ALBERT

Description: 〈span〉〈div〉ABSTRACT〈/div〉The task of downloading comprehensive datasets of event‐based seismic waveforms has been made easier through the development of standardized webservices but is still highly nontrivial because the likelihood of temporary network failures or subtle data errors naturally increases when the amount of requested data is in the order of millions of relatively short segments. This is even more challenging because the typical workflow is not restricted to a single massive download but consists of fetching all possible available input data (e.g., with several repeated download executions) for a processing stage producing any desired user‐defined output. Here, we present stream2segment, a highly customizable Python 2+3 package helping the user in the entire workflow of downloading, inspecting, and processing event‐based seismic data by means of a relational database management system as archiving storage, which has clear performance and usability advantages, and an integrated processing subroutine requiring a configuration file and a single Python function to produce user‐defined output. Stream2segment can also produce diagnostic maps or user‐defined plots, which, unlike existing tools, do not require external software dependencies and are not static images but instead are interactive browser‐based applications ideally suited for data inspection or annotation tasks and subsequent training of classifiers in foreseen supervised machine‐learning applications.Stream2segment has already been used as a data quality tool for datasets within the European Integrated Data Archive and to create a weak‐motion database (in the form of a so‐called flat file) for the stable continental region of Europe in the context of the European Ground Shaking Intensity Model service, in turn an important building block for seismic hazard studies.〈/span〉

Description: 〈span〉〈div〉Abstract〈/div〉Triplicated 〈span〉P〈/span〉 waveforms related to the 410‐km discontinuity from five intermediate‐depth earthquakes in the central Philippines are clearly recorded by the Chinese Digital Seismic Network, but some branches of the 〈span〉S〈/span〉‐wave triplications are obscure. Matching the observed 〈span〉P〈/span〉‐wave triplications with synthetics through a grid‐search technique, we obtain the best‐fit 1D 〈span〉P〈/span〉‐wave velocity model near the 410‐km discontinuity beneath the northeastern South China Sea. In such a model, a low‐velocity layer (LVL) is found to reside atop the mantle transition zone, and it is characterized by a thickness of 92.5±11.5  km and a 〈span〉P〈/span〉‐wave velocity decrement of 1.5%±0.1% compared with the IASP91 model. The relatively thick and weak LVL is possibly a response of a small amount of remnant hydrous partial melts after plume‐like upwelling.〈/span〉

Description: 〈span〉〈div〉Abstract〈/div〉Seismology has continuously recorded ground‐motion spanning up to decades. Blind, uninformed search for similar‐signal waveforms within this continuous data can detect small earthquakes missing from earthquake catalogs, yet doing so with naive approaches is computationally infeasible. We present results from an improved version of the Fingerprint And Similarity Thresholding (FAST) algorithm, an unsupervised data‐mining approach to earthquake detection, now available as open‐source software. We use FAST to search for small earthquakes in 6–11 yr of continuous data from 27 channels over an 11‐station local seismic network near the Diablo Canyon nuclear power plant in central California. FAST detected 4554 earthquakes in this data set, with a 7.5% false detection rate: 4134 of the detected events were previously cataloged earthquakes located across California, and 420 were new local earthquake detections with magnitudes −0.3≤ML≤2.4, of which 224 events were located near the seismic network. Although seismicity rates are low, this study confirms that nearby faults are active. This example shows how seismology can leverage recent advances in data‐mining algorithms, along with improved computing power, to extract useful additional earthquake information from long‐duration continuous data sets.〈/span〉

Description: 〈span〉〈div〉Abstract〈/div〉The aim of obtaining a single scale for earthquake magnitudes has led many studies in the past to either develop relationships among various existing scales or develop an altogether new scale to represent a wide range of magnitudes on a single scale. Although a reliable and standardized estimation of earthquake size is a basic requirement for all tectonophysical and engineering applications, different magnitude scales estimate different values for the same earthquake, thereby making such studies inadequate. The moment magnitude (Mw) scale has been referred to by various researchers as the best scale, one that matches well with the observed surface‐wave magnitudes with Ms≥7.5 at a global level. The formulation and validation of the Mw scale were carried out considering the southern California region for lower and intermediate earthquakes.In this study, an endeavor has been made to extend the moment magnitude scale to include lower and intermediate magnitudes in a global context emphasizing the use of body waves, particularly 〈span〉P〈/span〉 waves, in which data are abundant. We first investigate the degree of closeness of Mw values with other observed magnitudes (e.g., Ms and mb) for smaller and intermediate magnitude ranges considering global International Seismological Centre (ISC) and Global Centroid Moment Tensor (CMT) databases. To improve upon the consistency of the Mw scale for a wider range, a uniform generalized seismic moment magnitude scale Mwg=logM0/1.36−12.68, for magnitudes≥4.5, has been developed, considering 25,708 global earthquake events having mb and M0 values from ISC and Global CMT databases, respectively, during the period 1976–2006. The Mwg scale is also valid for 3.5≤mb≤7.0 because the relations between seismic moment and the magnitudes mb and Mwg are same.The greater accuracy of the Mwg scale over the Mw scale at different magnitudes (i.e., mb or Ms) is found to be statistically significant in the range including smaller and intermediate events. The similarity of the Mwg scale is also tested on 394 global seismic radiated energy values collected from 〈a href="https://pubs.geoscienceworld.org/bssa#rf6"〉Choy and Boatwright (1995)〈/a〉. It is observed that 76% of estimated radiated energy values obtained through the Mwg scale show closer agreement (than with Mw) to the observed radiated energy values. Mwg is computed from low‐ and high‐frequency spectra, and because it is consistent for small, intermediate, and large earthquake events, it will play a useful role as an earthquake magnitude estimator for all earthquake related studies.〈/span〉

Description: 〈span〉〈div〉Abstract〈/div〉Despite the theory for both Rayleigh and Love waves being well accepted and the theoretical predictions accurately matching observations, the direct observation of their quantifiable decay with depth has never been measured in the Earth’s crust. In this work, we present observations of the quantifiable decay with depth of surface‐wave eigenfunctions. This is done by making direct observations of both Rayleigh‐wave and Love‐wave eigenfunction amplitudes over a range of depths using data collected at the 3D Homestake array for a suite of nearby mine blasts. Observations of amplitudes over a range of frequencies from 0.4 to 1.2 Hz are consistent with theoretical eigenfunction predictions. They show a clear exponential decay of amplitudes with increasing depth and a reversal in sign of the radial‐component Rayleigh‐wave eigenfunction at large depths, as predicted for fundamental‐mode Rayleigh waves. Minor discrepancies between the observed eigenfunctions and those predicted using estimates of the local velocity structure suggest that the observed eigenfunctions could be used to improve the velocity model. Our results confirm that both Rayleigh and Love waves have the depth dependence that they have long been assumed to have. This is an important direct validation of a classic theoretical result in geophysics and provides new observational evidence that classical seismological surface‐wave theory can be used to accurately infer properties of Earth structure and earthquake sources.〈/span〉

Description: 〈span〉〈div〉ABSTRACT〈/div〉On 30 November 2018, three felt earthquakes occurred in the Septimus region of northeast British Columbia in an area where hydraulic fracturing was in progress. The proximity of oil and gas activities to populated areas and to critical infrastructure including major dams raises significant concern regarding the seismic hazard posed by moderate induced events and motivates study of their ground motions. Here, we analyze the ground‐motion amplitudes from these events recorded between 3 and 400 km. We use three‐component waveforms from 45 seismometer and accelerometer sensors to analyze the observed ground motions. The moment magnitude (Mw) of the first event is estimated as 4.6 using the vertical pseudoresponse spectral acceleration (PSA) based on the relations provided by 〈a href="https://pubs.geoscienceworld.org/srl#rf45"〉Novakovic 〈span〉et al.〈/span〉 (2018)〈/a〉. The Mw for the two smaller earthquakes are 3.5 and 4.0. The intensity of shaking from the Mw 4.6 and 4.0 events generally exceeded modified Mercalli intensity (MMI) VI at distances 〈6  km. The maximum duration above the MMI VI threshold at the closest station (3.5 km distance) from the mainshock is 1.6 s. The observed ground motions agree with the ground‐motion prediction equation (GMPE) of 〈a href="https://pubs.geoscienceworld.org/srl#rf45"〉Novakovic 〈span〉et al.〈/span〉 (2018)〈/a〉 for induced events in Oklahoma, with attenuation modified to match that for the study region, assuming typical regional site amplification. The inferred value of stress drop for the mainshock and the largest aftershock is approximately 50 bars based on the agreement of observed PSA values with the 〈a href="https://pubs.geoscienceworld.org/srl#rf45"〉Novakovic 〈span〉et al.〈/span〉 (2018)〈/a〉 GMPE.〈/span〉

Description: 〈span〉〈div〉ABSTRACT〈/div〉In November 2014, a temporary land and marine seismic network was deployed to monitor the drilling of an exploratory well in the Canary Channel (eastern Canary Islands). This region is characterized by low‐seismic activity; however, because of the increased awareness of the potential seismic hazard caused by hydrocarbon exploitation activities, the drilling operations were monitored with an unprecedented level of detail for an activity of this kind. According to the reported earthquakes, there was not a measurable increase in seismicity in the vicinity of the well. Overall seismic activity was low, which is consistent with the historical seismicity records. Harmonic tremor, explained here as resonances of the instrument‐seafloor system generated by bottom water currents in the area, was commonly detected on the ocean‐bottom seismometer (OBS) recordings. The marine network data also revealed dozens of nonseismic short‐duration signals per day that appear similar to other events on OBS recordings throughout the world. We suggest that they may be caused by direct perturbations on the OBS, mostly induced by ocean currents in the Canary Channel.〈/span〉

Description: 〈span〉〈div〉ABSTRACT〈/div〉The tsunami that followed the 1995 Mw 7.2 Nuweiba earthquake along the Dead Sea Transform in the Gulf of Elat–Aqaba (GOE) surprised the local population, who were unconcerned by seismogenic sea waves happening in a closed gulf, far away from the open ocean. Eyewitness reports, field observations, and a mareogram recorded near Elat demonstrated conclusively that tsunami hazard in the GOE deserves focused attention. Here we take up the challenge, adopting the GeoClaw package and investigating which of the available Nuweiba earthquake models are capable of better replicating the actual findings. In general, the simulated tsunamis that are based on both the seismological and Interferometric Synthetic Aperture Radar (InSAR) Nuweiba earthquake models are in line with the eyewitness descriptions of wave height, slight inundation, and limited damage. In addition, the simulations show a reasonable correlation with the amplitude and wave period derived from the analog mareogram, as expected from a tsunamigenic, mostly strike‐slip component earthquake. The InSAR inverse‐based modeling of the coseismic deformation, however, appears closer to the measured parameters of the recorded mareogram. The exception of 3–4 m high waves reported in the Nuweiba port may be the result of local perturbations in the harbor or the effect of a local tsunamigenic submarine landslide. The four countries of Israel, Jordan, Saudi Arabia, and Egypt that encircle the GOE seek to expand intensively their marine infrastructure, tourism, and population. The present study aims to warn the stakeholders around the GOE about the inevitable tsunamis.〈/span〉

Description: 〈span〉〈div〉ABSTRACT〈/div〉Seismic waves that are recorded by near‐surface sensors are usually disturbed by strong noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon can be characterized as a low signal‐to‐noise ratio (SNR). The low SNR of the seismic data may lower the quality of many subsequent seismological analyses, such as inversion and imaging. Thus, the removal of unwanted seismic noise has significant importance. In this article, we intend to improve the SNR of many seismological datasets by developing new denoising framework that is based on an unsupervised machine‐learning technique. We leverage the unsupervised learning philosophy of the autoencoding method to adaptively learn the seismic signals from the noisy observations. This could potentially enable us to better represent the true seismic‐wave components. To mitigate the influence of the seismic noise on the learned features and suppress the trivial components associated with low‐amplitude neurons in the hidden layer, we introduce a sparsity constraint to the autoencoder neural network. The sparse autoencoder method introduced in this article is effective in attenuating the seismic noise. More importantly, it is capable of preserving subtle features of the data, while removing the spatially incoherent random noise. We apply the proposed denoising framework to a reflection seismic image, depth‐domain receiver function gather, and an earthquake stack dataset. The purpose of this study is to demonstrate the framework’s potential in real‐world applications.〈/span〉

Description: 〈span〉〈div〉ABSTRACT〈/div〉Seismic station data quality is commonly defined by metrics such as data completeness or background seismic noise levels in specific frequency bands. However, for temporary networks such as aftershock deployments or induced seismicity monitoring, the most critical metric is often how well the station performs when recording events of interest. A timely measure of station performance can be used for real‐time network maintenance and to help make decisions about which stations may need to be moved or are redundant. We develop new event‐based methods to quickly assess station and network performance, including estimating network magnitude of completeness, determining station signal‐to‐noise ratios as a function of earthquake magnitude, and computing relative station amplitudes. At times, a complete catalog of local seismic events may not exist such as in an aftershock deployment in which hundreds to thousands of small earthquakes may be happening and catalog generation efforts cannot keep up. To overcome this, we use an envelope of the average energy recorded by the network to identify events of interest. We find the log amplitude of events identified using this technique scales linearly with local earthquake magnitudes. We examine two U.S. Geological Survey aftershock networks in Oklahoma to demonstrate this approach can be used to identify poorly performing stations and determine network detection thresholds as early as one day following the deployment of a temporary network.〈/span〉