A new efficient procedure for the estimation of onset times of seismic waves.

Abstract

A computationally efficient procedure was developed for the fitting of a locally stationary autoregressive model. The amount of computations is bounded by a function of the data length and the model order only and does not depend on the number of possible arrival times. This facilitates automatic determination of arrival time by an on-line system. The on-line system FUNIMAR (fast univariate case of minimum AIC method of AR model fitting) was developed to implement the procedure.The method was checked by applying it to weak seismic signals from earthquakes which were superimposed with background noises such as traffic noise, electronic hum noise, and heavy microtremor. Experimental results confirm the effectiveness of the method for determining the onset time of P-wave and suggest that an AR model of the fifth order is sufficient to determine the onset time of the wave even if it is buried in considerably heavy noises. It is also shown that the onset time of S-wave can also be successfully determined by the method.

Automatic S-Wave Picker for Local Earthquake Tomography

“…In practice, the method of Takanami and Kitagawa (1988) requires a noise model AIC forw i calculated forward in time starting at t NS and ending at t SS , assuming the noise part is included in the window from t NS to t NE as illustrated in the lower box of Figure 4. In addition, the signal model AIC back i is calculated backward in time starting at t SE and ending at t NE , where parts of the signal are expected to Figure 3.…”

“…Although multivariate AR approaches appear to be slightly more appropriate for robust S-wave picking (e.g., Takanami and Kitagawa, 1993;Leonard and Kennett, 1999), the application of the univariate method to single components (N, E, Q, and T) and combined components (E N) provides additional details about uncertainty of timing and phase identification. Our implementation is mainly based on the method of Takanami and Kitagawa (1988), which uses an univariate AR fitting approach to derive automatic arrival times. Ⓔ A detailed description of the AR modeling is provided in the supplement that is available in the electronic edition of BSSA.…”

Geometry of the Pamir‐Hindu Kush intermediate‐depth earthquake zone from local seismic data

“…An autoregressive Akaike Information Criterion (AR-AIC) based predictive picking algorithm as described in Takanami and Kitagawa [1988].…”

Comparison of recent S-wave indicating methods

“…Algorithm of Takanami-Kitagawa [5] was proposed in 1988 and uses the AR model to analyse seismic signal. Despite the fact of seismic signals being non-stationary, they can be divided into two subseries and each of them can be approximated by the AR model.…”

“…In this paper four recently developed methods estimating S-wave arrival are compared: the method operating on empirical mode decomposition and Teager-Kaiser operator [2], the modification of STA/LTA algorithm [3], the method using a nearest neighbour-based approach [4] and the algorithm operating on characteristic of signals' second moments. The methods will be also compared to wellknown algorithm based on the autoregressive model [5]. The algorithms will be tested in terms of their S-wave arrival identification accuracy on real data originating from International Research Institutions for Seismology (IRIS) database.…”