scholarly journals Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements

2007 ◽  
Vol 169 (3) ◽  
pp. 1239-1260 ◽  
Author(s):  
G. D. Bensen ◽  
M. H. Ritzwoller ◽  
M. P. Barmin ◽  
A. L. Levshin ◽  
F. Lin ◽  
...  
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Broad Band ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Seismic Ambient Noise ◽  
Noise Data

Extracting surface wave dispersion curves from asynchronous seismic stations: method and application

2021 ◽  
Author(s):  
Hao Rao ◽  
Yinhe Luo ◽  
Kaifeng Zhao ◽  
Yingjie Yang
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Ambient Noise ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Seismic Stations ◽  
Seismic Arrays ◽  
Single Station ◽  
Path Coverage ◽  
Noise Data

Summary Correlation of the coda of Empirical Green's functions from ambient noise can be used to reconstruct Empirical Green's function between two seismic stations deployed different periods of time. However, such method requires a number of source stations deployed in the area surrounding a pair of asynchronous stations, which limit its applicability in cases where there are not so many available source stations. Here, we propose an alternative method, called two-station C2 method, which uses one single station as a virtual source to retrieve surface wave phase velocities between a pair of asynchronous stations. Using ambient noise data from USArray as an example, we obtain the interstation C2 functions using our C2 method and the traditional cross-correlation functions (C1 functions). We compare the differences between the C1 and C2 functions in waveforms, dispersion measurements, and phase velocity maps. Our results show that our C2 method can obtain reliable interstation phase velocity measurements, which can be used in tomography to obtain reliable phase velocity maps. Our method can significantly improve ray path coverage from asynchronous seismic arrays and enhance the resolution in ambient noise tomography for areas between asynchronous seismic arrays.

scholarly journals Phase-weighted slant stacking for surface wave dispersion measurement

2021 ◽  
Vol 226 (1) ◽  
pp. 256-269
Author(s):  
Feng Cheng ◽  
Jianghai Xia ◽  
Kai Zhang ◽  
Changjiang Zhou ◽  
Jonathan B Ajo-Franklin
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Wave Dispersion ◽  
Source Surface ◽  
Coherent Signals ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Dispersion Measurement ◽  
Noise Data ◽  
Slant Stacking

SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.

CC-FJpy: A Python Package for Extracting Overtone Surface-Wave Dispersion from Seismic Ambient-Noise Cross Correlation

2021 ◽  
Author(s):  
Zhengbo Li ◽  
Jie Zhou ◽  
Gaoxiong Wu ◽  
Jiannan Wang ◽  
Gongheng Zhang ◽  
...  
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Wave Dispersion ◽  
Ease Of Use ◽  
Processing Unit ◽  
Surface Wave Dispersion ◽  
Seismic Ambient Noise ◽  
Noise Cross Correlation ◽  
Python Package

Abstract In the past two decades, seismic ambient-noise cross correlation (CC) has been one of the most important technologies in seismology. Usually, only the fundamental-mode surface-wave dispersion was extracted from the ambient noise. Recently, with the frequency–Bessel transform (F-J) method, overtone dispersion can also be extracted from the ambient noise and it adds significant value in inversion. This method has also been verified to be effective for array seismic records of earthquake events. In this article, we describe our algorithm and a Python package called CC-FJpy. For the F-J method, we use the Nvidia’s graphics processing unit to accelerate the computation, which can achieve a 100-fold computational efficiency. We have encapsulated our experiences and technologies into CC-FJpy and tested the CC-FJpy by ambient-noise and earthquake data to ensure its speed and ease of use. Our open-source package CC-FJpy can benefit the development of surface-wave studies using ambient noise and make it easier to start with high-mode surface waves.

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

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.

scholarly journals SWinvert: a workflow for performing rigorous 1-D surface wave inversions

2020 ◽  
Vol 224 (2) ◽  
pp. 1141-1156
Author(s):  
Joseph P Vantassel ◽  
Brady R Cox
Keyword(s):  
Surface Wave ◽  
Open Source ◽  
High Performance ◽  
Broad Band ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
University Of Texas ◽  
Wave Inversion ◽  
Dispersion Data ◽  
Band Limited

SUMMARY SWinvert is a workflow developed at The University of Texas at Austin for the inversion of surface wave dispersion data. SWinvert encourages analysts to investigate inversion uncertainty and non-uniqueness in shear wave velocity (Vs) by providing a systematic procedure and specific actionable recommendations for surface wave inversion. In particular, the workflow encourages the use of multiple layering parametrizations to address the inversion's non-uniqueness, multiple global searches for each parametrization to address the inverse problem's non-linearity and quantification of Vs uncertainty in the resulting profiles. While the workflow uses the Dinver module of the popular open-source Geopsy software as its inversion engine, the principles presented are of relevance to analysts using other inversion programs. To illustrate the effectiveness of the SWinvert workflow and to develop a set of benchmarks for use in future surface wave inversion studies, synthetic experimental dispersion data for 12 subsurface models of varying complexity are inverted. While the effects of inversion uncertainty and non-uniqueness are shown to be minimal for simple subsurface models characterized by broad-band dispersion data, these effects cannot be ignored in the Vs profiles derived for more complex models with band-limited dispersion data. To encourage adoption of the SWinvert workflow, an open-source Python package (SWprepost), for pre- and post-processing of surface wave inversion data, and an application on the DesignSafe-Cyberinfrastructure (SWbatch), for performing batch-style surface wave inversions with Dinver using high-performance computing, have been developed and released in conjunction with this work. The SWinvert workflow is shown to provide a methodical procedure and a powerful set of tools for performing rigorous surface wave inversions and quantifying the uncertainty in the resulting Vs profiles.

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