First arrival time picking for microseismic data based on DWSW algorithm

2018 ◽  
Vol 22 (4) ◽  
pp. 833-840 ◽  
Author(s):  
Yue Li ◽  
Yue Wang ◽  
Hongbo Lin ◽  
Tie Zhong
Keyword(s):  
Arrival Time ◽  
Microseismic Data ◽  
First Arrival ◽  
First Arrival Time ◽  
Arrival Time Picking

An Adaptive FCM-based Approach of First Arrival Time Picking for Microseismic Data

2021 ◽  
Author(s):  
Zhiqiang Lan ◽  
Yaojun Wang ◽  
Peng Wang ◽  
Peng Gao ◽  
Jiandong Liang
Keyword(s):  
Arrival Time ◽  
Microseismic Data ◽  
First Arrival ◽  
First Arrival Time ◽  
Arrival Time Picking

scholarly journals ANALYSES OF THE AGE OF GENES AND THE FIRST ARRIVAL TIMES IN A FINITE POPULATION

Genetics ◽  
1983 ◽  
Vol 105 (4) ◽  
pp. 1041-1059
Author(s):  
Takeo Maruyama ◽  
Paul A Fuerst
Keyword(s):  
Arrival Time ◽  
Mutant Gene ◽  
Intermediate Frequency ◽  
Lag Phase ◽  
Reversal Theory ◽  
Infinite Allele Model ◽  
First Arrival ◽  
Mean And Variance ◽  
The Mean ◽  
First Arrival Time

ABSTRACT The age of a mutant gene is studied using the infinite allele model in which every mutant is new and selectively neutral. Based on a time reversal theory of Markov processes, we develop a method of mathematical analysis that is considerably simpler for calculating the various statistics of the age than previous methods. Formulas for the mean and variance and for the distribution of age are presented together with some examples of relevance to cases in natural populations.—Theoretical studies of the first arrival time of an allele to a specified frequency, given an initially monomorphic condition of the locus, are presented. It is shown that, beginning with an allele that has frequency p = 1 or an allele with frequency p = 1/2N, there is an initial lag phase in which there is virtually no chance of an allele with a specified intermediate frequency appearing in the population. The distribution of the first arrival time is also presented. The distribution shows several characteristics that are not immediately obvious from a consideration of only the mean and variance of first arrival time. Especially noteworthy is the existence of a very long tail to the distribution. We have also studied the distribution of the age of an allele in the population. Again, the distribution of this measure is shown to be more informative for several questions than are the mean and variance alone.

Arrival-time picking of microseismic events based on MSNet

Geophysics ◽  
2021 ◽  
pp. 1-66
Author(s):  
Guanqun Sheng ◽  
Shuangyu Yang ◽  
Xiaolong Guo ◽  
Xingong Tang
Keyword(s):  
Arrival Time ◽  
Spatial Information ◽  
Synthetic Data ◽  
Microseismic Monitoring ◽  
Monitoring Data ◽  
Convolutional Network ◽  
Low Snr ◽  
Microseismic Data ◽  
Microseismic Events ◽  
Arrival Time Picking

Arrival-time picking of microseismic events is a critical procedure in microseismic data processing. However, as field monitoring data contain many microseismic events with low signal-to-noise ratios (SNRs), traditional arrival-time picking methods based on the instantaneous characteristics of seismic signals cannot meet the picking accuracy and efficiency requirements of microseismic monitoring owing to the large volume of monitoring data. Conversely, methods based on deep neural networks can significantly improve arrival-time picking accuracy and efficiency in low-SNR environments. Therefore, we propose a deep convolutional network that combines the U-net and DenseNet approaches to pick arrival times automatically. This novel network, called MSNet not only retains the spatial information of any input signal or profile based on the U-net, but also extracts and integrates more essential features of events and non-events through dense blocks, thereby further improving the picking accuracy and efficiency. An effective workflow is developed to verify the superiority of the proposed method. First, we describe the structure of MSNet and the workflow of the proposed picking method. Then, datasets are constructed using variable microseismic traces from field microseismic monitoring records and from the finite-difference forward modeling of microseismic data to train the network. Subsequently, hyperparameter tuning is conducted to optimize the MSNet. Finally, we test the MSNet using modeled signals with different SNRs and field microseismic data from different monitoring areas. By comparing the picking results of the proposed method with the results of U-net and short-term average and long-term average (STA/LTA) methods, the effectiveness of the proposed method is verified. The arrival picking results of synthetic data and microseismic field data show that the proposed network has increased adaptability and can achieve high accuracy for picking the arrival-time of microseismic events.

A comparative study of first arrival time computation for 3‐D imhomogeneous isotropic velocity models

1998 ◽  
Author(s):  
F. E. Akbar ◽  
C. Calderon‐Macias ◽  
V. Sen ◽  
M. K. Sen ◽  
P. L. Stoffa
Keyword(s):  
Comparative Study ◽  
Arrival Time ◽  
Velocity Models ◽  
First Arrival ◽  
First Arrival Time
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