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

Dong, Sheng1,2; Li, Zhengbo2,3,4; Chen, Xiaofei2,3; Fu, Lei2,3

2021-12-01

10.1785/0120210033

BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA   影响因子和分区

0037-1106

1943-3573

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.

SCI ; EI

英语

通讯

Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology[ZDSYS20190902093007855] ; Leading Talents of Guangdong Province Program[2016LJ06N652] ; National Natural Science Foundation of China[41790465,41974044,"U1901602"] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0203] ; Shenzhen Science and Technology Program[KQTD20170810111725321]

Geochemistry & Geophysics

Geochemistry & Geophysics

WOS:000722815800006

SEISMOLOGICAL SOC AMER

20214811253022

Data mining ; Earthquakes ; Extraction ; Shear flow ; Shear waves ; Surface waves ; Wave propagation

Seismology:484 ; Fluid Flow, General:631.1 ; Data Processing and Image Processing:723.2 ; Chemical Operations:802.3 ; Mechanics:931.1

GEOSCIENCES

Web of Science

1.Univ Sci & Technol China, Sch Earth & Space Sci, Hefei, Peoples R China
2.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China
3.Southern Univ Sci & Technol, Shenzhen Key Lab Deep Offshore Oil & Gas Explorat, Shenzhen, Peoples R China
4.Southern Univ Sci & Technol, Acad Adv Interdisciplinary Studies, Shenzhen, Peoples R China

Dong, Sheng,Li, Zhengbo,Chen, Xiaofei,et al. DisperNet: An Effective Method of Extracting and Classifying the Dispersion Curves in the Frequency-Bessel Dispersion Spectrum[J]. BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA,2021,111(6):3420-3431.

Dong, Sheng,Li, Zhengbo,Chen, Xiaofei,&Fu, Lei.(2021).DisperNet: An Effective Method of Extracting and Classifying the Dispersion Curves in the Frequency-Bessel Dispersion Spectrum.BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA,111(6),3420-3431.

Dong, Sheng,et al."DisperNet: An Effective Method of Extracting and Classifying the Dispersion Curves in the Frequency-Bessel Dispersion Spectrum".BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA 111.6(2021):3420-3431.