Documentation
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Index ¶
- func AICPick(data []float64, start, end int) (int, error)
- func AICSimple(data []float64) []float64
- func ARCoefficients(data []float64, order int) ([]float64, error)
- func ARPick(vertical, north, east []float64, ...) (pTime, sTime float64, err error)
- func ARResidual(data, coefficients []float64) []float64
- func CarlStaTrig(a []float64, nsta, nlta int, ratio, quiet float64) []float64
- func ClassicStaLta(a []float64, nsta, nlta int) []float64
- func DelayedStaLta(a []float64, nsta, nlta int) []float64
- func EnergyRatio(data []float64, nsta, nlta int) ([]float64, error)
- func KurtosisCharacteristic(data []float64, window int) ([]float64, error)
- func RecursiveStaLta(a []float64, nsta, nlta int) []float64
- func TriggerOnset(charfct []float64, thres1, thres2 float64, maxLen int, maxLenDelete bool) [][]int
- func ZDetect(a []float64, nsta int) []float64
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func AICPick ¶
AICPick returns the minimum finite AIC sample in [start, end). It is useful for narrowing an arrival search after a coarse trigger has found a window.
func AICSimple ¶
AICSimple calculates Maeda's sample-wise Akaike Information Criterion. The final value is repeated so the output has the same length as input.
func ARCoefficients ¶
ARCoefficients estimates autoregressive coefficients using ordinary least squares. The model is x[t] = c[0]x[t-1] + ... + c[p-1]x[t-p].
func ARPick ¶
func ARPick(vertical, north, east []float64, sampleRate, lowFrequency, highFrequency, ltaP, staP, ltaS, staS float64, orderP, orderS int, varianceP, varianceS float64, pickS bool) (pTime, sTime float64, err error)
ARPick returns P and optionally S arrival times in seconds using the ObsPy/ Akazawa AR-AIC and STA/LTA workflow. varianceP and varianceS are the P/S variance-window lengths in seconds, corresponding to ObsPy l_p/l_s.
func ARResidual ¶
ARResidual returns absolute one-step prediction errors for an AR model.
func CarlStaTrig ¶
Computes the carlSTAtrig characteristic function. Translated from Obspy. https://docs.obspy.org/tutorial/code_snippets/trigger_tutorial.html#carl-sta-trig
func ClassicStaLta ¶
Computes the standard STA/LTA from a given input array a. The length of the STA is given by nsta in samples, respectively is the length of the LTA given by nlta in samples. Translated from Obspy. https://docs.obspy.org/tutorial/code_snippets/trigger_tutorial.html#classic-sta-lta
func DelayedStaLta ¶
Delayed STA/LTA. Translated from Obspy. https://docs.obspy.org/tutorial/code_snippets/trigger_tutorial.html#delayed-sta-lta
func EnergyRatio ¶
EnergyRatio returns a short-window to long-window mean-square ratio. The initial long-window samples are zeroed because no stable LTA exists yet.
func KurtosisCharacteristic ¶
KurtosisCharacteristic computes excess kurtosis over a trailing window. It is useful for impulsive arrivals and is zero until a complete window is available.
func RecursiveStaLta ¶
Recursive STA/LTA, translated from Obspy. https://docs.obspy.org/tutorial/code_snippets/trigger_tutorial.html#recursive-sta-lta
func TriggerOnset ¶
Types ¶
This section is empty.
Source Files