Bulletin of the Seismological Society of America最新文献

{"title":"Crustal Imaging with Noisy Teleseismic Receiver Functions Using Sparse Radon Transforms","authors":"Ziqi Zhang, Tolulope Olugboji","doi":"10.1785/0120230254","DOIUrl":"https://doi.org/10.1785/0120230254","url":null,"abstract":"\u0000 The receiver function (RF) is a widely used crustal imaging technique. In principle, it assumes relatively noise-free traces that can be used to target receiver-side structures following source deconvolution. In practice, however, mode conversions and reflections may be severely degraded by noisy conditions, hampering robust estimation of crustal parameters. In this study, we use a sparsity-promoting Radon transform to decompose the observed RF traces into their wavefield contributions, that is, direct conversions, multiples, and incoherent noise. By applying a crustal mask on the Radon-transformed RF, we obtain noise-free RF traces with only Moho conversions and reflections. We demonstrate, using a synthetic experiment and a real-data example from the Sierra Nevada, that our approach can effectively denoise the RFs and extract the underlying Moho signals. This greatly improves the robustness of crustal structure recovery as exemplified by subsequent H−κ stacking. We further demonstrate, using a station sitting on loose sediments in the Upper Mississippi embayment, that a combination of our approach and frequency-domain filtering can significantly improve crustal imaging in reverberant settings. In the presence of complex crustal structures, for example, dipping Moho, intracrustal layers, and crustal anisotropy, we recommend caution when applying our proposed approach due to the difficulty of interpreting a possibly more complicated Radon image. We expect that our technique will enable high-resolution crustal imaging and inspire more applications of Radon transforms in seismic signal processing.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"45 13","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 2023 New Zealand Ground-Motion Database","authors":"Jesse A. Hutchinson, Chuanbin Zhu, Brendon A. Bradley, Robin L. Lee, L. Wotherspoon, Michael Dupuis, Claudio Schill, J. Motha, E. Manea, Anna E. Kaiser","doi":"10.1785/0120230184","DOIUrl":"https://doi.org/10.1785/0120230184","url":null,"abstract":"\u0000 This article summarizes the development of the 2023 New Zealand ground-motion database (NZGMDB). A preceding version was formally used as the central ground-motion database in the ground-motion characterization modeling for the 2022 New Zealand (NZ) National Seismic Hazard Model (NSHM) revision. The database contains ground motions for events with a moment magnitude greater than ∼3.0 from the years 2000 to the end of 2022. Several challenges associated with NZ earthquake source metadata are explained, including determination of earthquake location, magnitude, tectonic classification, and finite-fault geometry, among others. The site table leverages the site database developed as a part of the 2022 NZ NSHM revision, and several definitions of source-to-site distance are computed for the propagation path table. The ground-motion quality classification was initially assessed using a neural network. Subsequent waveform quality verification was conducted and additional quality criteria were enforced to ensure a sufficiently high-quality database. Standard processing techniques were applied to the ground motions before intensity measure (IM) calculation. IMs in the database include peak ground acceleration, 5%-damped pseudoacceleration response spectra, smoothed Fourier amplitude spectra, and other cumulative and duration-related metrics. The NZGMDB is publicly available and routinely updated as new and higher quality data become available.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"10 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid Estimation of Single-Station Earthquake Magnitudes with Machine Learning on a Global Scale","authors":"S. Dybing, W. Yeck, Hank M. Cole, Diego Melgar","doi":"10.1785/0120230171","DOIUrl":"https://doi.org/10.1785/0120230171","url":null,"abstract":"\u0000 The foundation of earthquake monitoring is the ability to rapidly detect, locate, and estimate the size of seismic sources. Earthquake magnitudes are particularly difficult to rapidly characterize because magnitude types are only applicable to specific magnitude ranges, and location errors propagate to substantial magnitude errors. We developed a method for rapid estimation of single-station earthquake magnitudes using raw three-component P waveforms observed at local to teleseismic distances, independent of prior size or location information. We used the MagNet regression model architecture (Mousavi and Beroza, 2020b), which combines convolutional and recurrent neural networks. We trained our model using ∼2.4 million P-phase arrivals labeled by the authoritative magnitude assigned by the U.S. Geological Survey. We tested input data parameters (e.g., window length) that could affect the performance of our model in near-real-time monitoring applications. At the longest waveform window length of 114 s, our model (Artificial Intelligence Magnitude [AIMag]) is accurate (median estimated magnitude within ±0.5 magnitude units from catalog magnitude) between M 2.3 and 7.6. However, magnitudes above M ∼7 are more underestimated as true magnitude increases. As the windows are shortened down to 1 s, the point at which higher magnitudes begin to be underestimated moves toward lower magnitudes, and the degree of underestimation increases. The over and underestimation of magnitudes for the smallest and largest earthquakes, respectively, are potentially related to the limited number of events in these ranges within the training data, as well as magnitude saturation effects related to not capturing the full source time function of large earthquakes. Importantly, AIMag can determine earthquake magnitudes with individual stations’ waveforms without instrument response correction or knowledge of an earthquake’s source-station distance. This work may enable monitoring agencies to more rapidly recognize large, potentially tsunamigenic global earthquakes from few stations, allowing for faster event processing and reporting. This is critical for timely warnings for seismic-related hazards.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"80 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Fault-System Inversion Approach: Methods and Application to NSHM23","authors":"K. Milner, E. Field","doi":"10.1785/0120230122","DOIUrl":"https://doi.org/10.1785/0120230122","url":null,"abstract":"\u0000 We present updated inversion-based fault-system solutions for the 2023 update to the National Seismic Hazard Model (NSHM23), standardizing earthquake rate model calculations on crustal faults across the western United States. We build upon the inversion methodology used in the Third Uniform California Earthquake Rupture Forecast (UCERF3) to solve for time-independent rates of earthquakes in an interconnected fault system. The updated model explicitly maps out a wide range of fault recurrence and segmentation behavior (epistemic uncertainty), more completely exploring the solution space of viable models beyond those of UCERF3. We also improve the simulated annealing implementation, greatly increasing computational efficiency (and thus inversion convergence), and introduce an adaptive constraint weight calculation algorithm that helps to mediate between competing constraints. Hazard calculations show that ingredient changes (especially fault and deformation models) are the primary driver of hazard changes between NSHM23 and UCERF3. Updates to the inversion methodology are also consequential near faults in which the slip rate in UCERF3 was poorly fit or was satisfied primarily using large multifault ruptures that are now restricted by explicit b-value and segmentation constraints.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"78 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138945524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The USGS 2023 Conterminous U.S. Time-Independent Earthquake Rupture Forecast","authors":"E. Field, K. Milner, A. Hatem, P. Powers, Fred F. Pollitz, A. Llenos, Yuehua Zeng, Kaj M. Johnson, Bruce E. Shaw, D. McPhillips, Jessica A. Thompson Jobe, A. Shumway, Andrew J. Michael, Zheng-Kang Shen, Eileen L. Evans, Elizabeth H. Hearn, C. Mueller, Arthur D. Frankel, Mark D. Petersen, C. DuRoss, Richard W. Briggs, M. Page, J. Rubinstein, Julie A. Herrick","doi":"10.1785/0120230120","DOIUrl":"https://doi.org/10.1785/0120230120","url":null,"abstract":"\u0000 We present the 2023 U.S. Geological Survey time-independent earthquake rupture forecast for the conterminous United States, which gives authoritative estimates of the magnitude, location, and time-averaged frequency of potentially damaging earthquakes throughout the region. In addition to updating virtually all model components, a major focus has been to provide a better representation of epistemic uncertainties. For example, we have improved the representation of multifault ruptures, both in terms of allowing more and less fault connectivity than in the previous models, and in sweeping over a broader range of viable models. An unprecedented level of diagnostic information has been provided for assessing the model, and the development was overseen by a 19-member participatory review panel. Although we believe the new model embodies significant improvements and represents the best available science, we also discuss potential model limitations, including the applicability of logic tree branch weights with respect different types of hazard and risk metrics. Future improvements are also discussed, with deformation model enhancements being particularly worthy of pursuit, as well as better representation of sampling errors in the gridded seismicity components. We also plan to add time-dependent components, and assess implications with a wider range of hazard and risk metrics.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"10 14","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Panel Review of the USGS 2023 Conterminous U.S. Time-Independent Earthquake Rupture Forecast","authors":"Thomas H. Jordan, Norman Abrahamson, John G. Anderson, Glenn Biasi, Ken Campbell, Tim Dawson, Heather DeShon, Matthew C. Gerstenberger, Nick Gregor, Keith Kelson, Yajie Lee, Nicolas Luco, W. Marzocchi, B. Rowshandel, David Schwartz, Nilesh Shome, Gabriel Toro, Ray Weldon, Ivan Wong","doi":"10.1785/0120230140","DOIUrl":"https://doi.org/10.1785/0120230140","url":null,"abstract":"\u0000 This report documents the assessment by the U.S. Geological Survey (USGS) Earthquake Rupture Forecast (ERF) Review Panel of the draft ERF for the conterminous United States (CONUS-ERF23) proposed for the 2023 update of the National Seismic Hazard Model (NSHM23). Panel members participated with the ERF Development Team in several verification and validation exercises, including spot checks of the hazard estimates at key localities. The ERF23 forecast is substantially different from its predecessor, yielding relative differences in hazard that exceed ±50% in some low-hazard areas. These stem primarily from the new model ingredients—new faults, revised deformation rates, and updated seismicity catalogs—rather than from changes in the modeling methodology. The panel found that the main hazard changes are scientifically justified at the long return periods (≥475 yr) for which NSHM23 is applicable. Based on its evaluation of the model, the panel offered six actionable recommendations for improvements to the draft ERF23 for the western United States and two for the Cascadia subduction zone. All eight recommendations were adopted by the USGS for the revised ERF, as documented by Field et al. (2023). The panel concluded that CONUS-ERF23 represents a significant scientific advance over ERF18 and should be incorporated, after suitable revision, into NSHM23. The panel also considered changes to the CONUS-ERF that cannot be feasibly implemented in NSHM23 but could lead to future improvements. Among these aspirational recommendations, the panel prioritized the development of time-dependent extensions of ERF23 that include models of seismic renewal and clustering. The panel endorsed USGS efforts to extend the NSHM to a national earthquake forecasting enterprise capable of continually updating and disseminating authoritative information about future earthquake occurrence through a well-designed hazard-risk interface. Operational earthquake forecasting will place new and heavy demands on USGS cyberinfrastructure, requiring a more integrated approach to software development and workflow management.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"28 25","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achieving a Comprehensive Microseismicity Catalog through a Deep-Learning-Based Workflow: Applications in the Central Ecuadorian Subduction Zone","authors":"Alexander Wickham-Piotrowski, Yvonne Font, Marc Regnier, Bertrand Delouis, O. Lengliné, Monica Segovia, Q. Bletery","doi":"10.1785/0120230128","DOIUrl":"https://doi.org/10.1785/0120230128","url":null,"abstract":"\u0000 Although seismological networks have densified along the Ecuadorian active margin since 2010, visual phase reading, ensuring high arrival times quality, is more and more time-consuming and becomes impossible to handle for the very large amount of recorded seismic traces, even when preprocessed with a detector. In this article, we calibrate a deep-learning-based automatized workflow to acquire accurate phase arrival times and build a reliable microseismicity catalog in the central Ecuadorian forearc. We reprocessed the dataset acquired through the OSISEC local onshore–offshore seismic network that was already used by Segovia et al. (2018) to produce a reference seismic database. We assess the precision of phase pickers EQTransformer and PhaseNet with respect to manual arrivals and evaluate the accuracy of hypocentral solutions located with NonLinLoc. Both the phase pickers read arrival times with a mean error for P waves lower than 0.05 s. They produce 2.7 additional S-labeled picks per event compared to the bulletins of references. Both detect a significant number of waves not related to seismicity. We select the PhaseNet workflow because of its ability to retrieve a higher number of reference picks with greater accuracy. The derived hypocentral solutions are also closer to the manual locations. We develop a procedure to automatically determine thresholds for location attributes to cull a reliable microseismicity catalog. We show that poorly controlled detection combined with effective cleaning of the catalog is a better strategy than highly controlled detection to produce comprehensive microseismicity catalogs. Application of this technique to two seismic networks in Ecuador produces a noise-free image of seismicity and retrieves up to twice as many microearthquakes than reference studies.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"32 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel VS30 Prediction Strategy Taking Fluid Saturation into Account and a New VS30 Model of Türkiye","authors":"Hakan Bora Okay, A. A. Özacar","doi":"10.1785/0120230032","DOIUrl":"https://doi.org/10.1785/0120230032","url":null,"abstract":"\u0000 The averaged shear-wave velocity of the top 30 m (VS30) is widely used in earthquake engineering as a proxy to represent site responses. However, the spatial availability of measured VS30 is rather limited, and, so far, a region-specific VS30 model that would aid prediction of strong ground motions is not yet developed for Türkiye. In this study, a new strategy for predicting VS30 is developed using data from Türkiye and California. At first, VS30 measurements are classified into four sedimentary classes according to their ages (Quaternary–Pliocene, Miocene, Paleogene, and Pre-Paleogene) and three nonsedimentary classes (Intrusive, Extrusive, and Metamorphic). Observations from Quaternary–Pliocene deposits are most abundant and characterized by large data scatter, thus further divided into two major landform groups. Because the reduction of VS with saturation is pronounced in soils due to capillary forces, Quaternary–Pliocene deposits are also differentiated as wet if the water table depth is less than 30 m and dry otherwise. In California, available groundwater measurements are utilized while flat areas with elevation differences less than 30 m from water bodies (sea, lake, and major rivers) are mapped out as wet zones throughout Türkiye. After the elimination of outliers, slope and elevation-based VS30 prediction equations are developed separately for subclasses of Quaternary–Pliocene, Miocene, and Paleogene-aged sedimentary units using multivariable linear regression, whereas VS30 values of Pre-Paleogene sedimentary and nonsedimentary units are fixed to the mean of each subclass. Resultant model misfits and comparisons with measurements from the microzonation study conducted across İstanbul clearly indicate that our proposed VS30 prediction strategy is performing better than the competing models tested, especially in the youngest sedimentary units, and thus provides a new, accurate VS30 model of Türkiye.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"60 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138952350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subduction Interface Earthquake Rise-Time Scaling Relations","authors":"Diego R. Cárdenas, Matthew Miller, G. Montalva","doi":"10.1785/0120230129","DOIUrl":"https://doi.org/10.1785/0120230129","url":null,"abstract":"\u0000 The slip duration in a fault plane, also known as the rise time (Tr), is determined in finite-fault rupture models (FFRMs) through the analysis of seismic source inversions using strong ground-motion (SGM) records and teleseismic data. For subduction interface earthquakes (megathrust), models exist that provide estimates for Tr values. The finite-source rupture model database and National Earthquake Information Center databases include FFRMs that allow for the extension of source-scaling relations. Currently, Tr versus seismic moment (M0) scaling relations specifically derived for large megathrust earthquakes in the near-source region are scarce. The relationship between stress drop and M0 is not straightforward; therefore, the logarithmic distribution of stress drop among earthquakes of different magnitudes (Mw) appears to be constant or self-similar. This self-similarity refers to a symmetry of the time-dependent fields, which remain unchanged under certain scale transformations in space and time characterized by similarity exponents and a function of the scaled variable, called the scaling function. In this study, Tr scaling has been conducted using 45 FFRMs derived from large megathrust earthquakes (Mw≥7.3) obtained from the previously mentioned databases. The scaling relation derived from the FFRMs based on SGM records closely approximates log(Tr)=const+1/3log(M0), which agrees with the self-similarity assumption for earthquake ruptures. On the other hand, the scaling relation obtained from the teleseismic dataset exhibits a smaller slope, indicating that the teleseismic data may overestimate source time characteristics compared with SGM data from seismic stations located close to the source.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":" 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138962951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correct Off-Site Determination of Seismic Sensor Orientation from Combined Analyses of Earthquake and Microseism Records","authors":"Seongjun Park, Tae-Kyung Hong","doi":"10.1785/0120230150","DOIUrl":"https://doi.org/10.1785/0120230150","url":null,"abstract":"\u0000 Correct sensor orientation is vital for seismological analysis. However, seismic sensors including both borehole and surface seismometers are often installed in incorrect orientations. Individual methods proposed for sensor-orientation determination suffer from their own limitations and uncertainty, leaving the estimates in question before on-site verification. We introduce a method to combine a set of seismic phase analyses, yielding accurate sensor-orientation estimates. The method determines the sensor orientations by weighted-averaging independent estimates from three individual sensor-orientation analyses that are based on earthquake-origin P waves, earthquake-origin Rayleigh waves, and microseism-origin Rayleigh waves. The earthquake-origin seismic phase analyses may suffer from seismic anisotropy along ray paths even with accurate source-location information. On the other hand, the microseism-origin Rayleigh-wave analysis is hardly affected by seismic anisotropy along ray paths, being applicable to any seismic station with a couple of hour-long records. The three analyses complement each other, which enables us to determine representative sensor orientations correctly. We apply the proposed method to densely deployed 377 seismometers in South Korea, examining the sensor orientations. The representative sensor orientations are determined stably with standard errors less than 1°, supporting the accuracy of results. Borehole seismometers are poorly oriented relative to surface seismometers. The proposed method is useful for instant examination of sensor orientations of seismometers in remote regions and borehole seismometers in which physical accessibility is highly limited.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":" November","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}