Array-assisted-Phase-Picker

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Array-assisted-Phase-Picker

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Array-assisted Phase Picker

Array-assisted-Phase-Picker 최신버젼 다운로드

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# Array-assisted Phase Picker (APP)

author: Jiang Yiran & Ning Jieyuan  
a program for earthquakes and micro-earthquakes detection  
it has three fundamental parts：  
1 detect earthquakes using APP  
2 detect micro-earthquakes based on GPU-WMFT  
3 calculate travel time difference by cross-correlation and provide input files for tomoDD

As we developed this program in our research, it also contains many other functions.  if not needed, you can ignore the other part. Anyway, the reorganizing work is going on and we would try to give a better way to use for users who need this kind of functions.

## 1、Array-assisted Phase Picker

### simple way
in the simple way, the only .py script you need is genMV3.py   
Needed packages: tensorflow, keras and numpy   
if you do not want to train model and just want to use our pretrained ones  
you can just install tensorflow's cpu version instead of gpu version 
because in the prediction process the cpu version's computation speed is enough
and does not involve difficulties in installing gpu drive

you can use our model by the following steps:      
first load the deep learning model  
as we use two nets for picking P and S waves separately 
we need load two nets(modelP,modelS)  
```py
from genMV3 import genModel
modelP = genModel()
modelP.load_weights('modelP_320000_0-2-15-with')
modelS = genModel()
modelS.load_weights('modelS_320000_0-2-15-with')
'''
'modelP_320000_0-2-15-with' and 'modelP_320000_0-2-15-with' are trained with typical noise 
'modelP_320000_100-2-15' and 'modelP_320000_100-2-15' are trained without typical noise
'''
```
prepare your data (inputData)   
the input data's shape should be [n, 2000, 1,3]   
"n" means the data slices count   
"2000" means each slice has 2000 samples in the time domain(50 Hz,40s)   
"3" means each slice have three components (E,N,Z)   
the data should be filtered in bandpass of (2 Hz,15 Hz)   
```py 
inputData /= inputData.std(axis=(1, 2, 3),keepdims=True) #normalize
```
input the data to model   
```py
probP = modelP.predict(inputData)
probS = modelS.predict(inputData)
```
then you get the probabilities of P and S wave(prob and probs)  
You can set different thresholds according to your requirments

### normal way

Needed package in this part: numpy, numba, obspy, h5py, scipy,matplotlib, openpyxl, tensorflow-gpu,keras(optional: basemap, netCDF4, lxml, pykml,pycpt). As pycpt is not available on open source, you can install it via:
```
pip install https://github.com/j08lue/pycpt/archive/master.zip
```

phaseNet is based on ZhuW‘s paper and we adopted it for continuous waveform proposing Phase Picker(PP). We combined it with the array strategy developed by Jiang Y and Ning J as Array-assisted Phase Picker (APP). As we provide some pre-trained model, you can directly use our program on your data.  
in general, you need to provide the following things before running our program:

### (1) station information list file:

net sta    comp longitude/° latitude/° elevation/m  rms_of_lon rms_of_lat
```
XX  JJS    BH   104.55      31.00      0.000000     0.000000   0 0.000000  
```
rms of lon/lat is the location rms from the data log's GPS location (not necessary, you can just set it to 0 )  
the example is 'staLstSC'  
   
 ### (2) file path function:  
 write a function that return the sacFileNames list according to the input(net/station/comp/date). we give an example:
```python
def FileName(net, station, comp, YmdHMSJ):
    sacFileNames = list()
    c=comp[-1]
    if c=='Z':
        c='U'
    sacFileNames.append('data/'+net+'/'+station+'/'+net+'.'+YmdHMSJ['Y']+\
        YmdHMSJ['m']+YmdHMSJ['d']+'.'+station+'.'+c+'.SAC')
    return sacFileNames
```
net is the network name(e. g. 'XX' ); station is the station name(e. g. 'ABC' ); comp is the component name(e. g. 'BHE' ); YmdHMSJ is a dict contained date information(year, month, day, hour, minute, second, day num from the first day of the year)(e. g. {'Y': '2019', 'm': '01', 'd': '01', 'H': '00', 'M': '01', 'S': '00', 'J': '001'}).    
you need not to think about how to give the input as we would automatically give it in our program. you just need to write the function that return the specific file path according to the input when it was called.  
in our example, the function will return the list of file path(e. g. ['data/XX/ABC/XX.ABC.20190101.E.SAC']). if a single day's data of one station is composed of several files, you should return list containing all of them, e. g. [fileA,fileB,....,fileX]. if you can easily convert your file into our file path pattern, you can use our file path function after the preparation.

### (3) edit the run script:
we give the detail in the script
```python
import os
import detecQuake
import trainPSV2 as trainPS
import sacTool
from obspy import UTCDateTime
import tool
from locate import locator

##########################file path function################################
'''
define your own file path function
'''
def FileName(net, station, comp, YmdHMSJ):
    sacFileNames = list()
    c=comp[-1]
    if c=='Z':
        c='U'
    sacFileNames.append('data/'+net+'/'+station+'/'+net+'.'+YmdHMSJ['Y']+\
        YmdHMSJ['m']+YmdHMSJ['d']+'.'+station+'.'+c+'.SAC')
    return sacFileNames
#############################################################################

os.environ["CUDA_VISIBLE_DEVICES"] = "0"
workDir='/home/jiangyr/accuratePickerV3/testNew/'# workDir: the dir to save the results
staLstFile='staLst_NM_New'#station list file
bSec=UTCDateTime(2015,6,1).timestamp#begain date
eSec=UTCDateTime(2015,10,1).timestamp#end date
laL=[35, 45]#area: [min_latitude, max_latitude]
loL=[96, 105]#area: [min_longitude, max_longitude]
laN=35#subareas in latitude/the default is enough
loN=35#subareas in longitude
nameFunction=FileName# set to your own file path function

#####no need to change ##########
taupM=tool.quickTaupModel(modelFile='iaspTaupMat')# load pre-calculated travel time result to accelerate the computation speed of travel time 
modelL = [trainPS.loadModel('modelP_320000_100-2-15'),\
trainPS.loadModel('modelS_320000_100-2-15')]#load pre-trained model of P/S
staInfos=sacTool.readStaInfos(staLstFile) #load station information stored in staLstFile
aMat=sacTool.areaMat(laL,loL,laN,loN)#generate subareas according to laL,loL,laN,loN
staTimeML= detecQuake.getStaTimeL(staInfos, aMat, taupM=taupM)#calculate the travel time  range between each station and each subarea
quakeLs=list()# init the quakeLs to store results in memory

for date in range(int(bSec),int(eSec),86400):
    
    date=UTCDateTime(float(date))
    print('pick on ',date)
    staL = detecQuake.getStaL(staInfos, aMat, staTimeML,\
        modelL, date, getFileName=nameFunction,\
        mode='norm',f=[2,15])
    quakeLs.append(detecQuake.associateSta(staL, aMat, \
        staTimeML, timeR=10, maxDTime=3, N=1,locator=\
        locator(staInfos)))
    '''
    save:
    result's in  workDir+'phaseLst'
    result's waveform in  workDir+'output/'
    result's plot picture in  workDir+'output/'
    '''
    tool.saveQuakeLs(quakeLs, workDir+'phaseLst')
    tool.saveQuakeLWaveform(staL, quakeLs[-1], \
        matDir=workDir+'output/',\
            index0=-1500,index1=1500)
    detecQuake.plotResS(staL,quakeLs[-1],outDir=workDir+'output/')
    staL=[]# clear data  to save memory
```

then run this script you can detect earthquakes in your data

in the phaseLst file:  
line for quake:

```quake: 41.069343 100.100329 1388627056.193255 num: 5 index: 0 randID: 1 filename:             16072/1388627055_1.mat -0.026085 3.335500```

quake: lat lon time(obspy.UTCDateTime().timestamp) num: (how many station record) index: ( index of quake) randID: (give each quake a unique ID) filename: (where to save the waveform data of this quake) magnitude depth

line for record :

```30 1388627080.025000  1388627095.945000 ```

staIndex(start from 0) pTime(0 means no phase picked) sTime

## Reference:  
刘芳,蒋一然,宁杰远,张建中,赵艳红.结合台阵策略的震相拾取深度学习方法[J/OL].科学通报:1-11[2020-03-25].http://kns.cnki.net/kcms/detail/11.1784.N.20200320.1616.004.html.

Zhu W, Beroza G C. PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophys J Int, 2018, 216(1): 261-273.  
Jiang Y R, Ning J Y. Automatic detection of seismic body-wave phases and determination of their arrival times based on support vector machine(in Chinese). Chinese J Geophys, 2019, 62(1): 361-373.

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