scholarly journals The Study of Automatic Picking of P and S Wave Arrival and Identification of Earthquake Sequence Pattern using Scalogram in Obspy (Python)

2021 ◽  
Vol 873 (1) ◽  
pp. 012014
Author(s):  
Sri Kiswanti ◽  
Indriati Retno Palupi ◽  
Wiji Raharjo ◽  
Faricha Yuna Arwa ◽  
Nela Elisa Dwiyanti
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Arrival Time ◽  
Continuous Wavelet ◽  
S Wave ◽  
S Waves ◽  
Automatic Picking ◽  
Earthquake Record ◽  
Initial Identification ◽  
Wave Arrival

Abstract Initial identification on an earthquake record (seismogram) is something that needs to be done precisely and accurately. Moreover, the discovery of a series of unexpected successive earthquake events has caused unpreparedness for the community and related agencies in tackling these events. Determining the arrival time of the P and S waves becomes an important parameter to finding the location of the earthquake source (hypocenter) as well as further information related to the earthquake event. However, manual steps that are currently often used are considered to be less effective, because it requires a lot of time in the process. Continuous Wavelet Transform (CWT) analysis can be a solution for this problem. With further CWT analysis in the form of a scalogram, can help to determine the arrival time of P and S waves automatically (automatic picking) becomes simpler. In addition, further CWT analysis can also be utilized to help identify the sequence of earthquake events (foreshock, mainshock, aftershock) through the resulting scalogram pattern.

First Arrival Time Identification Using Transfer Learning With Continuous Wavelet Transform Feature Images

2020 ◽  
Vol 17 (11) ◽  
pp. 2002-2006 ◽  
Author(s):  
Xiaofang Liao ◽  
Junxing Cao ◽  
Jiangtao Hu ◽  
Jiachun You ◽  
Xudong Jiang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Transfer Learning ◽  
Continuous Wavelet Transform ◽  
Arrival Time ◽  
Continuous Wavelet ◽  
First Arrival ◽  
First Arrival Time

scholarly journals Studi Automatic Picking Waktu Tiba Gelombang P dan S dengan Menggunakan Spektogram pada Obspy Python

2020 ◽  
Vol 8 (1) ◽  
pp. 77-82
Author(s):  
Indriati Retno Palupi ◽  
◽  
Wiji Raharjo ◽  
Keyword(s):  
Frequency Analysis ◽  
Arrival Time ◽  
Dominant Frequency ◽  
S Wave ◽  
Important Thing ◽  
Automatic Picking ◽  
Wave Arrival

One of important thing in locating hypocenter process is determine P and S arrival time of the seismogram. Beside that, frequency analysis by FFT method is needed to know the character of the seismogram, like dominant frequency. For further analysis, FFT method can be a good tools in determine P and S wave arrival time in the spectogram form. This process is called automatic picking.

scholarly journals Continuous Wavelet Transform of Schwartz Distributions in DL2′(Rn), n ≥ 1

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1106
Author(s):  
Jagdish N. Pandey
Keyword(s):  
Wavelet Transform ◽  
Function Space ◽  
Continuous Wavelet Transform ◽  
Inversion Formula ◽  
Continuous Wavelet ◽  
The Real ◽  
Schwartz Distributions ◽  
Distributional Sense ◽  
Wavelet Inversion

We define a testing function space DL2(Rn) consisting of a class of C∞ functions defined on Rn, n≥1 whose every derivtive is L2(Rn) integrable and equip it with a topology generated by a separating collection of seminorms {γk}|k|=0∞ on DL2(Rn), where |k|=0,1,2,… and γk(ϕ)=∥ϕ(k)∥2,ϕ∈DL2(Rn). We then extend the continuous wavelet transform to distributions in DL2′(Rn), n≥1 and derive the corresponding wavelet inversion formula interpreting convergence in the weak distributional sense. The kernel of our wavelet transform is defined by an element ψ(x) of DL2(Rn)∩DL1(Rn), n≥1 which, when integrated along each of the real axes X1,X2,…Xn vanishes, but none of its moments ∫Rnxmψ(x)dx is zero; here xm=x1m1x2m2⋯xnmn, dx=dx1dx2⋯dxn and m=(m1,m2,…mn) and each of m1,m2,…mn is ≥1. The set of such wavelets will be denoted by DM(Rn).

Sign in / Sign up

Export Citation Format

Share Document