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  • Articles  (6,789)
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  • Articles  (6,789)
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  • 1
    Publication Date: 2020-10-28
    Description: We show the equivalence of earthquake-induced ground acceleration and water-pressure waveforms for the case of collocated hydrophones and seafloor seismometers installed in shallow water. In particular, the comparison of the waveforms and amplitude spectra of the acceleration and water-pressure signals confirms the existence of a frequency range of “forced oscillations” in which the water-pressure variations are proportional to the vertical component of the ground acceleration. We demonstrate the equivalence of the acceleration and water-pressure signals for a set of local earthquakes (epicenter distance of a few tens of kilometers) and regional earthquakes with a wide range of magnitude (2.7
    Print ISSN: 0895-0695
    Electronic ISSN: 1938-2057
    Topics: Geosciences
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  • 2
    Publication Date: 2020-10-28
    Description: In recent years, as the amount of seismic data has grown rapidly, it is very important to develop a fast and reliable event detection and association algorithm. Generally, event detection is first performed on individual stations followed by event association through linking phase arrivals to a common event generating them. This study considers earthquake detection as the problem of image classification and convolutional neural networks (CNNs), as some of the widely used deep-learning tools in image processing, can be well used to solve this problem. In contrast to existing studies training the network using seismic data from individual stations, in this study, we train a CNN model jointly using records of multiple stations. Because the CNN automatically synthesizes information among multiple stations, the detector can more reliably detect seismic events and is less affected by spurious signals. The CNN is trained using aftershock data of the 2013 Mw 6.6 Lushan earthquake. We have applied it on two very different datasets of Gofar transform fault, East Pacific Rise and Changning shale gas field in southern Sichuan basin, China. The tests show that the trained CNN has strong generalization ability and is flexible with the number of available stations, different instrument types, and different data sampling rates. It can detect many more events than the conventional short-term average/long-term average detector and is more efficient than template-matching methods.
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  • 3
    Publication Date: 2020-10-28
    Description: We provide an overview of a 2019 workshop on the use of fragile geologic features (FGFs) to evaluate seismic hazard models. FGFs have been scarcely utilized in the evaluation of seismic hazard models, despite nearly 30 yr having passed since the first recognition of their potential value. Recently, several studies have begun to focus on the implementation of FGFs in seismic hazard modeling. The workshop was held to capture a “snapshot” of the state-of-the-art in FGF work and to define key research areas that would increase confidence in FGF-based evaluation of seismic hazard models. It was held at the annual meeting of the Southern California Earthquake Center on 8 September 2019, and the conveners were Mark Stirling (University of Otago, New Zealand) and Michael Oskin (University of California, Davis). The workshop attracted 44 participants from a wide range of disciplines. The main topics of discussion were FGF fragility age estimation (age at which an FGF achieved its current fragile geometry), fragility estimation, FGF-based evaluation of seismic hazard models, and ethical considerations relating to documentation and preservation of FGFs. There are now many scientists working on, or motivated to work on, FGFs, and more types of FGFs are being worked on than just the precariously balanced rock (PBR) variety. One of the ideas presented at the workshop is that fragility ages for FGFs should be treated stochastically rather than assuming that all share a common age. In a similar vein, new studies propose more comprehensive methods of fragility assessment beyond peak ground acceleration and peak ground velocity-based approaches. Two recent studies that apply PBRs to evaluate probabilistic seismic hazard models use significantly different methods of evaluation. Key research needs identified from the workshop will guide future, focused efforts that will ultimately facilitate the uptake of FGFs in seismic hazard analysis.
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  • 4
    Publication Date: 2020-10-28
    Description: Local seismic events recorded by the large-N Incorporated Research Institutions for Seismology Community Wavefield Experiment in Oklahoma are used to estimate Moho reflections near the array. For events within 50 km of the center of the array, normal moveout corrections and receiver stacking are applied to identify the PmP and SmS Moho reflections on the vertical and transverse components. Corrections for the reported focal depths are applied to a uniform event depth. To stack signals from multiple events, further static corrections of the envelopes of the Moho reflected arrivals from the individual event stacks are applied. The multiple-event stacks are then used to estimate the pre-critical PmP and SmS arrivals, and an average Poisson’s ratio of 1.77±0.02 was found for the crust near the array. Using a modified Oklahoma Geological Survey (OGS) velocity model with this Poisson’s ratio, the time-to-depth converted PmP and SmS arrivals resulted in a Moho depth of 41±0.6  km. The modeling of wide-angle Moho reflections for selected events at epicenter-to-station distances of 90–135 km provides additional constraints, and assuming the modified OGS model, a Moho depth of 40±1  km was inferred. The difference between the pre-critical and wide-angle Moho estimates could result from some lateral variability between the array and the wide-angle events. However, both estimates are slightly shallower than the original OGS model Moho depth of 42 km, and this could also result from a somewhat faster lower crust. This study shows that local seismic events, including induced events, can be utilized to estimate properties and structure of the crust, which, in turn, can be used to better understand the tectonics of a given region. The recording of local seismicity on large-N arrays provides increased lateral phase coherence for the better identification of precritical and wide-angle reflected arrivals.
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  • 5
    Publication Date: 2020-10-28
    Description: Hydraulic fracturing (HF) at Preston New Road (PNR), Lancashire, United Kingdom, in August 2019, induced a number of felt earthquakes. The largest event (ML 2.9) occurred on 26 August 2019, approximately three days after HF operations at the site had stopped. Following this, in November 2019, the United Kingdom Government announced a moratorium on HF for shale gas in England. Here we provide an analysis of the microseismic observations made during this case of HF-induced fault activation. More than 55,000 microseismic events were detected during operations using a downhole array, the vast majority measuring less than Mw 0. Event locations revealed the growth of hydraulic fractures and their interaction with several preexisting structures. The spatiotemporal distribution of events suggests that a hydraulic pathway was created between the injection points and a nearby northwest–southeast-striking fault, on which the largest events occurred. The aftershocks of the ML 2.9 event clearly delineate the rupture plane, with their spatial distribution forming a halo of activity around the mainshock rupture area. Across clusters of events, the magnitude distributions are distinctly bimodal, with a lower Gutenberg–Richter b-value for events above Mw 0, suggesting a break in scaling between events associated with hydraulic fracture propagation, and events associated with activation of the fault. This poses a challenge for mitigation strategies that rely on extrapolating microseismicity observed during injection to forecast future behavior. The activated fault was well oriented for failure in the regional stress field, significantly more so than the fault activated during previous operations at PNR in 2018. The differing orientations within the stress field likely explain why this PNR-2 fault produced larger events compared with the 2018 sequence, despite receiving a smaller volume of injected fluid. This indicates that fault orientation and in situ stress conditions play a key role in controlling the severity of seismicity induced by HF.
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  • 6
    Publication Date: 2020-10-21
    Description: Trains are now recognized as powerful sources for seismic interferometry based on noise correlation, but the optimal use of these signals still requires a better understanding of their source mechanisms. Here, we present a simple approach for modeling train-generated signals inspired by early work in the engineering community, assuming that seismic waves are emitted by sleepers regularly spaced along the railway and excited by passing train wheels. Our modeling reproduces well seismological observations of tremor-like emergent signals and of their harmonic spectra. We illustrate how these spectra are modulated by wheel spacing, and how their high-frequency content is controlled by the distribution of axle loads over the rail, which mainly depends on ground stiffness beneath the railway. This is summarized as a simple rule of thumb that predicts the frequency bands in which most of train-radiated energy is expected, as a function of train speed and of axle distance within bogies. Furthermore, we identify two end-member mechanisms—single stationary source versus single moving load—that explain two types of documented observations, characterized by different spectral signatures related to train speed and either wagon length or sleeper spacing. In view of using train-generated signals for seismic applications, an important conclusion is that the frequency content of the signals is dominated by high-frequency harmonics and not by fundamental modes of vibrations. Consequently, most train traffic worldwide is expected to generate signals with a significant high-frequency content, in particular in the case of trains traveling at variable speeds that produce truly broadband signals. Proposing a framework for predicting train-generated seismic wavefields over meters to kilometers distance from railways, this work paves the way for high-resolution passive seismic imaging and monitoring at different scales with applications to near-surface surveys (aquifers, civil engineering), natural resources exploration, and natural hazard studies (landslides, earthquakes, and volcanoes).
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  • 7
    Publication Date: 2020-10-21
    Description: Long-term and large-scale observations of dynamic earthquake triggering are urgently needed to understand the mechanism of earthquake interaction and assess seismic hazards. We developed a robust Python package termed DynTriPy to automatically detect dynamic triggering signals by distinguishing anomalous seismicity after the arrival of remote earthquakes. This package is an efficient implementation of the high-frequency power integral ratio algorithm, which is suitable for processing big data independent of earthquake catalogs or subjective judgments and can suppress the influence of noise and variations in the background seismicity. Finally, a confidence level of dynamic triggering (0–1) is statistically yielded. DynTriPy is designed to process data from multiple stations in parallel, taking advantage of rapidly expanding seismic arrays to monitor triggering on a global scale. Various data formats are supported, such as Seismic Analysis Code, mini Standard for Exchange of Earthquake Data (miniSEED), and SEED. To tune parameters more conveniently, we build a function to generate a database that stores power integrals in different time and frequency segments. All calculation functions possess a high-level parallel architecture, thoroughly capitalizing on available computational resources. We output and store the results of each function for continuous operation in the event of an unexpected interruption. The deployment of DynTriPy to data centers for real-time monitoring and investigating the sudden activation of any signal within a certain frequency scope has broad application prospects.
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  • 8
    Publication Date: 2020-10-21
    Description: The ability to calculate the seismogram of an earthquake at a local or regional scale is critical but challenging for many seismological studies because detailed knowledge about the 3D heterogeneities in the Earth’s subsurface, although essential, is often insufficient. Here, we present an application of compressed sensing technology that can help predict the seismograms of earthquakes at any position using data from past events randomly distributed in the same area in Jinggu County, Yunnan, China. This first data-driven approach for calculating seismograms generates a large dataset in 3D with a volume encompassing an active fault zone. The input number of earthquakes comprises only 1.27% of the total output events. We use the output data to create a database intended to find the best-matching waveform of a new event by applying an earthquake search engine, which instantly reveals the hypocenter and focal-mechanism solution.
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  • 9
    Publication Date: 2020-10-21
    Description: The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. In geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.
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  • 10
    Publication Date: 2020-10-21
    Description: Rapid response to destructive tsunami and seismic events requires rapid determination of the earthquake magnitude. We propose a new method that employs peak ground velocities (PGVs) derived from Global Navigation Satellite System (GNSS) data to estimate earthquake magnitudes. With a total of 1434 records from 22 events as the constraints, we perform the regression and obtain a PGV scaling law for magnitude determination. The advantage of the new method is that the PGVs are extracted from the GNSS velocity waveforms, which can be easily computed using broadcast GNSS ephemeris. In contrast, the peak ground displacement (PGD) depends on a sophisticated high-precision GNSS-processing subject to external correction data, realization of which cannot be kept robust constantly, especially in real time. The results show that the PGV magnitudes agree with reported moment magnitudes with mean absolute deviation of 0.26 magnitude units for the 22 events and also agree well with the PGD magnitude. We further demonstrate that GNSS-derived PGV and the modified Mercalli intensity values can be consistent with their counterparts from the U.S. Geological Survey ShakeMap products and therefore the GNSS-derived PGVs have the potential to be included in the ShakeMap as a complementary constraint, especially in areas with sparse seismic station coverage for large earthquake.
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