Discriminating non-seismic long-period pulses and noise to improve earthquake source inversion
© Sakai et al. 2016
Received: 26 January 2016
Accepted: 9 March 2016
Published: 1 April 2016
Broadband seismometers produce artifacts resembling long-period pulses (non-seismic pulses) that degrade centroid moment tensor (CMT) estimations based on waveform inversion of broadband seismic records in long-period bands (50–200 s). We propose a method to discriminate non-seismic pulses and long-period noise from seismic signals, which can be applied to automatic CMT inversion analysis. In this method, we calculate source amplitudes as peak-to-peak displacement amplitudes in individual long-period seismic records after each event has been corrected for medium attenuation and geometric spreading and then estimate the ratios of individual source amplitudes to the minimum source amplitude. Because source amplitude ratios for non-seismic pulses tend to be greater than those of the seismic signals, we use seismic records in CMT estimations only if their source amplitude ratios are lower than a threshold value (R). We tested this method using broadband seismic data from the Philippines and found that reprocessed inversion solutions using this method showed a clear improvement when using R = 11, although focal mechanism estimations were not entirely stable. To investigate the general applicability of this method, we analyzed broadband seismic data from F-net in Japan. Our analysis indicated that source amplitude ratios in F-net data ranged up to about 20, indicating that the threshold value may be dependent on station density. Given that F-net is one of the highest density networks in the world, we may assume that a threshold value between 10 and 20 is appropriate for application of our method for most regional broadband networks. Our synthetic tests indicated that source amplitude ratios can be as high as 103, although observed ratios are only within the range 10–20. This suggests that we happened to observe only events having focal mechanisms with source amplitude ratios of 10–20. Alternatively, these high source amplitude ratios can be explained by distortion of radiation patterns in the long-period band, which reduces maximum source amplitude ratios and affects CMT estimates.
Broadband seismic networks have been deployed in various regions of the world. Centroid moment tensor (CMT) solutions for earthquakes are routinely estimated by using broadband waveform data from global networks (Dziewonski et al. 1981; Sipkin 1994; Ekström et al. 2012). Near-real-time CMT inversions are performed using regional networks in Japan (e.g., Kawakatsu 1995; Fukuyama et al. 1998; Ito et al. 2006; Tsuruoka et al. 2009), California, USA (e.g., Dreger et al. 1998), New Zealand (Ristau 2008, 2013), Italy (Scognamiglio et al. 2009), Indonesia (Nakano et al. 2008, 2010a), the Philippines (Punongbayan et al. 2015; Bonita et al. 2015), and elsewhere. For large earthquakes beneath the ocean, rapid and correct CMT estimation is essential for prediction of tsunamis and evaluation of possible seismic and tsunami hazards.
It is recognized that broadband seismometers produce long-period pulse-like artifacts (Zahradník and Plešinger 2005, 2010; Delorey et al. 2008). These non-seismic pulses resemble the instrumental response to a step input in acceleration (Zahradník and Plešinger 2005) and occasionally appear during ground shaking. Such long-period pulses degrade CMT estimations based on waveform inversion of broadband seismic records in a long-period band, which may cause serious problems in tsunami prediction and early hazard assessment. Because the exact cause of the artifacts is unclear and there is no established way to correct for them, a method is needed to discriminate non-seismic pulses from seismic signals and avoid using waveform data including non-seismic pulses in automatic source inversion analysis.
In this paper, we propose a simple method to improve automatic CMT solutions by discriminating non-seismic pulses and long-period noise from usable waveform data, and we show that our method may be generally applicable to CMT analysis in other regional broadband seismic networks.
The SWIFT system developed by Nakano et al. (2008) estimates both the moment function and CMT based on waveform inversion of long-period seismic signals in the frequency domain, which enables efficient and rapid computations. SWIFT assumes a point source and a pure double-couple source mechanism in order to stabilize the solution when using data from a small number of stations. A spatial grid search of strike, dip, and rake angles identifies the best-fitting fault parameters and the source centroid that minimizes the normalized residual between the observed and synthetic displacement seismograms in the frequency domain. Because band-passed waveforms are used in the inversion, the resultant moment function is a band-passed form. The seismic moment is estimated from the moment function deconvolved from its band-passed form (Nakano et al. 2008). SWIFT has been used to study source mechanisms of earthquakes in various regions: Indonesia (Nakano et al. 2008, 2010a, b), the Philippines (Punongbayan et al. 2015; Bonita et al. 2015), Turkey (Görgün 2013; Görgün and Görgün 2015; Nakano et al. 2015), and Japan (Ando et al. 2012; Kumagai et al. 2012).
In the Philippines, the SWIFT system is triggered by receipt of an email at PHIVOLCS providing earthquake information determined by the SeisComP3 system (https://www.seiscomp3.org/) if an estimated earthquake magnitude (M) is greater than 4.5. Waveform data at individual stations within 1500 km of the epicenter are retrieved between 10 min before and 9 min after the earthquake origin time. The unprocessed waveform data are first checked for amplitude saturations and data gaps, and a band-pass filter between 50 and 100 s is then applied to the data that are free of saturations and gaps. The noise and signal amplitudes are estimated from peak-to-peak amplitudes during periods 200 s before and 482 s after the origin time, respectively, in each band-passed waveform. The band-passed waveforms with signal-to-noise amplitude ratios greater than 4 are selected. If the number of selected waveforms is greater than 4 and the selected waveforms are from more than 2 stations, the SWIFT inversion is performed. The waveform data are decimated to a sampling frequency of 0.5 Hz, and a total length of 512 s (30 s before and 482 s after the origin time) is used. The hypocenter given by SeisComP3 is used as an initial source location for the spatial grid search in which we use the adaptive grid spacing of Nakano et al. (2008). At each source node, a grid search of fault parameters (strike, dip, and rake angles) is conducted, and waveform inversion in the frequency domain is performed to estimate the moment function (band-passed form) for each combination of angles. We adopt the fault parameter and source location showing the minimum residual, in which the deconvolved form of the moment function is estimated to determine the seismic moment and moment magnitude (M w). If the estimated M w is greater than 7.0, the above processes are repeated for the waveform data band-passed between 50 and 200 s. Each automatic solution is manually checked, and the waveform traces including non-seismic pulses and long-period noise are discarded. A manual solution is then obtained by inversion using the selected waveform traces. Bonita et al. (2015) showed that the manual SWIFT CMT solutions for earthquakes in and around the Philippines were consistent with the corresponding CMT solutions estimated by the Global CMT Project (http://www.globalcmt.org/).
Automatic CMT solutions
We compared the 191 automatic solutions with their corresponding manual solutions and found that 24 of the automatic solutions provided degraded CMT solutions in which source locations for the automatic and manual solutions differed by more than 50 km. These 24 solutions were clearly degraded by non-seismic pulses and long-period noise.
Source amplitudes for automatic and manual CMT solutions
We estimated the ratios of the individual source amplitudes to the minimum source amplitude for each event (Fig. 4b). Most of the amplitude ratios for the manual CMT solutions were less than 10 and showed no dependence on M w, whereas those of the degraded automatic solutions had ratios greater than 10 (Fig. 4b). We thus considered whether a threshold value of source amplitude ratios might be useful to discriminate non-seismic pulses and long-period noise from seismic signals.
Application of the source amplitude ratio method
Source amplitudes of F-net data
Source amplitudes of synthetic and observed seismograms
Kawakatsu (1995) proposed a method of automatic CMT inversion that discarded waveforms with maximum-to-minimum ratios of root-mean-square amplitudes that were greater than 300 in long-period (45–100 s) seismograms at individual stations. We tested this method on the data providing the degraded CMT solutions from the Philippines network. We found that only three seismograms including the non-seismic pulses and long-period noise exceeded the threshold ratio of 300, whereas 45 seismograms including them were discriminated by our method using the source amplitude ratios with R = 11. As the threshold ratio was lowered, more non-seismic pulses and noise were discriminated, but seismic signals were also included. This approach using amplitude ratios is useful to discard seismograms containing transient spikes. However, our study demonstrated that to discriminate non-seismic pulses and long-period noise, source amplitude ratios provide a more effective means of discrimination than amplitude ratios.
Our analysis of data from F-net and from broadband seismic networks in the Philippines and Indonesia indicated that source amplitudes increase with increasing M w and that the range of source amplitude ratios is dependent on station density and focal mechanism. Maximum source amplitude ratios were around 10 for the Philippines and Indonesia networks and around 20 for F-net. This suggests that observations with a higher station density network provide better azimuthal coverage of source radiation patterns and result in larger R values. Given that F-net is among the highest density broadband seismic networks in the world, we can assume that the appropriate threshold of source amplitude ratio for application of our method lies between 10 and 20 and that it may be applicable for most of the regional broadband networks deployed to date.
Comparison of automatic and manual CMT solutions reprocessed after applying our method to data from the Philippines network showed a clear improvement. However, the results were not a perfect match and that the mechanism estimations were not entirely stable. We attributed the difference to the inability of our method to identify non-seismic pulses and long-period noise for source amplitude ratios less than the threshold, in which pulse and noise amplitudes were comparable to or smaller than seismic signal amplitudes. Our analysis of F-net data also indicated the existence of non-seismic pulses and long-period noise with large and small source amplitude ratios (orange circles in Fig. 9). To discriminate such small pulses and noise from seismic signals is a fundamentally difficult problem and remains unresolved. Future research is required to discriminate them and to further improve the accuracy of automatic solutions.
Our synthetic tests showed that the source amplitude ratios of synthetic seismograms based on CMT mechanisms calculated by NIED for individual events recorded by the Philippines network and F-net were similar to the source amplitude ratios of observed data. However, when we systematically changed strike, dip, and rake angles, the resultant maximum source amplitude ratios for the synthetic data were about 103, whereas the ratios in observed data are limited to the range 10–20. There are two plausible interpretations of this apparent inconsistency. One is that we happened to observe only events with focal mechanisms that produced source amplitude ratios within the range 10–20. Our synthetic tests showed that normalized frequencies decrease with increasing source amplitude ratio (Fig. 12), which indicates that very few events with mechanisms that produce larger source amplitude ratios were observed. The alternative interpretation is that distortion of radiation patterns in the long-period band reduces maximum source amplitude ratios and affects CMT estimates. Such distortion is known in high-frequency bands of around several hertz and has been explained by the path effect caused by scattering of seismic waves (e.g., Liu and Helmberger 1985; Takemura et al. 2009, 2015; Kumagai et al. 2010, 2011; Kobayashi et al. 2015). Because such scattering effects may not be dominant in the long-period band, wavefield distortion due to source finiteness and complexity and/or large-scale structures are possible causes. To validate these interpretations, we need further analysis of seismic waveform data and waveform simulations to be run for finite source rupture models in three-dimensional structures.
We analyzed automatic CMT solutions determined by the SWIFT system in the Philippines and found that they were affected by occasional long-period pulse-like artifacts and noise. To discriminate the non-seismic pulses and long-period noise from seismic signals and thus improve automatic inversion solutions, we investigated a method that uses source amplitude ratios estimated from individual waveforms. We set a threshold source amplitude ratio (R) above which waveforms were excluded from CMT solutions on the basis that they included non-seismic pulses and long-period noise. We found that the degraded CMT solutions were clearly improved by applying our method with R = 11, although the mechanism estimations were not entirely stable due to the existence of non-seismic pulses and long-period noise with source amplitude ratios less than the threshold.
Source amplitude ratios determined from waveform data from F-net in Japan, where station density is among the highest in the world, ranged up to about 20, which suggests that values of R between 10 and 20 are appropriate for application of our method for most regional broadband seismic networks.
Although source amplitude ratios of synthetic seismograms based on CMT solutions for individual events in the Philippines network and F-net were similar to source amplitude ratios determined from observed data, systematically changing strike, dip, and rake angles in CMT estimations produced maximum source amplitude ratios of up to about 103. This apparent inconsistency can be explained either by inclusion of only events with focal mechanisms that produce source amplitude ratios of 10–20, or by distortion of radiation patterns in the long-period band that reduces maximum source amplitude ratios and affects CMT estimates. Future studies are needed to further improve the accuracy of automatic solutions and to investigate the physical meaning of the range of source amplitude ratios in observed data.
TS analyzed the seismic data and wrote a first draft of this paper. HK developed the basic concept of the present work and revised the first draft. NP and JB provided inversion results for data from the Philippines network and NP for data from the Indonesia network. MN developed the SWIFT inversion programs. All authors read and approved the final manuscript.
We used data from F-net, which is managed by the National Research Institute for Earth Science and Disaster Prevention (NIED), Japan. We thank Jouji Senda for performing SWIFT inversion analyses. Comments from an anonymous reviewer and John Ristau helped to improve the manuscript. This work was supported by JST-JICA SATREPS.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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