Removal of Crossed Artifacts from Multimodal Dispersion Curves with Modified Frequency–Bessel Method

2021 ◽  
Author(s):  
Jie Zhou ◽  
Xiaofei Chen
Keyword(s):  
Surface Wave ◽  
Correlation Functions ◽  
Fundamental Mode ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Dispersion Curves ◽  
Hankel Function ◽  
Transform Method ◽  
Wave Inversion ◽  
Noise Data

ABSTRACT Frequency–Bessel (F-J) transform method can obtain higher-mode Rayleigh dispersion curves by multistation ambient noise data superposition (Wang et al., 2019). Because the dispersion curves of the overtones can provide more information compared with the single fundamental mode, the nonuniqueness of surface-wave inversion can be reduced. Because of the limited number of receivers, the integral in the process of transformation cannot be calculated precisely and there exists a kind of crossed artifacts which cuts off the real dispersion curves and contaminates the spectrum. Forbriger (2003) proposed to use the Hankel function instead of the Bessel function to conduct the transformation to remove the crossed artifacts. However, this method can reduce the resolution of the spectrum from ambient noise data. In this article, we give a complete workflow to deal with ambient noises which can eliminate the crossed artifacts without reducing the resolution. The Kramers–Kronig relations are used to obtain complete cross-correlation functions and a modified F-J transform is conducted to finally acquire the spectrum without crossed artifacts.

scholarly journals Surface wave group velocity in the Osaka sedimentary basin, Japan, estimated using ambient noise cross-correlation functions

2017 ◽  
Vol 69 (1) ◽  
Author(s):  
Kimiyuki Asano ◽  
Tomotaka Iwata ◽  
Haruko Sekiguchi ◽  
Kazuhiro Somei ◽  
Ken Miyakoshi ◽  
...  
Keyword(s):  
Surface Wave ◽  
Group Velocity ◽  
Correlation Functions ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Sedimentary Basin ◽  
Wave Group ◽  
Noise Cross Correlation

DisperNet: An Effective Method of Extracting and Classifying the Dispersion Curves in the Frequency–Bessel Dispersion Spectrum

2021 ◽  
Author(s):  
Sheng Dong ◽  
Zhengbo Li ◽  
Xiaofei Chen ◽  
Lei Fu
Keyword(s):  
Surface Wave ◽  
Dispersion Curve ◽  
Shear Wave ◽  
Wave Structure ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Surface Wave Dispersion ◽  
Transform Method ◽  
Practical Applications ◽  
Noise Data

ABSTRACT The subsurface shear-wave structure primarily determines the characteristics of the surface-wave dispersion curve theoretically and observationally. Therefore, surface-wave dispersion curve inversion is extensively applied in imaging subsurface shear-wave velocity structures. The frequency–Bessel transform method can effectively extract dispersion spectra of high quality from both ambient seismic noise data and earthquake events data. However, manual picking and semiautomatic methods for dispersion curves lack a unified criterion, which impacts the results of inversion and imaging. In addition, conventional methods are insufficiently efficient; more precisely, a large amount of time is required for curve extraction from vast dispersion spectra, especially in practical applications. Thus, we propose DisperNet, a neural network system, to extract and discriminate the different modes of the dispersion curve. DisperNet consists of two parts: a supervised network for dispersion curve extraction and an unsupervised method for dispersion curve classification. Dispersion spectra from ambient noise and earthquake events are applied in training and validation. A field data test and transfer learning test show that DisperNet can stably and efficiently extract dispersion curves. The results indicate that DisperNet can significantly improve multimode surface-wave imaging.

Extracting higher-mode dispersion curves from ambient noise data by using F-J method to acquire more accurate crustal and upper-mantle structure for the east of South China

2020 ◽  
Author(s):  
Juqing Chen ◽  
Xiaofei Chen
Keyword(s):  
South China ◽  
Upper Mantle ◽  
Fundamental Mode ◽  
Ambient Noise ◽  
Dispersion Curves ◽  
Inversion Method ◽  
Mantle Structure ◽  
Higher Modes ◽  
Upper Mantle Structure ◽  
Noise Data

<p>It has been widely recognized that the cross-correlation function (CCF) of ambient noise data recorded at two seismic stations approximates to the part of Green’s Function between these two stations. Theoretically, the CCF should include the higher modes, apart from the fundamental mode. However, currently well-known and mature methods that can extract dispersion curves are not pretty proficient in extracting higher modes. Fortunately, our newly proposing method, the Frequency-Bessel Transform Method (F-J Method), has presented its obvious advantage in extracting higher modes. This study applied F-J method to seismic ambient noise data for the east of South China, including Jiangnan Orogen and South China Fold System. We have acquired higher modes, not to mention the fundamental mode with wider frequency than previous studies. Combining both fundamental mode and higher modes, we used L-BFGS inversion method to inverse and acquire more accurate crustal and upper-mantle structure than previous studies only adopting fundamental mode for the east of South China. As shown in this study for the east of South China, we can use F-J method  to conveniently and precisely extract multimodes from ambient noise data and thus add more constrains for inversion results, which can significantly improve the preciseness of imaging crustal and upper-mantle structure.</p>

Extraction of Surface-Wave Dispersion Curves from Ambient Noise Data in a Mineral Exploration Site in Finland

2021 ◽  
Author(s):  
C. Colombero ◽  
M. Papadopoulou ◽  
F. Da Col ◽  
K. Emilia ◽  
Ł. Sito ◽  
...  
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Mineral Exploration ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Surface Wave Dispersion ◽  
Noise Data

Extracting Long-Period Surface Waves and Free Oscillations Using Ambient Noise Recorded by Global Distributed Superconducting Gravimeters

2020 ◽  
Vol 91 (4) ◽  
pp. 2234-2246
Author(s):  
Hang Li ◽  
Jianqiao Xu ◽  
Xiaodong Chen ◽  
Heping Sun ◽  
Miaomiao Zhang ◽  
...  
Keyword(s):  
Surface Waves ◽  
Group Velocity ◽  
Ambient Noise ◽  
Velocity Dispersion ◽  
Group Velocity Dispersion ◽  
Dispersion Curves ◽  
Free Oscillations ◽  
Long Period ◽  
Superconducting Gravimeters ◽  
Noise Data

Abstract Inversion of internal structure of the Earth using surface waves and free oscillations is a hot topic in seismological research nowadays. With the ambient noise data on seismically quiet days sourced from the gravity tidal observations of seven global distributed superconducting gravimeters (SGs) and the seismic observations for validation from three collocated STS-1 seismometers, long-period surface waves and background free oscillations are successfully extracted by the phase autocorrelation (PAC) method, respectively. Group-velocity dispersion curves at the frequency band of 2–7.5 mHz are extracted and compared with the theoretical values calculated with the preliminary reference Earth model. The comparison shows that the best observed values differ about ±2% from the corresponding theoretical results, and the extracted group velocities of the best SG are consistent with the result of the collocated STS-1 seismometer. The results indicate that reliable group-velocity dispersion curves can be measured with the ambient noise data from SGs. Furthermore, the fundamental frequency spherical free oscillations of 2–7 mHz are also clearly extracted using the same ambient noise data. The results in this study show that the SG, besides the seismometer, is proved to be another kind of instrument that can be used to observe long-period surface waves and free oscillations on seismically quiet days with a high degree of precision using the PAC method. It is worth mentioning that the PAC method is first and successfully introduced to analyze SG observations in our study.

scholarly journals Ambient noise cross-correlation observations of fundamental and higher-mode Rayleigh wave propagation governed by basement resonance

2021 ◽  
Author(s):  
Martha Savage ◽  
FC Lin ◽  
John Townend
Keyword(s):  
Rayleigh Wave ◽  
Correlation Functions ◽  
Rayleigh Waves ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Amplitude Ratio ◽  
Sedimentary Basins ◽  
Longitudinal Motion ◽  
Resonance Frequencies ◽  
Noise Cross Correlation

Measurement of basement seismic resonance frequencies can elucidate shallow velocity structure, an important factor in earthquake hazard estimation. Ambient noise cross correlation, which is well-suited to studying shallow earth structure, is commonly used to analyze fundamental-mode Rayleigh waves and, increasingly, Love waves. Here we show via multicomponent ambient noise cross correlation that the basement resonance frequency in the Canterbury region of New Zealand can be straightforwardly determined based on the horizontal to vertical amplitude ratio (H/V ratio) of the first higher-mode Rayleigh waves. At periods of 1-3 s, the first higher-mode is evident on the radial-radial cross-correlation functions but almost absent in the vertical-vertical cross-correlation functions, implying longitudinal motion and a high H/V ratio. A one-dimensional regional velocity model incorporating a ~ 1.5 km-thick sedimentary layer fits both the observed H/V ratio and Rayleigh wave group velocity. Similar analysis may enable resonance characteristics of other sedimentary basins to be determined. © 2013. American Geophysical Union. All Rights Reserved.

Waveform inversion of ambient noise cross-correlation functions in a coastal ocean environment

2014 ◽  
Vol 136 (4) ◽  
pp. 2156-2156
Author(s):  
Xiaoqin Zang ◽  
Michael G. Brown ◽  
Neil J. Williams ◽  
Oleg A. Godin ◽  
Nikolay A. Zabotin ◽  
...  
Keyword(s):  
Correlation Functions ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Waveform Inversion ◽  
Coastal Ocean ◽  
Ocean Environment ◽  
Noise Cross Correlation
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