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Data Acquisition and Analysis of Seismic Noise

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 12646

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Special Issue Editors

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

Dr. Gerardo Alguacil De La Blanca

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Guest Editor

Andalusian Institute of Geophysics, University of Granada, Granada, Spain
Interests: earthquake and volcanic seismology; seismic instruments; seismic signals processing; engineering seismology; seismic noise

Dr. Antonio Garcia Jerez

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Guest Editor

Department of Physics and Chemistry, University of Almeria, Almeria, Spain
Interests: seismology; earthquake engineering; ambient noise imaging; seismic data acquisition; elastic waves propagation; seismic hazard assessment

Special Issue Information

Dear Colleagues,

The record and analysis of seismic noise have become a suitable alternative to geotechnical techniques in the last several decades. The appropriate analysis of this noise allows the characterization of the soil and provides valuable information to predict the soil response in case of an earthquake.

Ambient noise measurements require the deployment of one or several sensors on the ground surface, together with a multi-channel digitizer for registering three or more signals simultaneously. In this research line, the growing development of microcontrollers and open-source electronic platforms has led to the design and implementation of customized seismic recorders, as well as sensor networks.

While standard seismometers are sensitive enough to record ground noise, seismic zonation and microzonation studies often require the deployment of sensors at many points. Lightweight, low-power, and handy equipment is an important advantage for this use. For such instruments, a high sensitivity is still needed, but their dynamic range may be limited.

Regarding the analysis, single-station, inter-station, and array techniques (e.g., H/V spectral ratio, ellipticity analysis, frequency- and time-domain correlation, frequency-wavenumber methods in linear and 2D arrays, miniature array analysis) are the most common tools used for soil characterization, providing resonant frequencies and dispersion curves. Subsequently, these outputs can be inverted in order to estimate the shear-wave profile in the area under study.

We are inviting original research works covering novel seismic data acquisition systems (including sensors, digitizers, and sensor networks), innovative theories and methods related with seismic noise analysis and the inversion of the ground structure, and meaningful applications that can potentially lead to significant advances in the field of data acquisition and signal processing applied to seismic noise.

The aim of this Special Issue is to present the most recent advances in the acquisition and analysis of seismic noise related but not restricted to the following topics:

Wireless seismic sensor networks;
Seismic recorders;
Seismic data acquisition;
Seismic sensors;
Real-time seismic noise analysis;
New methodologies in seismic noise analysis;
Development of inversion techniques.

Dr. Juan Jose Galiana-Merino
Dr. Gerardo Alguacil De La Blanca
Dr. Antonio Garcia Jerez
Guest Editors

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Published Papers (5 papers)

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Research

28 pages, 5226 KiB

Open AccessArticle

Design and Implementation of a Wireless Recorder System for Seismic Noise Array Measurements

by Julio Antonio Jornet-Monteverde, Juan José Galiana-Merino and Juan Luis Soler-Llorens

Sensors 2022, 22(21), 8103; https://doi.org/10.3390/s22218103 - 22 Oct 2022

Cited by 1 | Viewed by 1904

Abstract

In this work, a wireless data acquisition system for seismic noise array measurements is presented. The developed system is composed of a series of nodes and a central server arranged in a point-to-multipoint topology. The nodes consist of a CC3200 microcontroller, an analog-to-digital converter, and a low-noise conditioning circuit designed specifically to register seismic noise, and which is connected to the seismic sensor. As a server, a Raspberry Pi 4B has been used that will receive the samples from the nodes via Wi-Fi and will save them in files. It also incorporates a Web interface developed with JavaScript node.js technology that allows to configure the number of nodes as well as different options, to start and stop the records, and to view in real time the different signals received from the nodes. The system can be deployed anywhere since each of the nodes use independent batteries as a power supply. In addition, it is possible to operate the system remotely if internet connectivity is available. The prototype has been tested in four different locations in the Alicante province (southeast Spain), demonstrating its suitability for seismic noise array measurements. Full article

(This article belongs to the Special Issue Data Acquisition and Analysis of Seismic Noise)

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17 pages, 8544 KiB

Open AccessArticle

Rayleigh-Wave Dispersion Analysis and Inversion Based on the Rotation

by Lixia Sun, Yun Wang and Xinming Qiu

Sensors 2022, 22(3), 983; https://doi.org/10.3390/s22030983 - 27 Jan 2022

Cited by 2 | Viewed by 1941

Abstract

Rotational observation is essential for a comprehensive description of the ground motion, and can provide additional wave-field information. With respect to the three typical layered models in shallow engineering geology, under the assumption of linear small deformation, we simulate the 2-dimensional radial, vertical, and rotational components of the wave fields and analyze the different characteristics of Rayleigh wave dispersion recorded for the rotational and translational components. Then, we compare the results of single-component inversion with the results of multi-component joint inversion. It is found that the rotational component has wider spectral bands and more higher modes than the translational components, especially at high frequencies; the rotational component has better anti-interference performance in the noisy data test, and it can improve the inversion accuracy of the shallow shear-wave velocity. The field examples also show the significant advantages of the joint utility of the translational and rotational components, especially when a low-velocity layer exists. Rotational observation shall be beneficial for shallow surface-wave exploration. Full article

(This article belongs to the Special Issue Data Acquisition and Analysis of Seismic Noise)

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21 pages, 10375 KiB

Open AccessArticle

Baseline Correction of Acceleration Data Based on a Hybrid EMD–DNN Method

by Zengshun Chen, Jun Fu, Yanjian Peng, Tuanhai Chen, LiKai Zhang and Chenfeng Yuan

Sensors 2021, 21(18), 6283; https://doi.org/10.3390/s21186283 - 19 Sep 2021

Cited by 6 | Viewed by 2660

Abstract

Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances of ground tilt, low-frequency noise caused by instruments, hysteresis of the transducer, etc., it would generate a baseline drift phenomenon in acceleration integration, failing to obtain an actual displacement response. The improved traditional baseline correction methods still have some problems, such as high baseline correction error, poor adaptability, and narrow application scope. This paper proposes a deep neural network model based on empirical mode decomposition (EMD–DNN) to solve baseline correction by removing the drifting trend. The feature of multiple time sequences that EMD obtains is extracted via DNN, achieving the real displacement time history of prediction. In order to verify the effectiveness of the proposed method, two natural waves (EL centro wave, Taft wave) and one Artificial wave are selected to test in a shaking table test. Comparing the traditional methods such as the least squares method, EMD, and DNN method, EMD–DNN has the best baseline correction effect in terms of the evaluation indexes: Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and degree of fit (R-Square). Full article

(This article belongs to the Special Issue Data Acquisition and Analysis of Seismic Noise)

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19 pages, 18522 KiB

Open AccessArticle

On the Utility of Horizontal-to-Vertical Spectral Ratios of Ambient Noise in Joint Inversion with Rayleigh Wave Dispersion Curves for the Large-N Maupasacq Experiment

by Maik Neukirch, Antonio García-Jerez, Antonio Villaseñor, Francisco Luzón, Jacques Brives and Laurent Stehly

Sensors 2021, 21(17), 5946; https://doi.org/10.3390/s21175946 - 04 Sep 2021

Viewed by 1850

Abstract

Horizontal-to-Vertical Spectral Ratios (HVSR) and Rayleigh group velocity dispersion curves (DC) can be used to estimate the shallow S-wave velocity (

V_{S}

) structure. Knowing the

V_{S}

structure is important for geophysical data interpretation either in order to better constrain data [...] Read more.

Horizontal-to-Vertical Spectral Ratios (HVSR) and Rayleigh group velocity dispersion curves (DC) can be used to estimate the shallow S-wave velocity (

V_{S}

) structure. Knowing the

V_{S}

structure is important for geophysical data interpretation either in order to better constrain data inversions for P-wave velocity (

V_{P}

) structures such as travel time tomography or full waveform inversions or to directly study the

V_{S}

structure for geo-engineering purposes (e.g., ground motion prediction). The joint inversion of HVSR and dispersion data for 1D

V_{S}

structure allows characterising the uppermost crust and near surface, where the HVSR data (

0.03

10 s

) are most sensitive while the dispersion data (1 to

30 s

) constrain the deeper model which would, otherwise, add complexity to the HVSR data inversion and adversely affect its convergence. During a large-scale experiment, 197 three-component short-period stations, 41 broad band instruments and 190 geophones were continuously operated for 6 months (April to October 2017) covering an area of approximately

1500 {km}^{2}

with a site spacing of approximately 1 to

3 km

. Joint inversion of HVSR and DC allowed estimating

V_{S}

and, to some extent density, down to depths of around

1000 m

. Broadband and short period instruments performed statistically better than geophone nodes due to the latter’s gap in sensitivity between HVSR and DC. It may be possible to use HVSR data in a joint inversion with DC, increasing resolution for the shallower layers and/or alleviating the absence of short period DC data, which may be harder to obtain. By including HVSR to DC inversions, confidence improvements of two to three times for layers above

300 m

were achieved. Furthermore, HVSR/DC joint inversion may be useful to generate initial models for 3D tomographic inversions in large scale deployments. Lastly, the joint inversion of HVSR and DC data can be sensitive to density but this sensitivity is situational and depends strongly on the other inversion parameters, namely

V_{S}

and

V_{P}

. Density estimates from a HVSR/DC joint inversion should be treated with care, while some subsurface structures may be sensitive, others are clearly not. Inclusion of gravity inversion to HVSR/DC joint inversion may be possible and prove useful. Full article

(This article belongs to the Special Issue Data Acquisition and Analysis of Seismic Noise)

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16 pages, 7139 KiB

Open AccessArticle

Horizontal-to-Vertical Spectral Ratio of Ambient Vibration Obtained with Hilbert–Huang Transform

by Maik Neukirch, Antonio García-Jerez, Antonio Villaseñor, Francisco Luzón, Mario Ruiz and Luis Molina

Sensors 2021, 21(9), 3292; https://doi.org/10.3390/s21093292 - 10 May 2021

Cited by 3 | Viewed by 2492

Abstract

The Horizontal-to-Vertical Spectral Ratio (HVSR) of ambient vibration measurements is a common tool to explore near surface shear wave velocity (Vs) structure. HVSR is often applied for earthquake risk assessments and civil engineering projects. Ambient vibration signal originates from the combination of a multitude of natural and man-made sources. Ambient vibration sources can be any ground motion inducing phenomena, e.g., ocean waves, wind, industrial activity or road traffic, where each source does not need to be strictly stationary even during short times. Typically, the Fast Fourier Transform (FFT) is applied to obtain spectral information from the measured time series in order to estimate the HVSR, even though possible non-stationarity may bias the spectra and HVSR estimates. This problem can be alleviated by employing the Hilbert–Huang Transform (HHT) instead of FFT. Comparing 1D inversion results for FFT and HHT-based HVSR estimates from data measured at a well studied, urban, permanent station, we find that HHT-based inversion models may yield a lower data misfit

χ^{2}

by up to a factor of 25, a more appropriate Vs model according to available well-log lithology, and higher confidence in the achieved model. Full article

(This article belongs to the Special Issue Data Acquisition and Analysis of Seismic Noise)

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