Annemarie Christophersen, Matthew C. Gerstenberger
Abstract The 2022 revision of the New Zealand National Seismic Hazard Model—Te Tauira Matapae Pūmate Rū i Aotearoa (NZ NSHM 2022) is, like other regional and national seismic hazard models, a collection of many component models that are combined via logic trees to calculate various parameters of seismic hazard. Developing, selecting, and combining component models for the NZ NSHM 2022 requires expert judgment. Informal and unstructured use of expert judgment can lead to biases. Drawing on a broad body of literature on potential biases in expert judgment and how to mitigate them, we used three approaches to incorporate expert judgment with the aim to minimize biases and understand uncertainty in seismic hazard results. The first approach applied two closely aligned group structures—the Science Team Working Groups and the Technical Advisory Group (TAG). The groups between them defined the project and made the scientific decisions necessary to produce the final model. Second, the TAG provided the function of a participatory review panel, in which the reviewers of the NSHM were actively engaged throughout the project. The third approach was performance-based weighting of expert assessments, which was applied to the weighting of the logic trees. It involved asking experts so-called calibration questions with known answers, which were relevant to the questions of interest, that is, the logic-tree weights. Each expert provided their best estimates with uncertainty, from which calibration and information scores were calculated. The scores were used to weight the experts’ assessments. The combined approach to incorporating expert judgment was intended to provide a robust and well-reviewed application of seismic hazard analysis for Aotearoa, New Zealand. Robust expert judgment processes are critical to any large science project, and our approach may provide learnings and insights for others.
{"title":"Expert Judgment in the 2022 Aotearoa New Zealand National Seismic Hazard Model","authors":"Annemarie Christophersen, Matthew C. Gerstenberger","doi":"10.1785/0220230250","DOIUrl":"https://doi.org/10.1785/0220230250","url":null,"abstract":"Abstract The 2022 revision of the New Zealand National Seismic Hazard Model—Te Tauira Matapae Pūmate Rū i Aotearoa (NZ NSHM 2022) is, like other regional and national seismic hazard models, a collection of many component models that are combined via logic trees to calculate various parameters of seismic hazard. Developing, selecting, and combining component models for the NZ NSHM 2022 requires expert judgment. Informal and unstructured use of expert judgment can lead to biases. Drawing on a broad body of literature on potential biases in expert judgment and how to mitigate them, we used three approaches to incorporate expert judgment with the aim to minimize biases and understand uncertainty in seismic hazard results. The first approach applied two closely aligned group structures—the Science Team Working Groups and the Technical Advisory Group (TAG). The groups between them defined the project and made the scientific decisions necessary to produce the final model. Second, the TAG provided the function of a participatory review panel, in which the reviewers of the NSHM were actively engaged throughout the project. The third approach was performance-based weighting of expert assessments, which was applied to the weighting of the logic trees. It involved asking experts so-called calibration questions with known answers, which were relevant to the questions of interest, that is, the logic-tree weights. Each expert provided their best estimates with uncertainty, from which calibration and information scores were calculated. The scores were used to weight the experts’ assessments. The combined approach to incorporating expert judgment was intended to provide a robust and well-reviewed application of seismic hazard analysis for Aotearoa, New Zealand. Robust expert judgment processes are critical to any large science project, and our approach may provide learnings and insights for others.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778326","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}
Christian Grimm, Sebastian Hainzl, Martin Käser, Helmut Küchenhoff
Abstract The empirical Båth’s law states that the average magnitude difference (ΔM) between a mainshock and its strongest aftershock is ∼1.2, independent of the size of the mainshock. Although this observation can generally be explained by a scaling of aftershock productivity with mainshock magnitude in combination with a Gutenberg–Richter frequency–magnitude distribution, estimates of ΔM may be preferable because they are directly related to the most interesting information, namely the magnitudes of the main events, without relying on assumptions. However, a major challenge in calculating this value is the bias introduced by missing data points when the strongest aftershock is below the observed cut-off magnitude. Ignoring missing values leads to a systematic error because the data points removed are those with particularly large magnitude differences ΔM. The error can be minimized by restricting the statistics to mainshocks that are at least 2 magnitude units above the cutoff, but then the sample size is strongly reduced. This work provides an innovative approach for modeling ΔM by adapting methods for time-to-event data, which often suffer from incomplete observations (censoring). In doing so, we adequately account for unobserved values and estimate a fully parametric distribution of the magnitude differences ΔM for mainshocks in a global earthquake catalog. Our results suggest that magnitude differences are best modeled by the Gompertz distribution and that larger ΔM are expected at increasing depths and higher heat flows.
{"title":"A New Statistical Perspective on Båth’s Law","authors":"Christian Grimm, Sebastian Hainzl, Martin Käser, Helmut Küchenhoff","doi":"10.1785/0220230147","DOIUrl":"https://doi.org/10.1785/0220230147","url":null,"abstract":"Abstract The empirical Båth’s law states that the average magnitude difference (ΔM) between a mainshock and its strongest aftershock is ∼1.2, independent of the size of the mainshock. Although this observation can generally be explained by a scaling of aftershock productivity with mainshock magnitude in combination with a Gutenberg–Richter frequency–magnitude distribution, estimates of ΔM may be preferable because they are directly related to the most interesting information, namely the magnitudes of the main events, without relying on assumptions. However, a major challenge in calculating this value is the bias introduced by missing data points when the strongest aftershock is below the observed cut-off magnitude. Ignoring missing values leads to a systematic error because the data points removed are those with particularly large magnitude differences ΔM. The error can be minimized by restricting the statistics to mainshocks that are at least 2 magnitude units above the cutoff, but then the sample size is strongly reduced. This work provides an innovative approach for modeling ΔM by adapting methods for time-to-event data, which often suffer from incomplete observations (censoring). In doing so, we adequately account for unobserved values and estimate a fully parametric distribution of the magnitude differences ΔM for mainshocks in a global earthquake catalog. Our results suggest that magnitude differences are best modeled by the Gompertz distribution and that larger ΔM are expected at increasing depths and higher heat flows.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883713","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}
Abstract Automated teleseismic earthquake monitoring is an essential part of global seismicity analysis. Although constraining epicenters in an automated fashion is an established technique, constraining event depths is substantially more difficult. One solution to this challenge is teleseismic depth phases, but these can currently not be identified precisely by automatic detection methods. Here, we propose two deep-learning models, DepthPhaseTEAM and DepthPhaseNet, to detect and pick depth phases. For training the models, we create a dataset based on the ISC-EHB bulletin—a high-quality catalog with detailed phase annotations. We show how backprojecting the predicted phase arrival probability curves onto the depth axis yields accurate estimates of earthquake depth. Furthermore, we show how a multistation model, DepthPhaseTEAM, leads to better and more consistent predictions than the single-station model, DepthPhaseNet. To allow direct application of our models, we integrate them within the SeisBench library.
{"title":"Learning the Deep and the Shallow: Deep-Learning-Based Depth Phase Picking and Earthquake Depth Estimation","authors":"Jannes Münchmeyer, Joachim Saul, Frederik Tilmann","doi":"10.1785/0220230187","DOIUrl":"https://doi.org/10.1785/0220230187","url":null,"abstract":"Abstract Automated teleseismic earthquake monitoring is an essential part of global seismicity analysis. Although constraining epicenters in an automated fashion is an established technique, constraining event depths is substantially more difficult. One solution to this challenge is teleseismic depth phases, but these can currently not be identified precisely by automatic detection methods. Here, we propose two deep-learning models, DepthPhaseTEAM and DepthPhaseNet, to detect and pick depth phases. For training the models, we create a dataset based on the ISC-EHB bulletin—a high-quality catalog with detailed phase annotations. We show how backprojecting the predicted phase arrival probability curves onto the depth axis yields accurate estimates of earthquake depth. Furthermore, we show how a multistation model, DepthPhaseTEAM, leads to better and more consistent predictions than the single-station model, DepthPhaseNet. To allow direct application of our models, we integrate them within the SeisBench library.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136033641","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}
Abstract Ambient noise tomography has been widely used to estimate the shear-wave velocity structure of the Earth. A key step in this method is to pick dispersions from dispersion spectrograms. Using the frequency–Bessel (F-J) transform, the generated spectrograms can provide more dispersion information by including higher modes in addition to the fundamental mode. With the increasing availability of these spectrograms, manually picking dispersion curves is highly time and energy consuming. Consequently, neural networks have been used for automatically picking dispersions. Dispersion curves are picked based on deep learning mainly for denoising these spectrograms. In several studies, the neural network was solely trained, and its performance was verified for the denoising. However, they all learn single-source data in the training of neural network. It will lead the regionality of trained neural network. Even if we can use domain adaptation to improve its performance and achieve some success, there are still some spectrograms that cannot be solved effectively. Therefore, multisources training is useful and could reduce the regionality in training stage. Normally, dispersion spectrograms from multisources have feature differences of dispersion curves, especially for higher modes in F-J spectrograms. Thus, we propose a training strategy based on domain confusion through which the neural network effectively learns spectrograms from multisources. After domain confusion, the trained neural network can effectively process large number of test data and help us easily obtain more dispersion curves automatically. The proposed study can provide a deep insight into the denoising of dispersion spectrograms by neural network and facilitate ambient noise tomography.
{"title":"Applying Feature Transformation-Based Domain Confusion to Neural Network for the Denoising of Dispersion Spectrograms","authors":"Weibin Song, Shichuan Yuan, Ming Cheng, Guanchao Wang, Yilong Li, Xiaofei Chen","doi":"10.1785/0220230103","DOIUrl":"https://doi.org/10.1785/0220230103","url":null,"abstract":"Abstract Ambient noise tomography has been widely used to estimate the shear-wave velocity structure of the Earth. A key step in this method is to pick dispersions from dispersion spectrograms. Using the frequency–Bessel (F-J) transform, the generated spectrograms can provide more dispersion information by including higher modes in addition to the fundamental mode. With the increasing availability of these spectrograms, manually picking dispersion curves is highly time and energy consuming. Consequently, neural networks have been used for automatically picking dispersions. Dispersion curves are picked based on deep learning mainly for denoising these spectrograms. In several studies, the neural network was solely trained, and its performance was verified for the denoising. However, they all learn single-source data in the training of neural network. It will lead the regionality of trained neural network. Even if we can use domain adaptation to improve its performance and achieve some success, there are still some spectrograms that cannot be solved effectively. Therefore, multisources training is useful and could reduce the regionality in training stage. Normally, dispersion spectrograms from multisources have feature differences of dispersion curves, especially for higher modes in F-J spectrograms. Thus, we propose a training strategy based on domain confusion through which the neural network effectively learns spectrograms from multisources. After domain confusion, the trained neural network can effectively process large number of test data and help us easily obtain more dispersion curves automatically. The proposed study can provide a deep insight into the denoising of dispersion spectrograms by neural network and facilitate ambient noise tomography.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135992808","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":"SSA News and Notes","authors":"","doi":"10.1785/0220230316","DOIUrl":"https://doi.org/10.1785/0220230316","url":null,"abstract":"","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858597","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}
Wenwen Zhang, Yongqian Zhang, Qingtian Lü, Yutao Shi, Yao Xu, Jiayong Yan
Abstract Intracontinental deformation is out of the theory of conventional plate tectonics. It is widely recognized with deformation within the continental interior instead of the plate margin, yet its formation mechanism has long been controversial. The eastern Sichuan–Wuling mountains (ESWM) area is located ∼1300 km away from the subduction plate boundary and had developed intracontinental deformations, including crustal shortening and fold-and-thrust (FAT) tectonics, making it an ideal place to understand the mechanism of intracontinental deformation. In this study, we obtain a new seismic image of the 3D crustal structure of the ESWM area using the continuous ambient noise data of 67 broadband seismic stations. We invert the Rayleigh-wave dispersions of 5–30 s derived from cross-correlating the Z-component of all station pairs and obtain the fine crustal VS model. Our new seismic image reveals distinct velocity characteristics between the thin-skinned chevron anticline FAT tectonics in the eastern Sichuan basin and the thick-skinned chevron syncline FAT tectonics in the Wuling mountains area. Specifically, a low-VS layer observed beneath the Wuling mountains area, together with the crystalline basement beneath the eastern Sichuan basin, marks the ductile décollements confining the folding and thrusting deformation. Based on our new VS model and some previous studies, we propose a geodynamic model, which is associated with the far-field effect of the westward paleo-Pacific subduction during the late Mesozoic. Our model meets all the structural investigations at surface and geophysical observations at depth, and is reliable and valuable for further studies on similar intracontinental deformation in other regions.
{"title":"New Seismic Imaging of the Crustal Structure beneath the Eastern Sichuan and Wuling Mountains, South China: Insights into the Formation of Fold-and-Thrust Belts","authors":"Wenwen Zhang, Yongqian Zhang, Qingtian Lü, Yutao Shi, Yao Xu, Jiayong Yan","doi":"10.1785/0220230105","DOIUrl":"https://doi.org/10.1785/0220230105","url":null,"abstract":"Abstract Intracontinental deformation is out of the theory of conventional plate tectonics. It is widely recognized with deformation within the continental interior instead of the plate margin, yet its formation mechanism has long been controversial. The eastern Sichuan–Wuling mountains (ESWM) area is located ∼1300 km away from the subduction plate boundary and had developed intracontinental deformations, including crustal shortening and fold-and-thrust (FAT) tectonics, making it an ideal place to understand the mechanism of intracontinental deformation. In this study, we obtain a new seismic image of the 3D crustal structure of the ESWM area using the continuous ambient noise data of 67 broadband seismic stations. We invert the Rayleigh-wave dispersions of 5–30 s derived from cross-correlating the Z-component of all station pairs and obtain the fine crustal VS model. Our new seismic image reveals distinct velocity characteristics between the thin-skinned chevron anticline FAT tectonics in the eastern Sichuan basin and the thick-skinned chevron syncline FAT tectonics in the Wuling mountains area. Specifically, a low-VS layer observed beneath the Wuling mountains area, together with the crystalline basement beneath the eastern Sichuan basin, marks the ductile décollements confining the folding and thrusting deformation. Based on our new VS model and some previous studies, we propose a geodynamic model, which is associated with the far-field effect of the westward paleo-Pacific subduction during the late Mesozoic. Our model meets all the structural investigations at surface and geophysical observations at depth, and is reliable and valuable for further studies on similar intracontinental deformation in other regions.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135859127","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}
Computing theoretical seismograms from a point source in a given Earth model is essential for modeling and inversion of observed seismic waveforms for Earth’s structure and earthquake source parameters. Here, we derived the propagator matrices and source terms for a spherical multilayered Earth model using the exact earth flattening transformation. We found that their differences from their counterparts in horizontal layered media are inversely proportional to the nondimensional horizontal wavenumber and its higher order. In addition, all the source terms in a spherical layered model have a source-depth dependent scaling factor that differs from in a horizontal layered model by up to 6% for deep earthquakes. The surface displacement produced by a point source can be obtained in a similar form as in horizontal layered media. Computation of theoretical seismograms was implemented using the generalized reflection and transmission coefficients method. Numerical tests show that our formulae and implementation are correct and efficient for computing full-wave seismograms, including the permanent displacements, at teleseismic distances up to 100°. Individual seismic phases can be isolated and analyzed semianalytically because the generalized reflection and transmission method is used. Furthermore, our analytic expression of displacement in terms of the propagator matrices and source terms can be used to derive analytic derivatives of seismograms for full-wave waveform inversion.
{"title":"Computing Theoretical Seismograms from a Point Source in a Spherical Multilayered Medium","authors":"Shaoqian Hu, Lupei Zhu","doi":"10.1785/0220230173","DOIUrl":"https://doi.org/10.1785/0220230173","url":null,"abstract":"Computing theoretical seismograms from a point source in a given Earth model is essential for modeling and inversion of observed seismic waveforms for Earth’s structure and earthquake source parameters. Here, we derived the propagator matrices and source terms for a spherical multilayered Earth model using the exact earth flattening transformation. We found that their differences from their counterparts in horizontal layered media are inversely proportional to the nondimensional horizontal wavenumber and its higher order. In addition, all the source terms in a spherical layered model have a source-depth dependent scaling factor that differs from in a horizontal layered model by up to 6% for deep earthquakes. The surface displacement produced by a point source can be obtained in a similar form as in horizontal layered media. Computation of theoretical seismograms was implemented using the generalized reflection and transmission coefficients method. Numerical tests show that our formulae and implementation are correct and efficient for computing full-wave seismograms, including the permanent displacements, at teleseismic distances up to 100°. Individual seismic phases can be isolated and analyzed semianalytically because the generalized reflection and transmission method is used. Furthermore, our analytic expression of displacement in terms of the propagator matrices and source terms can be used to derive analytic derivatives of seismograms for full-wave waveform inversion.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858230","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}
Elaine K. Young, Michael E. Oskin, Alba M. Rodriguez Padilla
Abstract We use multiple, independently produced surface-rupture maps of the 2019 Ridgecrest earthquake sequence to test the reproducibility of surface-rupture map interpretation and completeness. The 4 July Mw 6.4 and 5 July Mw 7.1 earthquakes produced surface-rupture zones approximately 20 and 50 km in length, respectively. Three independent mappers with various backgrounds in active tectonics mapped the surface rupture from the postearthquake lidar data without knowledge from postearthquake field or geodetic observations. Visual comparisons of the three remote rupture maps show good agreement for scarps >50 cm in height. For features with less topographic expression, interpretations of the data vary more widely between mappers. Quantitative map comparisons range from 18% to 54% consistency between mapped lines with 1 m buffers. The percent overlap increases with buffer width, reflecting variance in line placement as well as differences in fault-zone interpretation. Overall, map similarity is higher in areas where the surface rupture was simpler and had more vertical offset than in areas with complex rupture patterns or little vertical offset. Fault-zone interpretation accounts for the most difference between maps, while line placement accounts for differences at the meter scale. In comparison to field observations, our remotely produced maps capture the principal rupture well but miss small features and geometric complexity. In general, lidar excels for the detection and measurement of vertical offsets in the landscape, and it is deficient for detecting lateral offset with little or no vertical motion.
{"title":"Reproducibility of Remote Mapping of the 2019 Ridgecrest Earthquake Surface Ruptures","authors":"Elaine K. Young, Michael E. Oskin, Alba M. Rodriguez Padilla","doi":"10.1785/0220230095","DOIUrl":"https://doi.org/10.1785/0220230095","url":null,"abstract":"Abstract We use multiple, independently produced surface-rupture maps of the 2019 Ridgecrest earthquake sequence to test the reproducibility of surface-rupture map interpretation and completeness. The 4 July Mw 6.4 and 5 July Mw 7.1 earthquakes produced surface-rupture zones approximately 20 and 50 km in length, respectively. Three independent mappers with various backgrounds in active tectonics mapped the surface rupture from the postearthquake lidar data without knowledge from postearthquake field or geodetic observations. Visual comparisons of the three remote rupture maps show good agreement for scarps >50 cm in height. For features with less topographic expression, interpretations of the data vary more widely between mappers. Quantitative map comparisons range from 18% to 54% consistency between mapped lines with 1 m buffers. The percent overlap increases with buffer width, reflecting variance in line placement as well as differences in fault-zone interpretation. Overall, map similarity is higher in areas where the surface rupture was simpler and had more vertical offset than in areas with complex rupture patterns or little vertical offset. Fault-zone interpretation accounts for the most difference between maps, while line placement accounts for differences at the meter scale. In comparison to field observations, our remotely produced maps capture the principal rupture well but miss small features and geometric complexity. In general, lidar excels for the detection and measurement of vertical offsets in the landscape, and it is deficient for detecting lateral offset with little or no vertical motion.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858462","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}
Genevieve L. Coffey, Chris Rollins, Russ J. Van Dissen, David A. Rhoades, Matthew C. Gerstenberger, Nicola J. Litchfield, Kiran K. S. Thingbaijam
Abstract Recurrence intervals of ground-surface rupturing earthquakes are one of numerous datasets used to constrain the rates of fault ruptures in the 2022 revision of the New Zealand National Seismic Hazard Model (NZ NSHM 2022). Paleoearthquake timing and single-event displacement (SED) data in the New Zealand Paleoseismic Site Database version 1.0 alongside geologic and geodetic slip rates from the New Zealand Community Fault Model version 1.0 and NZ NSHM 2022 Geodetic Deformation Model were used to estimate recurrence intervals on faults across New Zealand for inclusion in the NZ NSHM 2022. Past earthquake timings were fit with lognormal, exponential, and Brownian Passage Time recurrence models to derive probability density functions (PDFs) of mean recurrence interval (MRI) in a Bayesian framework. At some sites, SED and slip-rate (SR) data were used to estimate PDFs of MRI; and at sites where timings, slip rate, and displacement data are available, the timings-based and slip-based PDFs were combined to develop tighter constraints on MRI. Using these approaches, we produce a database of maximum-likelihood MRIs and their uncertainties for 80 sites across New Zealand. The resulting recurrence interval dataset is publicly available and is the largest such dataset in New Zealand to date. It provides a valuable resource for future seismic hazard modeling and highlights areas that would benefit from future study.
{"title":"Paleoseismic Earthquake Recurrence Interval Derivation for the 2022 Revision of the New Zealand National Seismic Hazard Model","authors":"Genevieve L. Coffey, Chris Rollins, Russ J. Van Dissen, David A. Rhoades, Matthew C. Gerstenberger, Nicola J. Litchfield, Kiran K. S. Thingbaijam","doi":"10.1785/0220230197","DOIUrl":"https://doi.org/10.1785/0220230197","url":null,"abstract":"Abstract Recurrence intervals of ground-surface rupturing earthquakes are one of numerous datasets used to constrain the rates of fault ruptures in the 2022 revision of the New Zealand National Seismic Hazard Model (NZ NSHM 2022). Paleoearthquake timing and single-event displacement (SED) data in the New Zealand Paleoseismic Site Database version 1.0 alongside geologic and geodetic slip rates from the New Zealand Community Fault Model version 1.0 and NZ NSHM 2022 Geodetic Deformation Model were used to estimate recurrence intervals on faults across New Zealand for inclusion in the NZ NSHM 2022. Past earthquake timings were fit with lognormal, exponential, and Brownian Passage Time recurrence models to derive probability density functions (PDFs) of mean recurrence interval (MRI) in a Bayesian framework. At some sites, SED and slip-rate (SR) data were used to estimate PDFs of MRI; and at sites where timings, slip rate, and displacement data are available, the timings-based and slip-based PDFs were combined to develop tighter constraints on MRI. Using these approaches, we produce a database of maximum-likelihood MRIs and their uncertainties for 80 sites across New Zealand. The resulting recurrence interval dataset is publicly available and is the largest such dataset in New Zealand to date. It provides a valuable resource for future seismic hazard modeling and highlights areas that would benefit from future study.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135923259","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}
Abstract On 14 August 2021, an Mw 7.2 earthquake struck Nippes, Haiti, 11 yr after the devastating 2010 Mw 7.0 Port-au-Prince earthquake. This earthquake occurred in a remote region where the structure at the depth of the main boundary Enriquillo Plantain Garden fault (EPGF) is less known. Using Synthetic Aperture Radar imagery, we retrieve the coseismic and early postseismic deformation of the 2021 Haiti earthquake to constrain its fault geometry and slip distribution. Our modeling results show that the 2021 earthquake ruptured the high-angle Ravine du Sud fault and a bend fault ∼64° dipping to the north at depth. Although not only conclusive, the combination of coseismic and postseismic deformation, along with geomorphic features, and relocated aftershocks, suggest a nonplanar fault structure with significant variations in dip angles along both the depth and track of the EPGF in this region. East of the epicenter, we document a 25 km section along the EPGF that crept for ∼15 days. This distribution of aseismic slip utilizing stacked deformation indicates that only a small fraction of the accumulated strain near the surface was released during the earthquake, suggesting a high potential for seismic hazard in the region along the EPGF from the ruptured segment to the east, before reaching the 2010 rupture.
{"title":"Coseismic and Early Postseismic Slip of the 2021 Mw 7.2 Nippes, Haiti, Earthquake: Transpressional Rupture of a Nonplanar Dipping Fault System","authors":"Zhen Li, Teng Wang","doi":"10.1785/0220230160","DOIUrl":"https://doi.org/10.1785/0220230160","url":null,"abstract":"Abstract On 14 August 2021, an Mw 7.2 earthquake struck Nippes, Haiti, 11 yr after the devastating 2010 Mw 7.0 Port-au-Prince earthquake. This earthquake occurred in a remote region where the structure at the depth of the main boundary Enriquillo Plantain Garden fault (EPGF) is less known. Using Synthetic Aperture Radar imagery, we retrieve the coseismic and early postseismic deformation of the 2021 Haiti earthquake to constrain its fault geometry and slip distribution. Our modeling results show that the 2021 earthquake ruptured the high-angle Ravine du Sud fault and a bend fault ∼64° dipping to the north at depth. Although not only conclusive, the combination of coseismic and postseismic deformation, along with geomorphic features, and relocated aftershocks, suggest a nonplanar fault structure with significant variations in dip angles along both the depth and track of the EPGF in this region. East of the epicenter, we document a 25 km section along the EPGF that crept for ∼15 days. This distribution of aseismic slip utilizing stacked deformation indicates that only a small fraction of the accumulated strain near the surface was released during the earthquake, suggesting a high potential for seismic hazard in the region along the EPGF from the ruptured segment to the east, before reaching the 2010 rupture.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013334","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}
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