Refinement Analysis for Time-Frequency Characteristic of Blasting Seismic Exploration Signals

2014 ◽  
Vol 926-930 ◽  
pp. 3541-3544
Author(s):  
Ming Shou Zhong ◽  
Quan Min Xie ◽  
Tao Guo ◽  
Xing Bo Xie ◽  
Hao Quan Liu ◽  
...  
Keyword(s):  
Wavelet Transformation ◽  
Seismic Exploration ◽  
Blasting Vibration ◽  
Seismic Signals ◽  
Analysis Method ◽  
Effect Analysis ◽  
Time Frequency ◽  
Vibration Effect ◽  
New Research ◽  
Frequency Features

Accurate extraction of time-frequency features for blasting vibration signals has great significance for blasting seismic exploration, so time-frequency analysis method for blasting seismic signals was researched based on frequency slice wavelet transformation technology, and separation and extraction of time-frequency features were were successfully achieved. Frequency slice wavelet transformation can be introduced into blasting vibration effect analysis fields, it can provide a new research idea for refinement analysis of time-frequency characteristics, and it also has great significance for improving the effect of blasting seismic exploration in China.

scholarly journals Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1765
Author(s):  
Wei Xin ◽  
Fei Tian ◽  
Xiaocai Shan ◽  
Yongjian Zhou ◽  
Huazhong Rong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Machine Learning Algorithms ◽  
Seismic Exploration ◽  
Seismic Signals ◽  
Time Frequency ◽  
Mode Decomposition ◽  
New Perspective ◽  
Geological Constraints ◽  
Frequency Features

As deep carbonate fracture-cavity paleokarst reservoirs are deeply buried and highly heterogeneous, and the responded seismic signals have weak amplitudes and low signal-to-noise ratios. Machine learning in seismic exploration provides a new perspective to solve the above problems, which is rapidly developing with compelling results. Applying machine learning algorithms directly on deep seismic signals or seismic attributes of deep carbonate fracture-cavity reservoirs without any prior knowledge constraints will result in wasted computation and reduce the accuracy. We propose a method of combining geological constraints and machine learning to describe deep carbonate fracture-cavity paleokarst reservoirs. By empirical mode decomposition, the time–frequency features of the seismic data are obtained and then a sensitive frequency is selected using geological prior constraints, which is input to fuzzy C-means cluster for characterizing the reservoir distribution. Application on Tahe oilfield data shows the potential of highlighting subtle geologic structures that might otherwise escape unnoticed by applying machine learning directly.

Time Frequency Analysis of Blasting Vibration Superposing Signal under Different Millisecond Intervals

2012 ◽  
Vol 170-173 ◽  
pp. 3097-3101
Author(s):  
Wei Zhang ◽  
Shi Hai Chen
Keyword(s):  
Vibration Reduction ◽  
Maximum Amplitude ◽  
Vibration Signal ◽  
Dominant Frequency ◽  
Single Stage ◽  
Blasting Vibration ◽  
Reduction Theory ◽  
Time Frequency ◽  
Vibration Effect ◽  
Signal Time

Based on the measured single-stage blasting vibration signal, time-frequency characteristics of two single-stage superposing signals were analyzed under the condition of different millisecond intervals ranging from 1ms to 350ms, also, variation laws of dominant frequency, amplitude and energy of the blasting vibration superposing signal with the delay time and the determination method of rational millisecond interval of similar engineering were put forward. Then, changing laws of the millisecond interval with the interference effect was obtained. It is found that, millisecond delay blasting does not follow the disturbance vibration reduction theory strictly that the vibration effect is weakened when interval is (2n-1)T/2 and strengthened when interval is nT, and the more similar the vibration characteristics of single-stage signals are, the larger the maximum amplitude declining rate of the obtained superposing signal is.

Influence of Distance from Blast Center on Time-Frequency Characteristics of Blast Vibration Signals

2014 ◽  
Vol 1033-1034 ◽  
pp. 444-448
Author(s):  
Ming Sheng Zhao ◽  
Xu Guang Wang ◽  
En An Chi ◽  
Qiang Kang
Keyword(s):  
Seismic Wave ◽  
Seismic Waves ◽  
Low Frequency ◽  
Open Pit ◽  
Blasting Vibration ◽  
Analysis Method ◽  
Vibration Signals ◽  
Frequency Bands ◽  
Time Frequency ◽  
Energy Property

The distance from the blast center will directly change the blasting seismic wave wave’s energy property and eventually influence the structure’s response to the wave. To study its influence on the time-frequency (t-f) characteristics of blasting vibration signals, the single-hole blasting vibration test was conducted in Jinduicheng Open Pit Mine. Based on the measured data, wavelet analysis was used to decompose the measured signals, and signal segments at different frequency bands were got. RSPWVD quadratic form time-frequency analysis method was applied to analyze the segments’ t-f characteristics, and the domain frequencies of the blasting seismic waves under different distances from the blast center and the energy distribution and duration of the frequency bands were collected. The results show that the distance from the blasting center has a big impact on the domain frequency of the blasting seismic wave. With the increasing of the distance, the domain frequency reduces, its duration extends, the percentage of energy at the low frequency in the total energy increases and the duration of the frequency band extends. The research results provide the analysis base for understanding the influence of the distance from the blast center on signals’ t-f characteristic and studying vibration resistance and vibration reduction.

A SIMULATION STUDY ON THE DIESEL ENGINE COMBUSTION PROCESS BASED ON THE FIELD-EFFECT ANALYSIS METHOD OF COMBINED COMBUSTION

2015 ◽  
Vol 14 (1) ◽  
pp. 213-221 ◽  
Author(s):  
Lu-ming Zhao ◽  
Xiang-rong Li ◽  
Zhi-jie Li ◽  
Fu-shui Liu
Keyword(s):  
Diesel Engine ◽  
Simulation Study ◽  
Field Effect ◽  
Combustion Process ◽  
Analysis Method ◽  
Effect Analysis ◽  
Engine Combustion

Noncontact Evaluation of Defects in Thin Plate With Multimodes Lamb’s Waves and Wavelet Transform

2002 ◽  
Author(s):  
Morimasa Murase ◽  
Koichiro Kawashima
Keyword(s):  
Group Velocity ◽  
Thin Plate ◽  
Wavelet Transformation ◽  
Velocity Dispersion ◽  
Lamb Waves ◽  
Group Velocity Dispersion ◽  
Dispersion Curves ◽  
Contact Method ◽  
Intact Specimen ◽  
Time Frequency

Multimode’s Lamb waves in aluminum plates with various defects were excited by a Q-switched Nd:YAG laser. The Lamb waves past through the defects were received a laser interferometer. The received signals of the Lamb waves are processed by the wavelet transformation. The wavelet transformation is generally shown on the time-frequency domain. By dividing a propagation distance by the time, the group velocities are identified. In this way, group velocity dispersion maps of multimode’s Lamb waves are constructed with the received temporal signals. By changing the shape of the mother wavelet, Gabor function, we can identify the dispersion curves of the higher mode Lamb waves. The group velocity dispersion maps of a intact specimen agree well on theoretical dispersion curves of S0, A0, S1, A1, S2, A2, and A3 modes. The difference between the dispersion maps of the intact specimen and that with defects clearly visualizes the existence of defects. This non-contact method is effective for inspecting various defects in thin plate structures.

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