understanding a newly proposed STA/LTA algorithm

Question

PARVATHY NAIR 2023-1-23

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1898725-understanding-a-newly-proposed-sta-lta-algorithm

评论： PARVATHY NAIR 2023-7-31

given below is the modified STA/LTA equation.

from my understanding i have formulated the code as follows.Kindly go through the code and correct me if my notion is wrong

clc
clear all
%% Data Inputs 
Error using importdata
Unable to open file.
Acc_EW = importdata("ADIB.HHE.dat")
Acc_NS = importdata("ADIB.HHN.dat");
Acc_ver = importdata("ADIB.HHZ.dat");
Fs = 100; %sampling frequency
%% Signal Pre-Processing
%Filter Design
digfilt = designfilt('lowpassiir', 'PassbandFrequency', 20, 'StopbandFrequency', 25, 'PassbandRipple', 1, 'StopbandAttenuation', 60, 'SampleRate', 200);
% Filtering Data
Acc_EW_filt = filter(digfilt,Acc_EW);
Acc_NS_filt = filter(digfilt,Acc_NS);
Acc_ver_filt = filter(digfilt,Acc_ver);
Fhp = 0.8; % high pass filter cutofff frequency 
[b1,a1] = butter(3,Fhp/Fs,'high'); %3rd order high pass butterworth filter
fildat = filter(b1,a1,Acc_ver); % filtered acceleration data
vel = cumtrapz(fildat)./Fs; % Integrating acceleration data for velocity
[b2,a2] = butter(3,Fhp/Fs,'high'); %3rd order high pass butterworth filter
fildat1 = filter(b2,a2,vel); % filtered velocity data
dis = cumtrapz(fildat1)./Fs;  % Integrating velocity data for displacement
peakToPeakRange = max(fildat) - min(fildat);
dt = 1/Fs; %sampling time
nt = length(fildat); % length of the input signal
time = (1:nt).*dt; % time duration of the input signal
 
%% STA-LTA Algorithm gor P-Wave detection
stw = 0.2;    %short time window length
ltw = 70;   %long time window length
thresh = 3; % Threshold
thresh1 = 4;
%t = 1;      
nl = fix(ltw / dt); %no. of data points in the long time window
ns = fix(stw / dt); %no. of data points in the short time window 
nt = length(fildat); 
sra = zeros(1, nt);
%%this where i have modified accroding to excerpt from the paper-'Framework
%%for Automated Earthquake Event Detection Based On Denoising by Adaptive
%%Filter'
for k = nl+1:nt
    staz(k,1) = (1/ns)* trapz(abs(fildat(k-ns:k)));            
    ltaz(k,1) = (1/nl)* trapz(abs(fildat(k-nl:k)));
    sta(k,1) = (1/ns)* [(staz(k-1)*ns)-fildat(k-ns)-fildat(k)];
    lta(k,1) = (1/nl)* [(ltaz(k-1)*nl)-fildat(k-nl)-fildat(k)];
 end
for l = nl+1: nt 
    sra(l) = sta(l)/lta(l);
end
  itm = find(sra > thresh);
    if ~isempty(itm)
      itmax=itm(1);
    end
    tp =itmax*dt; % P-wave arriving time 
    fprintf('P-Wave detection time for threshold 4 =  %f second\n', tp);
    
  itm1 = find(sra > thresh1);
    if ~isempty(itm1)
      itmax1 = itm1(1);
    end
    tp1 = itmax1*dt; % P-wave arriving time 
    fprintf('P-Wave detection time for threshold 3 = %f second\n', tp1);
%% S-wave arrival time
pkHts = 0.72; % 10 percent
[pk2,t22] = findpeaks(Acc_NS_dlycompensated,Fs,'MinPeakHeight',pkHts*max(Acc_ver_dlycompensated),'Npeaks',1);
[pk3,t33] = findpeaks(Acc_EW_dlycompensated,Fs,'MinPeakHeight',pkHts*max(Acc_ver_dlycompensated),'Npeaks',1);
display(sprintf('S-wave found on EW component at %f seconds and on NS componet at %f seconds,', t33,t22));
if(t22<t33)
    display('S-wave detected first on North-South component');
else
    display('S-wave detected first on East-West component');
end
ts = min(t22,t33);
line([ts,ts],[min(get(gca,'Ylim'))],'linestyle','--','linewidth',2,'color','red');
    
%% Tauc , Pd and Magnitude calculations
vel_sq = vel.^2;
dis_sq = dis.^2; 
r1 = trapz(vel_sq((itmax):(itmax+300)));
r2 = trapz(dis_sq((itmax):(itmax+300)));
r = r1/r2;
tauc = 2*pi/sqrt(r); 
pd = max(dis((itmax):(itmax+300)));
mag_tauc = (log(tauc) + 3.45)/0.47 %Coefficients varies from region to region
mag_pd = (0.873*((log(pd)+6.3)/0.513))+4.74 %Coefficients varies from region to region

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PARVATHY NAIR 2023-1-23

在 MATLAB Online 中打开

Framework_for_Automated_Earthquake_Event_Detection_Based_on_Denoising_by_Adaptive_Filter.pdf

thankyou @Mathieu NOE

its the same paper.I will attach the same again.and i will resend the code again

clc
clear all
%% Data Inputs 
Acc_EW = importdata("ADIB.HHE.dat")
Acc_NS = importdata("ADIB.HHN.dat");
Acc_ver = importdata("ADIB.HHZ.dat");
Fs = 100; %sampling frequency
%% Signal Pre-Processing
%Filter Design
digfilt = designfilt('lowpassiir', 'PassbandFrequency', 20, 'StopbandFrequency', 25, 'PassbandRipple', 1, 'StopbandAttenuation', 60, 'SampleRate', 200);
% Filtering Data
Acc_EW_filt = filter(digfilt,Acc_EW);
Acc_NS_filt = filter(digfilt,Acc_NS);
Acc_ver_filt = filter(digfilt,Acc_ver);
Fhp = 0.8; % high pass filter cutofff frequency 
[b1,a1] = butter(3,Fhp/Fs,'high'); %3rd order high pass butterworth filter
fildat = filter(b1,a1,Acc_ver); % filtered acceleration data
vel = cumtrapz(fildat)./Fs; % Integrating acceleration data for velocity
[b2,a2] = butter(3,Fhp/Fs,'high'); %3rd order high pass butterworth filter
fildat1 = filter(b2,a2,vel); % filtered velocity data
dis = cumtrapz(fildat1)./Fs;  % Integrating velocity data for displacement
peakToPeakRange = max(fildat) - min(fildat);
dt = 1/Fs; %sampling time
nt = length(fildat); % length of the input signal
time = (1:nt).*dt; % time duration of the input signal
%% Removing the delay introduced by lowpassiir filter
Acc_EW_dlycompensated = filtfilt(digfilt,Acc_EW);
Acc_NS_dlycompensated = filtfilt(digfilt,Acc_NS);
Acc_ver_dlycompensated = filtfilt(digfilt,Acc_ver);
 %% Finding the SNR
%  noise_ratio = snr(fildat) %returns the SNR in dBc of a real sinusoidal input signal, x, sampled at a rate fs. 
%  % The computation excludes the power contained in the lowest n harmonics, including the fundamental. 
%  % The default value of fs is 1. The default value of n is 6.
%  %fildat( .*) = 10^(3/20); %To increase the power of x by 3 dB:
%% plotting frequency spectrum
% fftsig = fft(fildat); %Taking fourier transform
% fftSig = fftshift(fftsig); %apply fftshift to put it in the form we are used to
% f = [-nt/2:nt/2-1]/nt; %Next, calculate the frequency axis, which is defined by the sampling rate
% figure;
% plot(f, abs(fftSig));
% title('magnitude FFT of sine');
% xlabel('Frequency (Hz)');
% ylabel('magnitude');
%% STA-LTA Algorithm gor P-Wave detection
stw = 0.2;    %short time window length
ltw = 70;   %long time window length
thresh = 3; % Threshold
thresh1 = 4;
%t = 1;      
nl = fix(ltw / dt); %no. of data points in the long time window
ns = fix(stw / dt); %no. of data points in the short time window 
nt = length(fildat); 
sra = zeros(1, nt);
%%this where i have modified accroding to excerpt from the paper-'Framework
%%for Automated Earthquake Event Detection Based On Denoising by Adaptive
%%Filter'
for k = nl+1:nt
    staz(k,1) = (1/ns)* trapz(abs(fildat(k-ns:k)));            
    ltaz(k,1) = (1/nl)* trapz(abs(fildat(k-nl:k)));
    sta(k,1) = (1/ns)* [(staz(k-1)*ns)-fildat(k-ns)-fildat(k)];
    lta(k,1) = (1/nl)* [(ltaz(k-1)*nl)-fildat(k-nl)-fildat(k)];
 end
for l = nl+1: nt 
    sra(l) = sta(l)/lta(l);
end
  itm = find(sra > thresh);
    if ~isempty(itm)
      itmax=itm(1);
    end
    tp =itmax*dt; % P-wave arriving time 
    fprintf('P-Wave detection time for threshold 4 =  %f second\n', tp);
    
  itm1 = find(sra > thresh1);
    if ~isempty(itm1)
      itmax1 = itm1(1);
    end
    tp1 = itmax1*dt; % P-wave arriving time 
    fprintf('P-Wave detection time for threshold 3 = %f second\n', tp1);
%% S-wave arrival time
pkHts = 0.72; % 10 percent
[pk2,t22] = findpeaks(Acc_NS_dlycompensated,Fs,'MinPeakHeight',pkHts*max(Acc_ver_dlycompensated),'Npeaks',1);
[pk3,t33] = findpeaks(Acc_EW_dlycompensated,Fs,'MinPeakHeight',pkHts*max(Acc_ver_dlycompensated),'Npeaks',1);
display(sprintf('S-wave found on EW component at %f seconds and on NS componet at %f seconds,', t33,t22));
if(t22<t33)
    display('S-wave detected first on North-South component');
else
    display('S-wave detected first on East-West component');
end
ts = min(t22,t33);
line([ts,ts],[min(get(gca,'Ylim'))],'linestyle','--','linewidth',2,'color','red');
    
%% Tauc , Pd and Magnitude calculations
vel_sq = vel.^2;
dis_sq = dis.^2; 
r1 = trapz(vel_sq((itmax):(itmax+300)));
r2 = trapz(dis_sq((itmax):(itmax+300)));
r = r1/r2;
tauc = 2*pi/sqrt(r); 
pd = max(dis((itmax):(itmax+300)));
mag_tauc = (log(tauc) + 3.45)/0.47 %Coefficients varies from region to region
mag_pd = (0.873*((log(pd)+6.3)/0.513))+4.74 %Coefficients varies from region to region
%% Distance of earthquake from the seismometer
dist = (ts-tp)*8;
display(sprintf('Earthquake is estimated to be %f kilometers from the seismometer',dist))
%% Acceleration Plot
figure(1);
subplot(3,1,1)
plot(time,fildat,[tp tp],ylim,'r','LineWidth',1)
%plot(time,fildat)
title('Acceleration Data');
xlabel('Time (Sec)');
ylabel('Acceleration (cm/sec^2)');
grid on 
grid minor
%% Velocity Plot
subplot(3,1,2)
plot(time,vel)
title('Velocity Data');
xlabel('Time (Sec)');
ylabel('Velocity (cm/sec)');
grid on 
grid minor
%% Displacement Plot
subplot(3,1,3)
plot(time,dis)
title('Displacement Data');
xlabel('Time (Sec)');
ylabel('Displacement (cm)');
grid on 
grid minor
%% Plotting Spectogram of Original Signal and detecting the P-wave first arrival
figure(2)
box on
hold on
subplot(3,1,1)
plot(time,fildat,[tp tp],ylim,'r','LineWidth',2)
hold on
plot(time,fildat,[tp1 tp1],ylim,'g','LineWidth',2)
%line([tp1 tp1],[0,100],'Color','green','LineWidth',2);
%title('Acceleration Data');
xlabel('Time (Sec)');
ylabel('Acceleration (cm/sec^2)');
grid on 
grid minor
axis tight
box on
s = spectrogram(abs(fildat),256,250,256,200,'yaxis');
subplot(3,1,2)
%title('Spectrogram of Acceleration Data');
spectrogram(abs(fildat),256,250,256,200,'yaxis')
tp_in_min = tp/60;
tp_in_min1 = tp1/60;
%line([tp_in_min tp_in_min],[0,100],'Color','red','LineWidth',2);
line([tp_in_min1 tp_in_min1],[0,100],'Color','green','LineWidth',2);
grid on 
grid minor
axis tight;
box on
subplot(3,1,3)
thresh_spec = spectrogram(abs(fildat),256,250,256,200,'MinThreshold',-50,'yaxis');
thresh_spec1 = abs(thresh_spec);
%title('Spectrogram of Acceleration Data with -50dB Threshold');
spectrogram(abs(fildat),256,250,256,200,'MinThreshold',-50,'yaxis')
tp_in_min = tp/60;
tp_in_min1 = tp1/60;
%line([tp_in_min tp_in_min],[0,100],'Color','red','LineWidth',2);
line([tp_in_min1 tp_in_min1],[0,100],'Color','green','LineWidth',2);
grid on 
grid minor
axis tight;
%print('-dpdf','-r600','Temp.pdf')
%% Vizualizing the displacement of the seismometer
% Using an integrator to compute velocity from acceleration data.
% Integrator is filter with TF = 1/(1-Z^-1)
Nr = 1;
Dr = [1,-1];
% Detrending acceleration data it to remove drift,Integrating and dividing with sampling frequency to get velocity data
Ver_acc_dt = detrend(Acc_ver_dlycompensated);
NS_acc_dt = detrend(Acc_NS_dlycompensated);
EW_acc_dt = detrend(Acc_EW_dlycompensated);
vel_ver_nodrift = filter(Nr,Dr,Ver_acc_dt)/Fs; 
vel_NS_nodrift = filter(Nr,Dr,NS_acc_dt)/Fs;
vel_EW_nodrift = filter(Nr,Dr,EW_acc_dt)/Fs;
% Integrating velocity data and dividing with sampling frequency to get displacement data
dis_ver = filter(Nr,Dr,vel_ver_nodrift)/Fs;A
dis_NS = filter(Nr,Dr,vel_NS_nodrift)/Fs;
dis_EW = filter(Nr,Dr,vel_EW_nodrift)/Fs;
% Plot the positions using 3D plot to visualize seismometer movements
figure(3);
plot3(dis_NS,dis_EW,dis_ver);
grid on; view([-45,30]);
xlabel('N-S Direction in cm');
ylabel('E-W Direction in cm');
zlabel('Vertical Direction in cm');
title('Displacement of the seismometer in 3D');
set(gcf,'Name','Seismometer Trajectory');
set(gcf,'Units','Normalized');

Jorge Luis Paredes Estacio 2023-7-30

Hi, @PARVATHY NAIRParbathy. I am reading that paper about the STA/LTA alborithm. I was wondering of you managed to finish that code as I am working on detecting P- and S-wave from a great bunch of records. I've got another algorithm but I would like to compare it with the one you may finish. My email is zs19084@bristol.ac.uk or jlparedese@gmail.com. Thank you.

PARVATHY NAIR 2023-7-31