We have investigated the potential automatic use of an onset picker based on autoregressive likelihood estimation. Both a single component version and a three component version of this method have been tested on data from events located in the Khibiny Massif of the Kola peninsula, recorded at the Apatity array, the Apatity three component station and the ARCESS array. Using this method, we have been able to estimate onset times to an accuracy (standard deviation) of about 0.05 s for P-phases and 0.15 0.20 s for S phases. These accuracies are as good as for analyst picks, and are considerably better than the accuracies of the current onset procedure used for processing of regional array data at NORSAR. In another application, we have develop...
This paper introduces a novel approach to estimate onsets in musical signals based on the phase spec...
This dataset contains the associated phase picks and event information from applying a deep learning...
2.1 Phase Timing and its Error Assessment.............. 4 2.2 Phase Identification and its Error Ass...
We have investigated the potential automatic use of an onset picker based on autoregressive likeliho...
ei sm ol og y Automated determination of S-phase arrival times using autoregressive prediction: appl...
Automatic seismogram interpretation and onset estimation are desirable goals facilitating the rapid ...
This work describes the automatic picking of the P-phase arrivals of the 3*106 seismic registers ori...
Autoregressive methods provide a very useful means of characterising a seismic record; calculating t...
Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground...
In a number of cases, the time series of seismic wave data under study may be considered to be stati...
The location of seismic events can be improved if accurate picks can be assigned for later seismic p...
Abstract Automatic-time picking continues to be a significant issue in seismo-gram analysis. Any ind...
Turning point detection is important in many areas. One application is forecasting the time of the n...
This work describes the automatic picking of the P-phase arrivals of the 3*106 seismic registers ori...
We propose two preprocessing algorithms suitable for climate time series. The first algorithm detec...
This paper introduces a novel approach to estimate onsets in musical signals based on the phase spec...
This dataset contains the associated phase picks and event information from applying a deep learning...
2.1 Phase Timing and its Error Assessment.............. 4 2.2 Phase Identification and its Error Ass...
We have investigated the potential automatic use of an onset picker based on autoregressive likeliho...
ei sm ol og y Automated determination of S-phase arrival times using autoregressive prediction: appl...
Automatic seismogram interpretation and onset estimation are desirable goals facilitating the rapid ...
This work describes the automatic picking of the P-phase arrivals of the 3*106 seismic registers ori...
Autoregressive methods provide a very useful means of characterising a seismic record; calculating t...
Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground...
In a number of cases, the time series of seismic wave data under study may be considered to be stati...
The location of seismic events can be improved if accurate picks can be assigned for later seismic p...
Abstract Automatic-time picking continues to be a significant issue in seismo-gram analysis. Any ind...
Turning point detection is important in many areas. One application is forecasting the time of the n...
This work describes the automatic picking of the P-phase arrivals of the 3*106 seismic registers ori...
We propose two preprocessing algorithms suitable for climate time series. The first algorithm detec...
This paper introduces a novel approach to estimate onsets in musical signals based on the phase spec...
This dataset contains the associated phase picks and event information from applying a deep learning...
2.1 Phase Timing and its Error Assessment.............. 4 2.2 Phase Identification and its Error Ass...