Andrew Nicol, Vasiliki Mouslopoulou, Andy Howell, Russ Van Dissen
Understanding temporal patterns of surface‐rupturing earthquakes is critical for seismic hazard assessment. We examine these patterns by collating elapsed time and recurrence interval data from paleoseismic and historical records in Aotearoa New Zealand. We find that the elapsed time since the last earthquake is less than the mean recurrence interval for the majority (∼70%–80%) of the >50 faults sampled. Calculated mean recurrence intervals using slip per event and slip rate for these faults do not indicate systematic bias of the paleoseismic recurrence‐interval dataset due to missing earthquakes. Stochastic modeling of elapsed times indicates that the rarity of elapsed times greater than the mean recurrence interval is consistent with positively skewed Weibull and lognormal recurrence‐interval models. Regardless of the precise explanation for the short elapsed times, the majority of faults sampled are unlikely to be chronically late in their seismic cycles.
{"title":"Why Do Large Earthquakes Appear to be Rarely “Overdue” for Aotearoa New Zealand Faults?","authors":"Andrew Nicol, Vasiliki Mouslopoulou, Andy Howell, Russ Van Dissen","doi":"10.1785/0220230204","DOIUrl":"https://doi.org/10.1785/0220230204","url":null,"abstract":"Understanding temporal patterns of surface‐rupturing earthquakes is critical for seismic hazard assessment. We examine these patterns by collating elapsed time and recurrence interval data from paleoseismic and historical records in Aotearoa New Zealand. We find that the elapsed time since the last earthquake is less than the mean recurrence interval for the majority (∼70%–80%) of the >50 faults sampled. Calculated mean recurrence intervals using slip per event and slip rate for these faults do not indicate systematic bias of the paleoseismic recurrence‐interval dataset due to missing earthquakes. Stochastic modeling of elapsed times indicates that the rarity of elapsed times greater than the mean recurrence interval is consistent with positively skewed Weibull and lognormal recurrence‐interval models. Regardless of the precise explanation for the short elapsed times, the majority of faults sampled are unlikely to be chronically late in their seismic cycles.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139582162","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}
A. Brotzer, H. Igel, É. Stutzmann, J. Montagner, F. Bernauer, J. Wassermann, R. Widmer-Schnidrig, Chin-Jen Lin, Sergey Kiselev, Frank Vernon, K. U. Schreiber
The development of high-sensitive ground-motion instrumentation for Earth and planetary exploration is governed by so-called low-noise models, which characterize the minimum level of physical ground motions, observed across a very broad frequency range (0.1 mHz–100 Hz). For decades, broadband instruments for seismic translational ground-motion sensing allowed for observations down to the Earth’s low-noise model. Knowing the lowermost noise level distribution across frequencies enabled not only to infer characteristics of Earth such as the ocean microseismic noise (microseisms) and seismic hum, but also to develop highly successful ambient seismic noise analysis techniques in seismology. Such a low-noise model currently does not exist for rotational ground motions. In the absence of a substantial observational database, we propose a preliminary rotational low-noise model (RLNM) for transverse rotations based on two main wavefield assumptions: the frequency range under investigation is dominated by surface-wave energy, and the employed phase velocity models for surface waves are representative. These assumptions hold, in particular, for a period range of about 2–50 s and lose validity towards long periods when constituents produced by atmospheric pressure dominate. Because noise levels of vertical and horizontal accelerations differ, we expect also different noise levels for transverse and vertical rotations. However, at this moment, we propose a common model for both types of rotations based on the transverse RLNM. We test our RLNM against available direct observations provided by two large-scale ring lasers (G-ring and ROMY) and array-derived rotations (Piñon Flats Observatory array, Gräfenberg array, and ROMY array). We propose this RLNM to be useful as guidance for the development of high-performance rotation instrumentation for seismic applications in a range of 2–50 s. Achieving broadband sensitivity below such a RLNM remains a challenging task, but one that has to be achieved.
{"title":"Characterizing the Background Noise Level of Rotational Ground Motions on Earth","authors":"A. Brotzer, H. Igel, É. Stutzmann, J. Montagner, F. Bernauer, J. Wassermann, R. Widmer-Schnidrig, Chin-Jen Lin, Sergey Kiselev, Frank Vernon, K. U. Schreiber","doi":"10.1785/0220230202","DOIUrl":"https://doi.org/10.1785/0220230202","url":null,"abstract":"\u0000 The development of high-sensitive ground-motion instrumentation for Earth and planetary exploration is governed by so-called low-noise models, which characterize the minimum level of physical ground motions, observed across a very broad frequency range (0.1 mHz–100 Hz). For decades, broadband instruments for seismic translational ground-motion sensing allowed for observations down to the Earth’s low-noise model. Knowing the lowermost noise level distribution across frequencies enabled not only to infer characteristics of Earth such as the ocean microseismic noise (microseisms) and seismic hum, but also to develop highly successful ambient seismic noise analysis techniques in seismology. Such a low-noise model currently does not exist for rotational ground motions. In the absence of a substantial observational database, we propose a preliminary rotational low-noise model (RLNM) for transverse rotations based on two main wavefield assumptions: the frequency range under investigation is dominated by surface-wave energy, and the employed phase velocity models for surface waves are representative. These assumptions hold, in particular, for a period range of about 2–50 s and lose validity towards long periods when constituents produced by atmospheric pressure dominate. Because noise levels of vertical and horizontal accelerations differ, we expect also different noise levels for transverse and vertical rotations. However, at this moment, we propose a common model for both types of rotations based on the transverse RLNM. We test our RLNM against available direct observations provided by two large-scale ring lasers (G-ring and ROMY) and array-derived rotations (Piñon Flats Observatory array, Gräfenberg array, and ROMY array). We propose this RLNM to be useful as guidance for the development of high-performance rotation instrumentation for seismic applications in a range of 2–50 s. Achieving broadband sensitivity below such a RLNM remains a challenging task, but one that has to be achieved.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138943967","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}
One of the major challenges in probabilistic seismic hazard analysis (PSHA) studies, particularly for risk-based decision-making, is to constrain epistemic uncertainties. Epistemic uncertainty associated with ground-motion characterization (GMC) models exerts a strong influence on the hazard estimate for a given target level of ground shaking. In the Indian context (mainly along the Himalayan arc), constraining epistemic uncertainty is a significant challenge owing to the lack of recorded data. This study investigates the epistemic uncertainty associated with ground-motion models (GMMs) considered appropriate for the Himalayan region. First, a review of GMMs considered applicable to the Himalayan region is provided. Subsequently, a graphical comparison of median models is performed, followed by residual and statistical analysis. The evaluation utilizes observations from a recently compiled strong-motion dataset across the Himalayas and Indo-Gangetic plains of northern India. The dataset comprises 519 acceleration traces from 150 events in the moment magnitude (Mw) range Mw 3–7.4, recorded at epicentral distances in the range REpi<300 km. The analysis demonstrates significant between-model variability, particularly with regard to median magnitude and distance scaling. The residual analysis also indicates a large bias and aleatory uncertainty. Moreover, some of the GMMs exhibit trends with distance and magnitude. Overall, our evaluation analysis shows that there is clearly significant aleatory and epistemic uncertainty associated with the GMC modeling owing to the paucity of recorded data. The range of epistemic uncertainty represented by the GMMs (available in the literature) is much larger than that typically captured by the (multiple) global models often used in PSHA studies across India.
{"title":"Epistemic Uncertainty in Ground-Motion Characterization in the Indian Context: Evaluation of Ground-Motion Models (GMMs) for the Himalayan Region","authors":"Shikha Sharma, U. Mannu, Sanjay Singh Bora","doi":"10.1785/0220230157","DOIUrl":"https://doi.org/10.1785/0220230157","url":null,"abstract":"\u0000 One of the major challenges in probabilistic seismic hazard analysis (PSHA) studies, particularly for risk-based decision-making, is to constrain epistemic uncertainties. Epistemic uncertainty associated with ground-motion characterization (GMC) models exerts a strong influence on the hazard estimate for a given target level of ground shaking. In the Indian context (mainly along the Himalayan arc), constraining epistemic uncertainty is a significant challenge owing to the lack of recorded data. This study investigates the epistemic uncertainty associated with ground-motion models (GMMs) considered appropriate for the Himalayan region. First, a review of GMMs considered applicable to the Himalayan region is provided. Subsequently, a graphical comparison of median models is performed, followed by residual and statistical analysis. The evaluation utilizes observations from a recently compiled strong-motion dataset across the Himalayas and Indo-Gangetic plains of northern India. The dataset comprises 519 acceleration traces from 150 events in the moment magnitude (Mw) range Mw 3–7.4, recorded at epicentral distances in the range REpi<300 km. The analysis demonstrates significant between-model variability, particularly with regard to median magnitude and distance scaling. The residual analysis also indicates a large bias and aleatory uncertainty. Moreover, some of the GMMs exhibit trends with distance and magnitude. Overall, our evaluation analysis shows that there is clearly significant aleatory and epistemic uncertainty associated with the GMC modeling owing to the paucity of recorded data. The range of epistemic uncertainty represented by the GMMs (available in the literature) is much larger than that typically captured by the (multiple) global models often used in PSHA studies across India.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954932","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}
E. Manea, Sanjay S. Bora, Jesse A. Hutchinson, Anna E. Kaiser
We present a ground-motion parameter database for earthquakes recorded between 2000 and the end of 2022 in New Zealand, which was developed within the New Zealand National Seismic Hazard Model (NZ NSHM 2022) program. It comprises all the local events with moment magnitudes in the range Mw 3.5–7.8 for crustal seismicity and Mw 4–7.8 for subduction seismicity recorded by GeoNet strong-motion network. Out of 2809 events, 1598 (∼57.1%) were classified as crustal, 432 as interface (∼15.3%), 98 as outer-rise (3.5%), 597 as inslab (∼21.3%), and the rest are undetermined. Beside the information that GeoNet provides for each event, the source metadata also comprises moment tensor solutions and finite-fault source models compiled from the literature. Various distance measures are computed for each event–station pair, including estimates of rupture distance for sufficiently large events by incorporating finite-fault source models. More than 150,000 strong ground-motion records, within 500 km rupture distance, were processed using an automated algorithm that combines traditional processing algorithms and machine learning. Several intensity measures (i.e., smoothed and down-sampled Fourier spectral amplitudes, Arias intensity, cumulative absolute velocity, and duration measures) of the processed ground motions are presented in the database. Finally, the database includes station site parameters sourced directly from the 2022 NSHM compilation of Wotherspoon et al. (2022, 2023).
{"title":"Uniformly Processed Fourier Spectra Amplitude Database for Recently Compiled New Zealand Strong Ground Motions","authors":"E. Manea, Sanjay S. Bora, Jesse A. Hutchinson, Anna E. Kaiser","doi":"10.1785/0220230228","DOIUrl":"https://doi.org/10.1785/0220230228","url":null,"abstract":"\u0000 We present a ground-motion parameter database for earthquakes recorded between 2000 and the end of 2022 in New Zealand, which was developed within the New Zealand National Seismic Hazard Model (NZ NSHM 2022) program. It comprises all the local events with moment magnitudes in the range Mw 3.5–7.8 for crustal seismicity and Mw 4–7.8 for subduction seismicity recorded by GeoNet strong-motion network. Out of 2809 events, 1598 (∼57.1%) were classified as crustal, 432 as interface (∼15.3%), 98 as outer-rise (3.5%), 597 as inslab (∼21.3%), and the rest are undetermined. Beside the information that GeoNet provides for each event, the source metadata also comprises moment tensor solutions and finite-fault source models compiled from the literature. Various distance measures are computed for each event–station pair, including estimates of rupture distance for sufficiently large events by incorporating finite-fault source models. More than 150,000 strong ground-motion records, within 500 km rupture distance, were processed using an automated algorithm that combines traditional processing algorithms and machine learning. Several intensity measures (i.e., smoothed and down-sampled Fourier spectral amplitudes, Arias intensity, cumulative absolute velocity, and duration measures) of the processed ground motions are presented in the database. Finally, the database includes station site parameters sourced directly from the 2022 NSHM compilation of Wotherspoon et al. (2022, 2023).","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138961999","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}
Juliann R. Coffey, Alex J. C. Witsil, Kenneth A. Macpherson, David Fee
Developing techniques for improving quality control (QC) schemes to catch seismic and infrasound data defects continues to be an area of active research. Selecting universal thresholds for the automation of data quality (DQ) checks is an efficient way to find QC issues, but these thresholds may not apply well to multiple stations with varying DQ characteristics. In addition, these thresholds may not catch subtle changes in DQ parameters that still indicate problems. Machine learning can be an alternative way of diagnosing QC issues. K-means clustering, an unsupervised machine learning clustering algorithm, has been effectively used in the past for geophysical pattern exploration. This study furthers k-means applications to DQ analysis through clustering on DQ metrics derived from day-long segments of nuclear explosion monitoring data. Our k-means implementation on broadband seismometer DQ metrics separately clustered mass recenters, calibrations lasting at least one hour, and days without either. Applying this technique to infrasound DQ metrics revealed clusters related to physical issues at the stations, such as missing back volume screws and the flooding of ported pipe inlets. These are both examples of QC issues that are difficult to diagnose or detect through the thresholding of metrics or by inspecting waveforms and spectra. Our results show that k-means clustering can be a useful QC tool in exploring DQ patterns to assist analyst review of station operation and maintenance. The learned knowledge from this exploration can then inform a thresholding workflow on how to tailor to individual stations, or the k-means model could classify data directly.
{"title":"Unsupervised Machine Learning Clustering of Seismic and Infrasound Data Quality Metrics","authors":"Juliann R. Coffey, Alex J. C. Witsil, Kenneth A. Macpherson, David Fee","doi":"10.1785/0220230177","DOIUrl":"https://doi.org/10.1785/0220230177","url":null,"abstract":"\u0000 Developing techniques for improving quality control (QC) schemes to catch seismic and infrasound data defects continues to be an area of active research. Selecting universal thresholds for the automation of data quality (DQ) checks is an efficient way to find QC issues, but these thresholds may not apply well to multiple stations with varying DQ characteristics. In addition, these thresholds may not catch subtle changes in DQ parameters that still indicate problems. Machine learning can be an alternative way of diagnosing QC issues. K-means clustering, an unsupervised machine learning clustering algorithm, has been effectively used in the past for geophysical pattern exploration. This study furthers k-means applications to DQ analysis through clustering on DQ metrics derived from day-long segments of nuclear explosion monitoring data. Our k-means implementation on broadband seismometer DQ metrics separately clustered mass recenters, calibrations lasting at least one hour, and days without either. Applying this technique to infrasound DQ metrics revealed clusters related to physical issues at the stations, such as missing back volume screws and the flooding of ported pipe inlets. These are both examples of QC issues that are difficult to diagnose or detect through the thresholding of metrics or by inspecting waveforms and spectra. Our results show that k-means clustering can be a useful QC tool in exploring DQ patterns to assist analyst review of station operation and maintenance. The learned knowledge from this exploration can then inform a thresholding workflow on how to tailor to individual stations, or the k-means model could classify data directly.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138961407","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}
Hojjat Kaveh, Pau Batlle, M. Acosta, Pranav Kulkarni, S. J. Bourne, J. Avouac
Reservoir operations for gas extraction, fluid disposal, carbon dioxide storage, or geothermal energy production are capable of inducing seismicity. Modeling tools exist for seismicity forecasting using operational data, but the computational costs and uncertainty quantification (UQ) pose challenges. We address this issue in the context of seismicity induced by gas production from the Groningen gas field using an integrated modeling framework, which combines reservoir modeling, geomechanical modeling, and stress-based earthquake forecasting. The framework is computationally efficient thanks to a 2D finite-element reservoir model, which assumes vertical flow equilibrium, and the use of semianalytical solutions to calculate poroelastic stress changes and predict seismicity rate. The earthquake nucleation model is based on rate-and-state friction and allows for an initial strength excess so that the faults are not assumed initially critically stressed. We estimate uncertainties in the predicted number of earthquakes and magnitudes. To reduce the computational costs, we assume that the stress model is true, but our UQ algorithm is general enough that the uncertainties in reservoir and stress models could be incorporated. We explore how the selection of either a Poisson or a Gaussian likelihood influences the forecast. We also use a synthetic catalog to estimate the improved forecasting performance that would have resulted from a better seismicity detection threshold. Finally, we use tapered and nontapered Gutenberg–Richter distributions to evaluate the most probable maximum magnitude over time and account for uncertainties in its estimation. Although we did not formally account for uncertainties in the stress model, we tested several alternative stress models, and found negligible impact on the predicted temporal evolution of seismicity and forecast uncertainties. Our study shows that the proposed approach yields realistic estimates of the uncertainties of temporal seismicity and is applicable for operational forecasting or induced seismicity monitoring. It can also be used in probabilistic traffic light systems.
{"title":"Induced Seismicity Forecasting with Uncertainty Quantification: Application to the Groningen Gas Field","authors":"Hojjat Kaveh, Pau Batlle, M. Acosta, Pranav Kulkarni, S. J. Bourne, J. Avouac","doi":"10.1785/0220230179","DOIUrl":"https://doi.org/10.1785/0220230179","url":null,"abstract":"\u0000 Reservoir operations for gas extraction, fluid disposal, carbon dioxide storage, or geothermal energy production are capable of inducing seismicity. Modeling tools exist for seismicity forecasting using operational data, but the computational costs and uncertainty quantification (UQ) pose challenges. We address this issue in the context of seismicity induced by gas production from the Groningen gas field using an integrated modeling framework, which combines reservoir modeling, geomechanical modeling, and stress-based earthquake forecasting. The framework is computationally efficient thanks to a 2D finite-element reservoir model, which assumes vertical flow equilibrium, and the use of semianalytical solutions to calculate poroelastic stress changes and predict seismicity rate. The earthquake nucleation model is based on rate-and-state friction and allows for an initial strength excess so that the faults are not assumed initially critically stressed. We estimate uncertainties in the predicted number of earthquakes and magnitudes. To reduce the computational costs, we assume that the stress model is true, but our UQ algorithm is general enough that the uncertainties in reservoir and stress models could be incorporated. We explore how the selection of either a Poisson or a Gaussian likelihood influences the forecast. We also use a synthetic catalog to estimate the improved forecasting performance that would have resulted from a better seismicity detection threshold. Finally, we use tapered and nontapered Gutenberg–Richter distributions to evaluate the most probable maximum magnitude over time and account for uncertainties in its estimation. Although we did not formally account for uncertainties in the stress model, we tested several alternative stress models, and found negligible impact on the predicted temporal evolution of seismicity and forecast uncertainties. Our study shows that the proposed approach yields realistic estimates of the uncertainties of temporal seismicity and is applicable for operational forecasting or induced seismicity monitoring. It can also be used in probabilistic traffic light systems.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000127","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}
The 2023 Mw 7.8 Türkiye earthquake caused severe damage in near-fault regions. The broadband source model, which is important for predicting strong motions in near-fault regions, was estimated. First, high-frequency (3–10 Hz) source imaging was performed through isochrone backprojection using near-field strong-motion records. Four segments were set, consisting of three segments along the East Anatolian fault and one segment along the splay fault where the rupture started. The estimated rupture velocities at the four segments were 2.6–3.3 km/s. The broadband (0.2–10 Hz) source model was then estimated using the empirical Green’s function method. The locations of eight strong-motion generation areas (SMGAs) of the broadband source model were searched with reference to the large brightness area estimated by isochrone backprojection. The source parameters of the SMGAs were estimated to fit the calculated acceleration and velocity envelopes at 21 strong-motion stations to the observed ones. The locations of the SMGAs were mostly consistent with the large slip area estimated by previous studies from long-period waveforms or static data, except for one SMGA with the highest Brune’s stress drop on the splay fault. The highest stress drop caused large ground motions near the splay fault, for which the supershear rupture has been suggested by previous studies. Ground motions were reproduced except for some stations affected by the fling-steps or nonlinear site effects. Although the SMGAs were not located near the southern side of the southwestern segment in Hatay Province, the large ground motions at shorter than about 2 s were mostly simulated. Large empirical site amplification factors estimated in this study must play a role on the large ground motions. The forward rupture directivity effects, with a rupture velocity of 3.3 km/s as large as the S-wave velocity, were also responsible for the large ground motions there.
{"title":"Broadband Source Model of the 2023 Mw 7.8 Türkiye Earthquake from Strong-Motion Records by Isochrone Backprojection and Empirical Green’s Function Method","authors":"T. Satoh","doi":"10.1785/0220230268","DOIUrl":"https://doi.org/10.1785/0220230268","url":null,"abstract":"\u0000 The 2023 Mw 7.8 Türkiye earthquake caused severe damage in near-fault regions. The broadband source model, which is important for predicting strong motions in near-fault regions, was estimated. First, high-frequency (3–10 Hz) source imaging was performed through isochrone backprojection using near-field strong-motion records. Four segments were set, consisting of three segments along the East Anatolian fault and one segment along the splay fault where the rupture started. The estimated rupture velocities at the four segments were 2.6–3.3 km/s. The broadband (0.2–10 Hz) source model was then estimated using the empirical Green’s function method. The locations of eight strong-motion generation areas (SMGAs) of the broadband source model were searched with reference to the large brightness area estimated by isochrone backprojection. The source parameters of the SMGAs were estimated to fit the calculated acceleration and velocity envelopes at 21 strong-motion stations to the observed ones. The locations of the SMGAs were mostly consistent with the large slip area estimated by previous studies from long-period waveforms or static data, except for one SMGA with the highest Brune’s stress drop on the splay fault. The highest stress drop caused large ground motions near the splay fault, for which the supershear rupture has been suggested by previous studies. Ground motions were reproduced except for some stations affected by the fling-steps or nonlinear site effects. Although the SMGAs were not located near the southern side of the southwestern segment in Hatay Province, the large ground motions at shorter than about 2 s were mostly simulated. Large empirical site amplification factors estimated in this study must play a role on the large ground motions. The forward rupture directivity effects, with a rupture velocity of 3.3 km/s as large as the S-wave velocity, were also responsible for the large ground motions there.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139005232","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}
Jongwon Han, Keun Joo Seo, Seongryong Kim, Dong-Hoon Sheen, Donghun Lee, Ah-Hyun Byun
A seismicity catalog spanning 2012–2021 is proposed for the inland and near-coastal areas of the southern Korean Peninsula (SKP). Using deep learning (DL) techniques combined with conventional methods, we developed an integrated framework for compiling a comprehensive seismicity catalog. The proposed DL-based framework allowed us to process, within a week, a large volume of data (spanning 10 yr) collected from more than 300 seismic stations. To improve the framework’s performance, a DL picker (i.e., EQTransformer) was retrained using the local datasets from the SKP combined with globally obtained data. A total of 66,858 events were detected by phase association using a machine learning algorithm, and a DL-based event discrimination model classified 29,371 events as natural earthquakes. We estimate source information more precisely using newly updated parameters for locations (a 1D velocity model and station corrections related to the location process) and magnitudes (a local magnitude equation) based on data derived from the application of the DL picker. Compared with a previous catalog, the proposed catalog exhibited improved statistical completeness, detecting 21,475 additional earthquakes. With the newly detected and located earthquakes, we observed the relative low seismicity in the northern SKP, and the linear trends of earthquakes striking northeast–southwest (NE–SW) and northwest–southeast (NW–SE) with a near-right angle between them. In particular, the NE–SW trend corresponds to boundaries of major tectonic regions in the SKP that potentially indicates the development of fault structures along the boundaries. The two predominant trends slightly differ to the suggested optimal fault orientations, implying more complex processes of preexisting geological structures. This study demonstrates the effectiveness of the DL-based framework in analyzing large datasets and detecting many microearthquakes in seismically inactive regions, which will advance our understanding of seismotectonics and seismic hazards in stable continental regions.
{"title":"Research Catalog of Inland Seismicity in the Southern Korean Peninsula from 2012 to 2021 Using Deep Learning Techniques","authors":"Jongwon Han, Keun Joo Seo, Seongryong Kim, Dong-Hoon Sheen, Donghun Lee, Ah-Hyun Byun","doi":"10.1785/0220230246","DOIUrl":"https://doi.org/10.1785/0220230246","url":null,"abstract":"\u0000 A seismicity catalog spanning 2012–2021 is proposed for the inland and near-coastal areas of the southern Korean Peninsula (SKP). Using deep learning (DL) techniques combined with conventional methods, we developed an integrated framework for compiling a comprehensive seismicity catalog. The proposed DL-based framework allowed us to process, within a week, a large volume of data (spanning 10 yr) collected from more than 300 seismic stations. To improve the framework’s performance, a DL picker (i.e., EQTransformer) was retrained using the local datasets from the SKP combined with globally obtained data. A total of 66,858 events were detected by phase association using a machine learning algorithm, and a DL-based event discrimination model classified 29,371 events as natural earthquakes. We estimate source information more precisely using newly updated parameters for locations (a 1D velocity model and station corrections related to the location process) and magnitudes (a local magnitude equation) based on data derived from the application of the DL picker. Compared with a previous catalog, the proposed catalog exhibited improved statistical completeness, detecting 21,475 additional earthquakes. With the newly detected and located earthquakes, we observed the relative low seismicity in the northern SKP, and the linear trends of earthquakes striking northeast–southwest (NE–SW) and northwest–southeast (NW–SE) with a near-right angle between them. In particular, the NE–SW trend corresponds to boundaries of major tectonic regions in the SKP that potentially indicates the development of fault structures along the boundaries. The two predominant trends slightly differ to the suggested optimal fault orientations, implying more complex processes of preexisting geological structures. This study demonstrates the effectiveness of the DL-based framework in analyzing large datasets and detecting many microearthquakes in seismically inactive regions, which will advance our understanding of seismotectonics and seismic hazards in stable continental regions.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004377","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}
R. O. Salvage, David W. Eaton, Carolyn M. Furlong, Jan Dettmer, Per K. Pedersen
Based on information available at the time, several questionnaire-based schemes have been developed to provide a qualitative assessment of whether a specific earthquake (or earthquake sequence) was likely induced by anthropogenic activities or is inferred to be natural. From a pragmatic perspective, the value of this assessment is arguably the greatest in the immediate aftermath of an event (hours to days), because it could then better serve to guide regulatory response. However, necessary information is often incomplete or uncertain, and there remains a lack of scientific consensus on the most distinctive attributes of induced (vs. natural) earthquake sequences. We present a case study of the Mw 5.2 Peace River earthquake sequence (Alberta, Canada), evaluated using two published frameworks for origin interpretation. The Alberta Energy Regulator initially considered the sequence to be natural, but a study published ~4 mo later came to the opposite interpretation. Prior to this publication, we convened a panel of experts who completed questionnaires as set out by the frameworks; results using both schemes indicate that experts believe the sequence was likely induced. Based on these expert responses, we critically evaluate information that was available publicly in the weeks to months following the mainshock on 30 November 2022; reassess the relative importance of various components of the questionnaires from a parsimonious, rapid-response perspective; and consider other types of information that could be critical for near-real-time assessment of whether an event was induced or natural.
{"title":"Induced or Natural? Toward Rapid Expert Assessment, with Application to the Mw 5.2 Peace River Earthquake Sequence","authors":"R. O. Salvage, David W. Eaton, Carolyn M. Furlong, Jan Dettmer, Per K. Pedersen","doi":"10.1785/0220230289","DOIUrl":"https://doi.org/10.1785/0220230289","url":null,"abstract":"\u0000 Based on information available at the time, several questionnaire-based schemes have been developed to provide a qualitative assessment of whether a specific earthquake (or earthquake sequence) was likely induced by anthropogenic activities or is inferred to be natural. From a pragmatic perspective, the value of this assessment is arguably the greatest in the immediate aftermath of an event (hours to days), because it could then better serve to guide regulatory response. However, necessary information is often incomplete or uncertain, and there remains a lack of scientific consensus on the most distinctive attributes of induced (vs. natural) earthquake sequences. We present a case study of the Mw 5.2 Peace River earthquake sequence (Alberta, Canada), evaluated using two published frameworks for origin interpretation. The Alberta Energy Regulator initially considered the sequence to be natural, but a study published ~4 mo later came to the opposite interpretation. Prior to this publication, we convened a panel of experts who completed questionnaires as set out by the frameworks; results using both schemes indicate that experts believe the sequence was likely induced. Based on these expert responses, we critically evaluate information that was available publicly in the weeks to months following the mainshock on 30 November 2022; reassess the relative importance of various components of the questionnaires from a parsimonious, rapid-response perspective; and consider other types of information that could be critical for near-real-time assessment of whether an event was induced or natural.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004379","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}
Dynamic rupture simulations generate synthetic waveforms that account for nonlinear source and path complexity. Here, we analyze millions of spatially dense waveforms from 3D dynamic rupture simulations in a novel way to illuminate the spectral fingerprints of earthquake physics. We define a Brune-type equivalent near-field corner frequency (fc) to analyze the spatial variability of ground-motion spectra and unravel their link to source complexity. We first investigate a simple 3D strike-slip setup, including an asperity and a barrier, and illustrate basic relations between source properties and fc variations. Next, we analyze >13,000,000 synthetic near-field strong-motion waveforms generated in three high-resolution dynamic rupture simulations of real earthquakes, the 2019 Mw 7.1 Ridgecrest mainshock, the Mw 6.4 Searles Valley foreshock, and the 1992 Mw 7.3 Landers earthquake. All scenarios consider 3D fault geometries, topography, off-fault plasticity, viscoelastic attenuation, and 3D velocity structure and resolve frequencies up to 1–2 Hz. Our analysis reveals pronounced and localized patterns of elevated fc, specifically in the vertical components. We validate such fc variability with observed near-fault spectra. Using isochrone analysis, we identify the complex dynamic mechanisms that explain rays of elevated fc and cause unexpectedly impulsive, localized, vertical ground motions. Although the high vertical frequencies are also associated with path effects, rupture directivity, and coalescence of multiple rupture fronts, we show that they are dominantly caused by rake-rotated surface-breaking rupture fronts that decelerate due to fault heterogeneities or geometric complexity. Our findings highlight the potential of spatially dense ground-motion observations to further our understanding of earthquake physics directly from near-field data. Observed near-field fc variability may inform on directivity, surface rupture, and slip segmentation. Physics-based models can identify “what to look for,” for example, in the potentially vast amount of near-field large array or distributed acoustic sensing data.
{"title":"Equivalent Near-Field Corner Frequency Analysis of 3D Dynamic Rupture Simulations Reveals Dynamic Source Effects","authors":"Nico Schliwa, A. Gabriel","doi":"10.1785/0220230225","DOIUrl":"https://doi.org/10.1785/0220230225","url":null,"abstract":"\u0000 Dynamic rupture simulations generate synthetic waveforms that account for nonlinear source and path complexity. Here, we analyze millions of spatially dense waveforms from 3D dynamic rupture simulations in a novel way to illuminate the spectral fingerprints of earthquake physics. We define a Brune-type equivalent near-field corner frequency (fc) to analyze the spatial variability of ground-motion spectra and unravel their link to source complexity. We first investigate a simple 3D strike-slip setup, including an asperity and a barrier, and illustrate basic relations between source properties and fc variations. Next, we analyze >13,000,000 synthetic near-field strong-motion waveforms generated in three high-resolution dynamic rupture simulations of real earthquakes, the 2019 Mw 7.1 Ridgecrest mainshock, the Mw 6.4 Searles Valley foreshock, and the 1992 Mw 7.3 Landers earthquake. All scenarios consider 3D fault geometries, topography, off-fault plasticity, viscoelastic attenuation, and 3D velocity structure and resolve frequencies up to 1–2 Hz. Our analysis reveals pronounced and localized patterns of elevated fc, specifically in the vertical components. We validate such fc variability with observed near-fault spectra. Using isochrone analysis, we identify the complex dynamic mechanisms that explain rays of elevated fc and cause unexpectedly impulsive, localized, vertical ground motions. Although the high vertical frequencies are also associated with path effects, rupture directivity, and coalescence of multiple rupture fronts, we show that they are dominantly caused by rake-rotated surface-breaking rupture fronts that decelerate due to fault heterogeneities or geometric complexity. Our findings highlight the potential of spatially dense ground-motion observations to further our understanding of earthquake physics directly from near-field data. Observed near-field fc variability may inform on directivity, surface rupture, and slip segmentation. Physics-based models can identify “what to look for,” for example, in the potentially vast amount of near-field large array or distributed acoustic sensing data.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591228","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：