Ambient noise cross-correlation functions and three-dimensional S-wave velocity structure in the Noto Peninsula, Japan

The three-year-lasting earthquake swarm in the Noto Peninsula, Japan, led to the 2024 Noto earthquake (moment magnitude 7.5). To reveal structural controls on the swarm evolution and the large earthquake generation, we imaged the three-dimensional S-wave velocity structure beneath the Noto Peninsula using ambient noise surface wave tomography with dense seismic observations. This dataset provides the cross-correlation functions of the densely observed ambient seismic noise and the estimated three-dimensional S-wave velocity structure model. We computed the multicomponent cross-correlation functions for the pairs of 22 seismic stations (composed of 12 seismic nodes and 10 short-period permanent stations). The observation data spanned 32 days, from October to November 2023. The cross-correlation functions in the vertical-vertical, vertical-radial, radial-radial, and transverse-transverse components yielded the estimates of dispersion curves of Rayleigh and Love waves. The dispersion curve..., , , # Ambient noise cross-correlation functions and three-dimensional S-wave velocity structure in the Noto Peninsula, Japan

[https://doi.org/10.5061/dryad.zcrjdfnm3](https://doi.org/10.5061/dryad.zcrjdfnm3)

## Description of the data and file structure

ccf.h5: The cross-correlation function (CCF) data are stored in the self-describing HDF5 format. The attributes of the HDF5 file describe the following parameters:

* b: Starting lag time of CCFs in seconds (-204.7).
* nc: Number of components of original seismograms (3). The number of CCF components is nc x nc (9).
* ns: Number of stations (22).
* npts: Number of CCF data samples (4096).
* sr: Sampling rate in samples per second (10).
* stnm1 & stnm2: Names of stations 1 & 2.
* stla1 & stla2: Latitudes of stations 1 & 2.
* stlo1 & stlo2: Longitudes of stations 1 & 2.
* stel1 & stel2: Elevations of stations 1 & 2.
* cmpnm: Name of CCF component.
* nstack: Number of CCF stacks.

vs3d.nc: The 3D S-wave velocity model is stored in the NetCDF...,

日本能登半岛持续三年的地震群引发了2024年能登地震（矩震级7.5，moment magnitude 7.5）。为揭示该地震群演化与大地震孕育的构造控制机制，研究团队利用密集台阵观测结合环境噪声面波层析成像（ambient noise surface wave tomography）方法，对能登半岛下方的三维S波速度结构（three-dimensional S-wave velocity structure）开展了成像反演。本数据集包含两部分内容：一是密集观测获取的环境地震噪声互相关函数（cross-correlation function，CCF），二是反演得到的三维S波速度结构模型。

研究针对22个地震台站（由12个地震节点与10个固定短周期台站组成）的台站对，计算了多分量环境噪声互相关函数。观测时段为2023年10月至11月，共计32天。通过垂直-垂直、垂直-径向、径向-径向以及横向-横向分量的互相关函数，提取得到瑞利波与勒夫波的频散曲线。频散曲线……

# 日本能登半岛地区环境噪声互相关函数与三维S波速度结构
https://doi.org/10.5061/dryad.zcrjdfnm3

## 数据与文件结构说明
ccf.h5：互相关函数（CCF）数据采用自描述型HDF5格式存储。该HDF5文件的属性包含以下参数：
* b：互相关函数的起始滞后时间，单位为秒（取值为-204.7）。
* nc：原始地震记录的分量数（取值为3），互相关函数的分量总数为nc×nc（共9个分量）。
* ns：台站总数（22）。
* npts：互相关函数的数据采样点数（4096）。
* sr：采样率，单位为每秒采样点数（10）。
* stnm1与stnm2：台站1与台站2的名称。
* stla1与stla2：台站1与台站2的纬度。
* stlo1与stlo2：台站1与台站2的经度。
* stel1与stel2：台站1与台站2的海拔。
* cmpnm：互相关函数分量的名称。
* nstack：互相关函数的叠加次数。

vs3d.nc：三维S波速度模型采用NetCDF格式存储……