# CC-FJpy **Repository Path**: ucaslilong/CC-FJpy ## Basic Information - **Project Name**: CC-FJpy - **Description**: A Python Package for seismic ambient noise cross-correlation and the frequency-Bessel transform method - **Primary Language**: Unknown - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2022-02-11 - **Last Updated**: 2024-05-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # CC-FJpy: A Python Package for seismic ambient noise cross-correlation (CC) and the frequency-Bessel transform (FJ) method # CC-FJpy: 背景噪声互相关和频率贝塞尔变换法软件包 ## Copyright Xiaofei Chen Research Group Department of Earth and Space Sciences, SUSTech, China. ## Related article https://doi.org/10.1785/0220210042 ## 相关论文 https://doi.org/10.1785/0220210042 ## 版权所有 陈晓非课题组 南方科技大学，地球与空间科学系 ## Installation ## 安装 Python 3 is required. Anaconda environment is required for installation by `make install`. 目前仅支持 Python 3，推荐使用Anaconda配置Python环境。请确保您所安装和使用的Python以及编译器是同一个。 **Make sure that the CUDA and Python in the same version for you install and run.** Before Installation: Download & Compile `fftw` Library. 在安装前请安装`fftw`库，可以在当前目录通过如下命令一键安装。 ``` make fftw ``` After the installation of the fftw, install the CC-FJpy with the following commands 在安装fftw库之后可以在当前目录通过如下两行命令完成CC-FJpy的安装。 ``` make make install ``` **If the machine has an NVIDIA graphics card and the `nvcc` compiler is already in the path, the `GPU` version of CC-FJpy will be installed. If the above conditions are not met, the `CPU` version will be installed.** If you want to install only the `CPU` version, you can install it with the following commands. **若本机有英伟达显卡且`nvcc`编译器已经在路径中时，`GPU`版本的CC-FJpy将会被安装，如果不满足上述条件则会安装`CPU`版本。** 若想仅安装`CPU`版本，则可以通过如下两行命令安装。 ``` make cpu make install ``` ## Usage ## 使用 Please import the package before use its functions. 请在使用前先导入。 ``` import ccfj ``` ### Cross-correlation (CC) ### 互相关计算 ``` CCFs=ccfj.CC( npts,nsta,nf,fftlen, Pairs,startend,data, overlaprate=0.0, nThreads=8, fstride=1, ifonebit=0, ifspecwhittenning=1) ``` - `npts`: The number of points for data of one station read in（每次互相关每个台所使用数据点数，在程序中对于每个台是一致的，没有数据的点可以通过补0处理 ） - `nsta`: The number of stations（台站总数） - `nf`: The number of points of the frequency domain CC output （输出互相关的频率点数） - `fftlen`: The number of points for one CC （单词互相关所用点数） - `Pairs`: A numpy array for Station Pairs, the `dtype` should be `np.int32` （要计算的台站对。一维数组,长度为nparis*2。要计算的第i个台站对互相关为`Pairs[i*2,i*2+1]`） You can use function GetStationPairs to generate Pairs. - `startend`: A numpy array records the start point and end point for each station, the dtype should be np.int32. For example, if the npts length is for one day, and station A have all of A day's data. Then for station A the startend is [0,npts] （记录每个台的记录开始时间和结束时间的数组。） - `data`: The seismic records, the `dtype` should be `np.float32` （连续地震记录，长度为nsta*npts的一维数组。将每个台的记录（不满npts的点补0）依次排在该数组中。） - `overlaprate`: The rate for overlap, do not >= 1 - `nThreads`: The number of Threads for reading data and CC （互相关计算中omp所使用的核数） - `fstride`: The output frequency stride （输出的互相关对应的频率点数相对于原始数据傅里叶变换（fftlen）时频谱点的间隔） - `ifonebit`: if perform onebit （是否使用onebit） - `ifspecwhittening`: if perform specwhittenning （是否使用谱白化） - `CCFs`: The output is the noise cross-correlation functions (CCFs) （输出为频率域互相关函数） According to our experience, `ifonebit` and `ifspecwhittening`, pick one of them is ok. 根据我们的经验，`ifonebit` 和`ifspecwhittening`二者选其一即可。 The specific example, please read example_CC.ipynb. 具体例子请参考 example_CC.ipynb。 ### frequency-Bessel transform method (F-J method) ### 频率-贝塞尔变换法 #### For ambient noise #### 对背景噪声数据 ``` out = ccfj.fj_noise(uf,r,c,f,fstride=1,itype=1,func=0,num=20) ``` The F-J Method for CCFs - `uf`: the CCFs in the frequency domain （频率域互相关函数,二维数组，大小为`[互相关数，nf]`） - `r`: the list of station distances (unit: m) （对应的台间距数组，单位m，长度为互相关数） - `c`: the list of phase velocities you want to calculate （对要计算的相速度数组，单位m/s） - `f`: the list of frequencies. The number of points of f should be consistent with the columns of `uf` (频率域互相关对应的频率) - `fstride`: stride of frequency for output （默认1即可） - `itype`: `0` for trapezoidal integral; `1` for linear approximate （积分类型`0`梯形积分，`1`对格林函数进行线性逼近） - `func`: `0` for Bessel function; `1` for Hankel function (使用的积分基底`0`贝塞尔函数，`1`汉克尔函数) - `out`: the output dispersion spectrum （输出的频散谱） - `num`: the number of threads for cpu version (defult 20) and the device number of gpu version (default 0) （若是cpu版本则对应并行cpu核数，若是gpu版本则对应了gpu id） #### For earthquakes #### 对地震数据（或主动源数据） ``` out = ccfj.fj_earthquake(u,r,c,f,fstride=1,itype=1,func=0,num =20) ``` - `u`: the records in time domain （时间域地震记录） - `r`: the list of station distances (unit: m) （震中距） - `c`: the list of phase velocities you want to calculate （相速度） - `f`: the list of frequencies. The number of points of f should be consistent with the columns of `uf` （频率，这里频率需要对应输入u中时间域点数对应的频率） - `fstride`: stride of frequency for output (默认1) - `itype`: `0` for trapezoidal integral; `1` for linear approximate （积分类型`0`梯形积分，`1`对格林函数进行线性逼近） - `func`: `0` for Bessel function; `1` for Hankel function (使用的积分基底`0`贝塞尔函数，`1`汉克尔函数) - `out`: the output dispersion spectrum （输出的频散谱） - `num`: the number of threads for cpu version (defult 20) and the device number of gpu version (default 0) （若是cpu版本则对应并行cpu核数，若是gpu版本则对应了gpu id） ### Mutli-windows F-J method (MWFJ) ### 多窗频率贝塞尔变换法 （MWFJ） This is mainly for earthquake 该函数主要针对地震或者主动源数据 ``` out = ccfj.MWFJ(u,r,c,f,Fs,nwin,winl,winr,taper=0.9,fstride=1,itype=0,func=0, num=20) ``` - `u`: the records in time domain - `r`: the list of station distances (unit: m) - `c`: the list of phase velocities you want to calculate - `f`: the list of frequencies. The number of points of f should be consistent with the columns of `uf` - `Fs`: The sample frequency - `nwin`: number of time windows （时间窗个数） - `winl`: list of left side of time windows （每个记录时间窗的左端） - `winr`: list of right side of time windows （每个记录时间窗的右端） - `fstride`: stride of frequency for output - `itype`: `0` for trapezoidal integral; `1` for linear approximate - `func`: `0` for Bessel function; `1` for Hankel function - `out`: the output dispersion spectrum - `num`: the number of threads for cpu version (defult 20) and the device number of gpu version (default 0) ## Uninstall ## 卸载 ``` make clean make uninstall ``` ## References ## 参考文献 Wang, J., Wu, G., & Chen, X. (2019). Frequency‐Bessel Transform Method for Effective Imaging of Higher‐Mode Rayleigh Dispersion Curves From Ambient Seismic Noise Data. Journal of Geophysical Research: Solid Earth, 124(4), 3708-3723. doi:10.1029/2018jb016595 Wu, G.-x., Pan, L., Wang, J.-n., & Chen, X. (2020). Shear Velocity Inversion Using Multimodal Dispersion Curves From Ambient Seismic Noise Data of USArray Transportable Array. Journal of Geophysical Research: Solid Earth, 125(1), e2019JB018213. doi:10.1029/2019jb018213 Li, Z., & Chen, X., (2020). An Effective Method to Extract Overtones of Surface Wave from Array Seismic Records of Earthquake Events. Journal of Geophysical Research: Solid Earth, 125(3), e2019JB18511. doi:10.1029/2019jb018511 Li, Z., Zhou, J., Wu, G., Wang, J., Zhang, G., Dong, S., Pan, L., Yang, Z., Gao, L., Ma, Q., Ren, H., & Chen, X. (2021). CC-FJpy: A Python Package for seismic ambient noise cross-correlation and the frequency-Bessel transform method. Seismological Research Letters. doi:10.1785/0220210042