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STA/LTA first break picking method C language code
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STA (Short Term Average) and LTA (Long Term Average) are two commonly used seismic signal processing methods, commonly used for automatic detection and location of seismic events. The basic idea of the STA/LTA method is to detect the presence of seismic events by comparing the short-term average (STA) and long-term average (LTA) of seismic signals. Specifically, the STA/LTA method first performs sliding time window averaging on the input seismic signal to obtain two time-domain signals, STA and LTA. Then, calculate the ratio of STA and LTA, that is, the STA/LTA ratio. When the STA/LTA ratio exceeds a certain threshold, it is generally considered that there is a seismic event. The advantage of the STA/LTA method is that it is simple to understand and easy to implement. However, it also has some disadvantages, such as being sensitive to setting thresholds and requiring adjustments based on specific datasets and tasks; In addition, the STA/LTA method is not accurate enough and can easily misjudge noise as seismic signals. Therefore, it is often necessary to use it in combination with other methods to improve the detection and positioning accuracy of seismic events.
huanggt001
2023-04-03
0
1
AIC microseismic first break pickup
no vote
AIC (Akaike Information Criterion) is a statistical model selection method used to compare the fitting capabilities of different models. Its principle is based on information theory and provides a method for balancing model fit and model complexity. The AIC method was proposed by the Japanese statistician Hirotsugu Akaike in 1974. The basic idea is that the better the fitting ability of the model under given data, the smaller the AIC value of the model. The calculation formula for the AIC method is as follows: AIC=- 2 ln (L)+2 k, where L is the maximum likelihood function value of the model and k is the number of parameters of the model. The smaller the AIC value, the better the model. Because with the same degree of fit, models with smaller AIC values have fewer parameters, which means they are more simple. Therefore, the AIC method can prevent the model from over fitting data and improve the generalization ability of the model. The AIC method is not only suitable for linear models, but also for generalized linear models, nonlinear models, and time series models. It has been widely used in many fields, such as economics, ecology, physics, psychology, and biostatistics.
huanggt001
2023-04-03
0
1
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