Continuous seismograms contain a wealth of information with a large variety of signals with different origin. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of signals in continuous single‐station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an independent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two‐day‐long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics. At a low level in the cluster hierarchy, we obtain three clusters related to anthropogenic and ambient seismic noise and one cluster related to earthquake activity. At a high level in the cluster hierarchy, we identify a seismic burst that includes around 200 events with similar waveforms and high‐frequent signals with correlating envelopes and an anthropogenic origin. The application shows that the cluster hierarchy helps to identify particular families of signals and to extract subclusters for further analysis. This is valuable when certain types of signals, such as earthquakes, are under‐represented in the data. The proposed method may also successfully discover new types of signals since it is entirely data‐driven.
Seismograms contain multiple sources of seismic waves, from distinct transient signals such as earthquakes to continuous ambient seismic vibrations such as microseism. Ambient vibrations contaminate the earthquake signals, while the earthquake signals pollute the ambient noise’s statistical properties necessary for ambient-noise seismology analysis. Separating ambient noise from earthquake signals would thus benefit multiple seismological analyses. This work develops a multitask encoder–decoder network named WaveDecompNet to separate transient signals from ambient signals directly in the time domain for 3-component seismograms. We choose the active-volcanic Big Island in Hawai’i as a natural laboratory given its richness in transients (tectonic and volcanic earthquakes) and diffuse ambient noise (strong microseism). The approach takes a noisy 3-component seismogram as input and independently predicts the 3-component earthquake and noise waveforms. The model is trained on earthquake and noise waveforms from the STandford EArthquake Dataset (STEAD) and on the local noise of seismic station IU.POHA. We estimate the network’s performance by using the explained variance metric on both earthquake and noise waveforms. We explore different neural network designs for WaveDecompNet and find that the model with long-short-term memory (LSTM) performs best over other structures. Overall, we find that WaveDecompNet provides satisfactory performance down to a signal-to-noise ratio (SNR) of 0.1. The potential of the method is (1) to improve broad-band SNR of transient (earthquake) waveforms and (2) to improve local ambient noise to monitor the Earth’s structure using ambient noise signals. To test this, we apply a short-time average to a long-time average filter and improve the number of detected events. We also measure single-station cross-correlation functions of the recovered ambient noise and establish their improved coherence through time and over different frequency bands. We conclude that WaveDecompNet is a promising tool for a broad range of seismological research.
more » « less- Award ID(s):
- 2124722
- NSF-PAR ID:
- 10370897
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Geophysical Journal International
- Volume:
- 231
- Issue:
- 3
- ISSN:
- 0956-540X
- Format(s):
- Medium: X Size: p. 1806-1822
- Size(s):
- ["p. 1806-1822"]
- Sponsoring Org:
- National Science Foundation
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SUMMARY A fleet of autonomously drifting profiling floats equipped with hydrophones, known by their acronym mermaid, monitors worldwide seismic activity from inside the oceans. The instruments are programmed to detect and transmit acoustic pressure conversions from teleseismic P wave arrivals for use in mantle tomography. Reporting seismograms in near-real time, within hours or days after they were recorded, the instruments are not usually recovered, but if and when they are, their memory buffers can be read out. We present a unique 1-yr-long data set of sound recorded at frequencies between 0.1 and 20 Hz in the South Pacific around French Polynesia by a mermaid float that was, in fact, recovered. Using time-domain, frequency-domain and time-frequency-domain techniques to comb through the time-series, we identified signals from 213 global earthquakes known to published catalogues, with magnitudes 4.6–8.0, and at epicentral distances between 24° and 168°. The observed signals contain seismoacoustic conversions of compressional and shear waves travelling through crust, mantle and core, including P, S, Pdif, Sdif, PKIKP, SKIKS, surface waves and hydroacoustic T phases. Only 10 earthquake records had been automatically reported by the instrument—the others were deemed low-priority by the onboard processing algorithm. After removing all seismic signals from the record, and also those from other transient, dominantly non-seismic, sources, we are left with the infrasonic ambient noise field recorded at 1500 m depth. We relate the temporally varying noise spectral density to a time-resolved ocean-wave model, WAVEWATCH III. The noise record is extremely well explained, both in spectral shape and in temporal variability, by the interaction of oceanic surface gravity waves. These produce secondary microseisms at acoustic frequencies between 0.1 and 1 Hz according to the well-known frequency-doubling mechanism.more » « less
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SUMMARY We recently found the original Omori seismograms recorded at Hongo, Tokyo, of the 1922 Atacama, Chile, earthquake (MS = 8.3) in the historical seismogram archive of the Earthquake Research Institute (ERI) of the University of Tokyo. These recordings enable a quantitative investigation of long-period seismic radiation from the 1922 earthquake. We document and provide interpretation of these seismograms together with a few other seismograms from Mizusawa, Japan, Uppsala, Sweden, Strasbourg, France, Zi-ka-wei, China and De Bilt, Netherlands. The 1922 event is of significant historical interest concerning the cause of tsunami, discovery of G wave, and study of various seismic phase and first-motion data. Also, because of its spatial proximity to the 1943, 1995 and 2015 great earthquakes in Chile, the 1922 event provides useful information on similarity and variability of great earthquakes on a subduction-zone boundary. The 1922 source region, having previously ruptured in 1796 and 1819, is considered to have significant seismic hazard. The focus of this paper is to document the 1922 seismograms so that they can be used for further seismological studies on global subduction zones. Since the instrument constants of the Omori seismographs were only incompletely documented, we estimate them using the waveforms of the observed records, a calibration pulse recorded on the seismogram and the waveforms of better calibrated Uppsala Wiechert seismograms. Comparison of the Hongo Omori seismograms with those of the 1995 Antofagasta, Chile, earthquake (Mw = 8.0) and the 2015 Illapel, Chile, earthquake (Mw = 8.3) suggests that the 1922 event is similar to the 1995 and 2015 events in mechanism (i.e. on the plate boundary megathrust) and rupture characteristics (i.e. not a tsunami earthquake) with Mw = 8.6 ± 0.25. However, the initial fine scale rupture process varies significantly from event to event. The G1 and G2, and R1 and R2 of the 1922 event are comparable in amplitude, suggesting a bilateral rupture, which is uncommon for large megathrust earthquakes.more » « less
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SUMMARY Accurate synthetic seismic wavefields can now be computed in 3-D earth models using the spectral element method (SEM), which helps improve resolution in full waveform global tomography. However, computational costs are still a challenge. These costs can be reduced by implementing a source stacking method, in which multiple earthquake sources are simultaneously triggered in only one teleseismic SEM simulation. One drawback of this approach is the perceived loss of resolution at depth, in particular because high-amplitude fundamental mode surface waves dominate the summed waveforms, without the possibility of windowing and weighting as in conventional waveform tomography.
This can be addressed by redefining the cost-function and computing the cross-correlation wavefield between pairs of stations before each inversion iteration. While the Green’s function between the two stations is not reconstructed as well as in the case of ambient noise tomography, where sources are distributed more uniformly around the globe, this is not a drawback, since the same processing is applied to the 3-D synthetics and to the data, and the source parameters are known to a good approximation. By doing so, we can separate time windows with large energy arrivals corresponding to fundamental mode surface waves. This opens the possibility of designing a weighting scheme to bring out the contribution of overtones and body waves. It also makes it possible to balance the contributions of frequently sampled paths versus rarely sampled ones, as in more conventional tomography.
Here we present the results of proof of concept testing of such an approach for a synthetic 3-component long period waveform data set (periods longer than 60 s), computed for 273 globally distributed events in a simple toy 3-D radially anisotropic upper mantle model which contains shear wave anomalies at different scales. We compare the results of inversion of 10 000 s long stacked time-series, starting from a 1-D model, using source stacked waveforms and station-pair cross-correlations of these stacked waveforms in the definition of the cost function. We compute the gradient and the Hessian using normal mode perturbation theory, which avoids the problem of cross-talk encountered when forming the gradient using an adjoint approach. We perform inversions with and without realistic noise added and show that the model can be recovered equally well using one or the other cost function.
The proposed approach is computationally very efficient. While application to more realistic synthetic data sets is beyond the scope of this paper, as well as to real data, since that requires additional steps to account for such issues as missing data, we illustrate how this methodology can help inform first order questions such as model resolution in the presence of noise, and trade-offs between different physical parameters (anisotropy, attenuation, crustal structure, etc.) that would be computationally very costly to address adequately, when using conventional full waveform tomography based on single-event wavefield computations.
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Abstract Rapid earthquake magnitude estimation from real-time space-based geodetic observation streams provides an opportunity to mitigate the impact of large and potentially damaging earthquakes by issuing low-latency warnings prior to any significant and destructive shaking. Geodetic contributions to earthquake characterization and rapid magnitude estimation have evolved in the last 20 yr, from post-processed seismic waveforms to, more recently, improved capacity of regional geodetic networks enabled real-time Global Navigation Satellite System seismology using precise point positioning (PPP) displacement estimates. In addition, empirical scaling laws relating earthquake magnitude to peak ground displacement (PGD) at a given hypocentral distance have proven effective in rapid earthquake magnitude estimation, with an emphasis on performance in earthquakes larger than ∼Mw 6.5 in which near-field seismometers generally saturate. Although the primary geodetic contributions to date in earthquake early warning have focused on the use of 3D position estimates and displacements, concurrent efforts in time-differenced carrier phase (TDCP)-derived velocity estimates also have demonstrated that this methodology has utility, including similarly derived empirical scaling relationships. This study builds upon previous efforts in quantifying the ambient noise of three-component ground-displacement and ground-velocity estimates. We relate these noise thresholds to expected signals based on published scaling laws. Finally, we compare the performance of PPP-derived PGD to TDCP-derived peak ground velocity (PGV), given several rich event datasets. Our results indicate that TDCP-PGV is more likely than PPP-PGD to detect intermediate magnitude (∼Mw 5.0–6.0) earthquakes, albeit with greater magnitude estimate uncertainty and across smaller epicentral distances. We conclude that the computationally lightweight TDCP-derived PGV magnitude estimation is complementary to PPP-derived PGD magnitude estimates, which could be produced at the network edge at high rates and with increased sensitivity to ground motion than current PPP estimates.more » « less
