of earthquake parameters;
Monitoring and early-warning systems.

Dr. Sergio Molina Palacios
Prof. Dr. Juan Jose Galiana-Merino
Guest Editors

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Automatic Detection of Seismic Signals—Second Edition in Sensors (2 articles)

Published Papers (10 papers)

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17 pages, 6438 KiB

Open AccessArticle

Feature Extraction of a Non-Stationary Seismic–Acoustic Signal Using a High-Resolution Dyadic Spectrogram

by Diego Seuret-Jiménez, Eduardo Trutié-Carrero, José Manuel Nieto-Jalil, Erick Daniel García-Aquino, Lorena Díaz-González, Laura Carballo-Sigler, Daily Quintana-Fuentes and Luis Manuel Gaggero-Sager

Sensors 2023, 23(13), 6051; https://doi.org/10.3390/s23136051 - 30 Jun 2023

Cited by 2 | Viewed by 2613

Abstract

Using a novel mathematical tool called the

T_{e}

-gram, researchers analyzed the energy distribution of frequency components in the scale–frequency plane. Through this analysis, a frequency band of approximately 12 Hz is identified, which can be isolated without distorting its constituent frequencies. [...] Read more.

Using a novel mathematical tool called the

T_{e}

T_{e}

-gram successfully addresses this knowledge gap, providing multi-sensitivity in the frequency domain and effectively attenuating cross-term energy. The Daubechies 45 wavelet function was employed due to its exceptional 150 dB attenuation in the rejection band. The validation process encompassed three stages: pre-, during-, and post-seismic activity. The utilized signal corresponds to the 19 September 2017 earthquake, occurring between the states of Morelos and Puebla, Mexico. The results showcased the impressive ability of the

T_{e}

-gram to surpass expectations in terms of sensitivity and energy distribution within the frequency domain. The

T_{e}

-gram outperformed the procedures documented in the existing literature. On the other hand, the results show a frequency band between 0.7 Hz and 1.75 Hz, which is named the planet Earth noise. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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13 pages, 14587 KiB

Open AccessArticle

Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space

by Yunyue Elita Li, Enhedelihai Alex Nilot, Yumin Zhao and Gang Fang

Sensors 2023, 23(3), 1322; https://doi.org/10.3390/s23031322 - 24 Jan 2023

Cited by 1 | Viewed by 2097

Abstract

Earth’s surface is constantly vibrating due to natural processes inside and human activities on the surface of the Earth. These vibrations form the ambient seismic fields that are measured by sensitive seismometers. Compared with natural processes, anthropogenic vibrations dominate the seismic measurements at higher frequency bands, demonstrate clear temporal and cyclic variability, and are more heterogeneous in space. Consequently, urban ambient seismic fields are a rich information source for human activity monitoring. Improving from the conventional energy-based seismic spectral analysis, we utilize advanced signal processing techniques to extract the occurrence of specific urban activities, including motor vehicle counts and runner activities, from the high-frequency ambient seismic noise. We compare the seismic energy in different frequency bands with the extracted activity intensity at different locations within a one-kilometer radius and highlight the high-resolution information in the seismic data. Our results demonstrate the intense heterogeneity in a highly developed urban space. Different sectors of urban society serve different functions and respond differently when urban life is severely disturbed by the impact of the COVID-19 pandemic in 2020. The anonymity of seismic data enabled an unprecedented spatial and temporal resolution, which potentially could be utilized by government regulators and policymakers for dynamic monitoring and urban management. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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23 pages, 1093 KiB

Open AccessArticle

Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

by Jiangfeng Li, Lina Stankovic, Vladimir Stankovic, Stella Pytharouli, Cheng Yang and Qingjiang Shi

Sensors 2023, 23(1), 243; https://doi.org/10.3390/s23010243 - 26 Dec 2022

Cited by 2 | Viewed by 1564

Abstract

Slope instabilities caused by heavy rainfall, man-made activity or earthquakes can be characterised by seismic events. To minimise mortality and infrastructure damage, a good understanding of seismic signal properties characterising slope failures is therefore crucial to classify seismic events recorded from continuous recordings effectively. However, there are limited contributions towards understanding the importance of feature selection for the classification of seismic signals from continuous noisy recordings from multiple channels/sensors. This paper first proposes a novel multi-channel event-detection scheme based on Neyman–Pearson lemma and Multi-channel Coherency Migration (MCM) on the stacked signal across multi-channels. Furthermore, this paper adapts graph-based feature weight optimisation as feature selection, exploiting the signal’s physical characteristics, to improve signal classification. Specifically, we alternatively optimise the feature weight and classification label with graph smoothness and semidefinite programming (SDP). Experimental results show that with expert interpretation, compared with the conventional short-time average/long-time average (STA/LTA) detection approach, our detection method identified 614 more seismic events in five days. Furthermore, feature selection, especially via graph-based feature weight optimisation, provides more focused feature sets with less than half of the original number of features, at the same time enhancing the classification performance; for example, with feature selection, the Graph Laplacian Regularisation classifier (GLR) raised the rockfall and slide quake sensitivities to 92% and 88% from 89% and 85%, respectively. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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14 pages, 6130 KiB

Open AccessArticle

First Arrival Picking of Zero-Phase Seismic Data by Hilbert Envelope Empirical Half Window (HEEH) Method

by Amen Bargees and Abdullatif A. Al-Shuhail

Sensors 2022, 22(19), 7580; https://doi.org/10.3390/s22197580 - 6 Oct 2022

Cited by 9 | Viewed by 2401

Abstract

First arrival travel time picking is an important step in many seismic data-processing applications. Most first arrival picking methods search for a sudden jump in seismic energy at trace onsets, which is clearly appropriate for minimum-phase data. This paper proposes a method for the first arrival picking of non-minimum phase data based on complex trace analysis. The Hilbert integral transform generates a complex seismic trace, followed by extraction of the envelope. The first arrival identification introduces an outlier detection method that uses the widely used three-sigma rule of thumb, which is commonly used in most software algorithms to identify outliers. The proposed method ultimately generates logical windows of ones (at the locations of outliers) and zeros (elsewhere). The first arrival is selected in the middle of the first outlier window. Testing the proposed method on zero-phase synthetic data with added 10% and 20% random noise, the method detected the true first arrivals accurately. Furthermore, tests on real Vibroseis data showed that the method recognizes the first arrivals with 67% accuracy within 20 milliseconds of their corresponding arrival times manually picked by an experienced geophysicist. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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16 pages, 2400 KiB

Open AccessArticle

An Early Warning System for Earthquake Prediction from Seismic Data Using Batch Normalized Graph Convolutional Neural Network with Attention Mechanism (BNGCNNATT)

by Muhammad Atif Bilal, Yanju Ji, Yongzhi Wang, Muhammad Pervez Akhter and Muhammad Yaqub

Sensors 2022, 22(17), 6482; https://doi.org/10.3390/s22176482 - 28 Aug 2022

Cited by 11 | Viewed by 5532

Abstract

Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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24 pages, 8963 KiB

Open AccessArticle

Raspberry Shake-Based Rapid Structural Identification of Existing Buildings Subject to Earthquake Ground Motion: The Case Study of Bucharest

by Ali Güney Özcebe, Alexandru Tiganescu, Ekin Ozer, Caterina Negulescu, Juan Jose Galiana-Merino, Enrico Tubaldi, Dragos Toma-Danila, Sergio Molina, Alireza Kharazian, Francesca Bozzoni, Barbara Borzi and Stefan Florin Balan

Sensors 2022, 22(13), 4787; https://doi.org/10.3390/s22134787 - 24 Jun 2022

Cited by 10 | Viewed by 4718

Abstract

The Internet of things concept empowered by low-cost sensor technologies and headless computers has upscaled the applicability of vibration monitoring systems in recent years. Raspberry Shake devices are among those systems, constituting a crowdsourcing framework and forming a worldwide seismic network of over a thousand nodes. While Raspberry Shake devices have been proven to densify seismograph arrays efficiently, their potential for structural health monitoring (SHM) is still unknown and is open to discovery. This paper presents recent findings from existing buildings located in Bucharest (Romania) equipped with Raspberry Shake 4D (RS4D) devices, whose signal recorded under multiple seismic events has been analyzed using different modal identification algorithms. The obtained results show that RS4D modules can capture the building vibration behavior despite the short-duration and low-amplitude excitation sources. Based on 15 RS4D device readings from five different multistorey buildings, the results do not indicate damage in terms of modal frequency decay. The findings of this research propose a baseline for future seismic events that can track the changes in vibration characteristics as a consequence of future strong earthquakes. In summary, this research presents multi-device, multi-testbed, and multi-algorithm evidence on the feasibility of RS4D modules as SHM instruments, which are yet to be explored in earthquake engineering. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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18 pages, 38316 KiB

Open AccessArticle

Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events

by Jiaxuan Leng, Zhichao Yu, Zhonghua Mao and Chuan He

Sensors 2022, 22(11), 4065; https://doi.org/10.3390/s22114065 - 27 May 2022

Cited by 6 | Viewed by 1855

Abstract

Arrival-time picking is a critical step in microseismic data processing, and thus the quality control of arrival results is necessary. Conventional picking methods may be inaccurate or inconsistent due to varied signal-to-noise ratios (SNR) and waveform patterns of the events recorded in different time sections. To address this issue, we propose a quality assessment method based on waveform similarity coefficients to evaluate arrival results and also a global optimization algorithm based on iterative cross-correlation to refine arrival times. The recordings after moveout correction are applied to calculate the intra-event and inter-event waveform coefficients for the quality assessment of arrival results. The residual time differences of intra-event and inter-event traces are calculated sequentially using an enhanced iterative cross-correlation method. In addition, the stacked waveform of each event after the intra-event residual time correction is introduced for global optimization to obtain the inter-event residual time discrepancies. We use both synthetic data and field data to validate the proposed method. The results indicate that the proposed method yields more robust and reliable results. The quality assessment of the optimized arrivals is greatly enhanced compared to the adjusted picks obtained from single event-based processing methods. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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11 pages, 4092 KiB

Open AccessArticle

Microseismic Time Delay Estimation Method Based on Continuous Wavelet

by Cunpeng Du, Shengwen Yu, Haitao Yin and Zhen Sun

Sensors 2022, 22(8), 2845; https://doi.org/10.3390/s22082845 - 7 Apr 2022

Cited by 7 | Viewed by 2140

Abstract

The microseismic signal is easily affected by observation noise and the inaccurate estimation of traditional methods will seriously reduce the location accuracy of the microseismic event. Therefore, based on the continuous wavelet spectrum and the similarity coefficient, a fast and efficient microseismic time delay estimation method is proposed. Firstly, the original signals are denoised by continuous wavelet transform. Subsequently, the time-frequency transform of the original signal by continuous wavelet transform, time-frequency signal extraction is the process of band-pass filtering, which can further reduce the influence of noise interference on the time delay estimation. Finally, we calculated the similarity between the time-frequency signals via the time domain and frequency domain integration. The similarity function is based on correlation and proposed according to the time-frequency transformation provided by the phase spectrum to evaluate the similarity between two noisy signals. The time delay estimation is determined by searching for the similarity function peak. The experimental results show the precision and accuracy of the method over the cross-correlation method and generalized cross-correlation phase transformation method, especially when the signal-to-noise ratio is low. Therefore, a new time delay estimation method for non-stationary random signals is presented in this paper. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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15 pages, 2734 KiB

Open AccessArticle

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking

by Kang Peng, Hongyang Guo and Xueyi Shang

Sensors 2021, 21(16), 5271; https://doi.org/10.3390/s21165271 - 4 Aug 2021

Cited by 22 | Viewed by 2867

Abstract

Signal denoising is one of the most important issues in signal processing, and various techniques have been proposed to address this issue. A combined method involving wavelet decomposition and multiscale principal component analysis (MSPCA) has been proposed and exhibits a strong signal denoising performance. This technique takes advantage of several signals that have similar noises to conduct denoising; however, noises are usually quite different between signals, and wavelet decomposition has limited adaptive decomposition abilities for complex signals. To address this issue, we propose a signal denoising method based on ensemble empirical mode decomposition (EEMD) and MSPCA. The proposed method can conduct MSPCA-based denoising for a single signal compared with the former MSPCA-based denoising methods. The main steps of the proposed denoising method are as follows: First, EEMD is used for adaptive decomposition of a signal, and the variance contribution rate is selected to remove components with high-frequency noises. Subsequently, the Hankel matrix is constructed on each component to obtain a higher order matrix, and the main score and load vectors of the PCA are adopted to denoise the Hankel matrix. Next, the PCA-denoised component is denoised using soft thresholding. Finally, the stacking of PCA- and soft thresholding-denoised components is treated as the final denoised signal. Synthetic tests demonstrate that the EEMD-MSPCA-based method can provide good signal denoising results and is superior to the low-pass filter, wavelet reconstruction, EEMD reconstruction, Hankel–SVD, EEMD-Hankel–SVD, and wavelet-MSPCA-based denoising methods. Moreover, the proposed method in combination with the AIC picking method shows good prospects for processing microseismic waves. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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Review

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23 pages, 1536 KiB

Open AccessReview

Real-Time Seismic Intensity Measurements Prediction for Earthquake Early Warning: A Systematic Literature Review

by Zhenpeng Cheng, Chaoyong Peng and Meirong Chen

Sensors 2023, 23(11), 5052; https://doi.org/10.3390/s23115052 - 25 May 2023

Cited by 3 | Viewed by 4632

Abstract

With the gradual development of and improvement in earthquake early warning systems (EEWS), more accurate real-time seismic intensity measurements (IMs) methods are needed to assess the impact range of earthquake intensities. Although traditional point source warning systems have made some progress in terms of predicting earthquake source parameters, they are still inadequate at assessing the accuracy of IMs predictions. In this paper, we aim to explore the current state of the field by reviewing real-time seismic IMs methods. First, we analyze different views on the ultimate earthquake magnitude and rupture initiation behavior. Then, we summarize the progress of IMs predictions as they relate to regional and field warnings. The applications of finite faults and simulated seismic wave fields in IMs predictions are analyzed. Finally, the methods used to evaluate IMs are discussed in terms of the accuracy of the IMs measured by different algorithms and the cost of alerts. The trend of IMs prediction methods in real time is diversified, and the integration of various types of warning algorithms and of various configurations of seismic station equipment in an integrated earthquake warning network is an important development trend for future EEWS construction. Full article

(This article belongs to the Special Issue Automatic Detection of Seismic Signals)

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