Machine learning based seismic wave shocking property identification method

A technology of machine learning and identification methods, applied in the field of machine learning, can solve problems such as misleading and affecting seismological research work, and achieve the effect of safeguarding national interests and protecting human property

Inactive Publication Date: 2019-11-22
NORTHEASTERN UNIV
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Problems solved by technology

When observing the seismic wave signals of natural earthquakes and non-natural earthquakes recorded by various stations in the center of ...

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  • Machine learning based seismic wave shocking property identification method
  • Machine learning based seismic wave shocking property identification method
  • Machine learning based seismic wave shocking property identification method

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Embodiment Construction

[0020] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0021] The software environment of this embodiment is the WINDOWS 7 system, and the integrated development environment selects Pycharm IDE.

[0022] The identification method of seismic wave vibration properties based on machine learning includes the following steps:

[0023] Step 1: read the original seismic waveform data, and determine the epicentral distance of the seismic waveform that needs to be classified and identified;

[0024] The epicentral distance is the spherical distance between the station that recorded the waveform and the source;

[0025] The concrete steps of described step 1 are

[0026] Step 1.1: Use the Python library obspy in Anaconda for the seis...

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Abstract

The invention provides a machine learning based seismic wave shocking property identification method, and relates to the technical field of machine learning. The method comprises seismic waveform processing, characteristic value extraction, model training and model application. Distance between a seismic source and a station is calculated, epicenter distances are screened, three-component seismicwaveform data after epicenter distance screening is read, a long and short time window algorithm STA/LTA and an AIC method are used to accurately find an initial position of seismic waves, and furtherthe length of seismic data is intercepted. The time domain and frequency domain of seismic waveform are analyzed to extract the complexity, spectral ratio and waveform complexity/spectral ratio as input of an artificial neural network model. An artificial neural network model which includes two hidden layers and can identify a dichotomy problem is trained, and output represents the probability that the waveform belongs to certain type. The trained model can accurately and efficiently determine the type of seismic waveforms.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a machine learning-based method for identifying seismic wave vibration properties. Background technique [0002] Earthquakes can be divided into natural earthquakes and non-natural earthquakes according to their shaking properties. Natural earthquakes are caused by mutual extrusion and collision between the earth's plates and plates, causing dislocation and rupture at the edge of the plate and inside the plate. Unnatural earthquakes, also known as induced earthquakes, refer to abnormal seismic activities in local areas caused by human activities, such as artificial nuclear explosion tests or collapses. When observing the seismic wave signals of natural earthquakes and non-natural earthquakes recorded by various stations in the center of the seismic network, it is found that the waveforms of the two are very similar. Scholars brought misleading, affecting the research wo...

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Application Information

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IPC IPC(8): G01V1/30G01V1/28G06K9/62G06N3/04G06N20/00
CPCG01V1/307G01V1/28G01V1/30G06N20/00G06N3/04G06F18/24G06F18/214
Inventor 刘昕靓任涛王柳婷杨丹丹商冰冰
Owner NORTHEASTERN UNIV
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