A nearly 20-year hiatus in major seismic activity in southern California ended on 4 July 2019 with a sequence of intersecting earthquakes near the city of Ridgecrest, California. This sequence included a foreshock with a moment magnitude (Mw) of 6.4 followed by a Mw 7.1 mainshock nearly 34 hours later. Geodetic, seismic, and seismicity data provided an integrative view of this sequence, which ruptured an unmapped multiscale network of interlaced orthogonal faults. This complex fault geometry persists over the entire seismogenic depth range. The rupture of the mainshock terminated only a few kilometers from the major regional Garlock fault, triggering shallow creep and a substantial earthquake swarm. The repeated occurrence of multifault ruptures, as revealed by modern instrumentation and analysis techniques, poses a formidable challenge in quantifying regional seismic hazards.
Sparse seismic instrumentation in the oceans limits our understanding of deep Earth dynamics and submarine earthquakes. Distributed acoustic sensing (DAS), an emerging technology that converts optical fiber to seismic sensors, allows us to leverage pre-existing submarine telecommunication cables for seismic monitoring. Here we report observations of microseism, local surface gravity waves, and a teleseismic earthquake along a 4192-sensor ocean-bottom DAS array offshore Belgium. We observe in-situ how opposing groups of ocean surface gravity waves generate double-frequency seismic Scholte waves, as described by the Longuet-Higgins theory of microseism generation. We also extract P- and S-wave phases from the 2018-08-19 Fiji deep earthquake in the 0.01-1 Hz frequency band, though waveform fidelity is low at high frequencies. These results suggest significant potential of DAS in next-generation submarine seismic networks.
S U M M A R YAmbient seismic noise cross-correlations are now being used to detect temporal variations of seismic velocity, which are typically on the order of 0.1 per cent. At this small level, temporal variations in the properties of noise sources can cause apparent velocity changes. For example, the spatial distribution and frequency content of ambient noise have seasonal variations due to the seasonal hemispherical shift of storms. Here, we show that if the stretching method is used to measure time-shifts, then the temporal variability of noise frequency content causes apparent velocity changes due to the changes in both amplitude and phase spectra caused by waveform stretching. With realistic seasonal variations of frequency content in the Los Angeles Basin, our numerical tests produce about 0.05 per cent apparent velocity change, comparable to what Meier et al. observed in the Los Angeles Basin. We find that the apparent velocity change from waveform stretching depends on time windows and station-pair distances, and hence it is important to test a range of these parameters to diagnose the stretching bias. Better understanding of spatiotemporal noise source properties is critical for more accurate and reliable passive monitoring.
Distributed acoustic sensing (DAS) is a new, relatively inexpensive technology that is rapidly demonstrating its promise for recording earthquake waves and other seismic signals in a wide range of research and public safety arenas. It should significantly augment present seismic networks. For several important applications, it should be superior. It employs ordinary fiber‐optic cables, but not as channels for data among separate sophisticated instruments. With DAS, the hair‐thin glass fibers themselves are the sensors. Internal natural flaws serve as seismic strainmeters, kinds of seismic detector. Unused or dark fibers are common in fiber cables widespread around the globe, or in dedicated cables designed for special application, are appropriate for DAS. They can sample passing seismic waves at locations every few meters or closer along paths stretching for tens of kilometers. DAS arrays should enrich the three major areas of local and regional seismology: earthquake monitoring, imaging of faults and many other geologic formations, and hazard assessment. Recent laboratory and field results from DAS tests underscore its broad bandwidth and high‐waveform fidelity. Thus, while still in its infancy, DAS already has shown itself as the working heart—or perhaps ear drums—of a valuable new seismic listening tool. My colleagues and I expect rapid growth of applications. We further expect it to spread into such frontiers as ocean‐bottom seismology, glacial and related cryoseismology, and seismology on other solar system bodies.
Performance of earthquake early warning systems suffers from false alerts caused by local impulsive noise from natural or anthropogenic sources. To mitigate this problem, we train a generative adversarial network (GAN) to learn the characteristics of first‐arrival earthquake P waves, using 300,000 waveforms recorded in southern California and Japan. We apply the GAN critic as an automatic feature extractor and train a Random Forest classifier with about 700,000 earthquake and noise waveforms. We show that the discriminator can recognize 99.2% of the earthquake P waves and 98.4% of the noise signals. This state‐of‐the‐art performance is expected to reduce significantly the number of false triggers from local impulsive noise. Our study demonstrates that GANs can discover a compact and effective representation of seismic waves, which has the potential for wide applications in seismology.
S U M M A R YTheoretical studies on ambient seismic noise (ASN) predict that complete Green's function between seismic stations can be retrieved from cross correlation. However, only fundamental mode surface waves emerge in most studies involving real data. Here we show that Mohoreflected body wave (SmS) and its multiples can be identified with ASN for station pairs near their critical distances in the short period band (1-5 s). We also show that an uneven distribution of noise sources, such as mining activity and wind-topography interaction, can cause surface wave precursors, which mask weaker body wave phases.
More than 90% of the energy trapped on Earth by increasingly abundant greenhouse gases is absorbed by the ocean. Monitoring the resulting ocean warming remains a challenging sampling problem. To complement existing point measurements, we introduce a method that infers basin-scale deep-ocean temperature changes from the travel times of sound waves that are generated by repeating earthquakes. A first implementation of this seismic ocean thermometry constrains temperature anomalies averaged across a 3000-kilometer-long section in the equatorial East Indian Ocean with a standard error of 0.0060 kelvin. Between 2005 and 2016, we find temperature fluctuations on time scales of 12 months, 6 months, and ~10 days, and we infer a decadal warming trend that substantially exceeds previous estimates.
[1] Seismic body waves that sample Earth's core are indispensable for studying the most remote regions of the planet. Traditional core phase studies rely on well-defined earthquake signals, which are spatially and temporally limited. We show that, by stacking ambient-noise cross-correlations between USArray seismometers, body wave phases reflected off the outer core (ScS), and twice refracted through the inner core (PKIKP 2 ) can be clearly extracted. Temporal correlation between the amplitude of these core phases and global seismicity suggests that the signals originate from distant earthquakes and emerge due to array interferometry. Similar results from a seismic array in New Zealand demonstrate that our approach is applicable in other regions and with fewer station pairs. Extraction of core phases by interferometry can significantly improve the spatial sampling of the deep Earth because the technique can be applied anywhere broadband seismic arrays exist.
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