Zhitu Ma, Ramees R. Mir, Colleen A. Dalton, Karen E. Godfrey
Many linear(ized) geophysical inverse problems cannot be solved without regularization. Finding the regularization parameter that best balances the model complexity and data misfit is often a key step in the inversion problem. Traditionally, this is done by first plotting the measure of model complexity versus data misfit for different values of regularization parameter, which manifests as an L-shaped curve, and then choosing the regularization parameter corresponding to the corner point on the L-curve. For this approach, the difference in units between model complexity and data misfit must be considered, otherwise the result will be strongly affected by the scaling between these two quantities. Inspired by the machine learning literature, we here propose an extension to the traditional L-curve method. We first split the raw dataset into training and validation sets, obtain a solution by performing inversion on the training set only, and calculate data misfits on the validation set. We demonstrate the efficacy of this approach with a toy example and with two synthetic datasets. In realistic global surface-wave tomography studies where sampling of the Earth is nonuniform, we devise a procedure to generate a validation dataset with sampling as uniform as possible. We then show that the regularization parameter can be determined using this validation set, and this determination is apparently robust to the ratio of data split between training and validation sets. For both synthetic tests and realistic inversions, we find that our procedure can produce a minimal point that can be easily identified on the misfit curves calculated on the validation sets, and avoids the nuances encountered in the traditional L-curve analysis.
许多线性（化）地球物理反演问题都离不开正则化。找到最能平衡模型复杂性和数据误拟合的正则化参数往往是反演问题的关键步骤。传统的做法是，首先绘制不同正则化参数值下的模型复杂度与数据不拟合度的对比图（表现为 L 型曲线），然后选择与 L 型曲线上的角点相对应的正则化参数。对于这种方法，必须考虑模型复杂度和数据误拟合之间的单位差异，否则结果会受到这两个量之间的比例关系的强烈影响。受机器学习文献的启发，我们在此提出了对传统 L 曲线方法的扩展。我们首先将原始数据集拆分为训练集和验证集，仅在训练集上执行反演获得解决方案，然后计算验证集上的数据误差。我们用一个玩具实例和两个合成数据集证明了这种方法的有效性。在现实的全球地表波层析成像研究中，地球的采样是不均匀的，我们设计了一个程序来生成采样尽可能均匀的验证数据集。然后，我们证明可以利用该验证集确定正则化参数，而且该确定方法对训练集和验证集之间的数据分割比例具有明显的稳健性。对于合成测试和实际反演，我们发现我们的程序可以产生一个最小点，这个最小点可以很容易地在验证集计算的误拟合曲线上识别出来，并避免了传统 L 曲线分析中遇到的细微差别。
{"title":"Choosing Appropriate Regularization Parameters by Splitting Data into Training and Validation Sets—Application in Global Surface-Wave Tomography","authors":"Zhitu Ma, Ramees R. Mir, Colleen A. Dalton, Karen E. Godfrey","doi":"10.1785/0220230032","DOIUrl":"https://doi.org/10.1785/0220230032","url":null,"abstract":"\u0000 Many linear(ized) geophysical inverse problems cannot be solved without regularization. Finding the regularization parameter that best balances the model complexity and data misfit is often a key step in the inversion problem. Traditionally, this is done by first plotting the measure of model complexity versus data misfit for different values of regularization parameter, which manifests as an L-shaped curve, and then choosing the regularization parameter corresponding to the corner point on the L-curve. For this approach, the difference in units between model complexity and data misfit must be considered, otherwise the result will be strongly affected by the scaling between these two quantities. Inspired by the machine learning literature, we here propose an extension to the traditional L-curve method. We first split the raw dataset into training and validation sets, obtain a solution by performing inversion on the training set only, and calculate data misfits on the validation set. We demonstrate the efficacy of this approach with a toy example and with two synthetic datasets. In realistic global surface-wave tomography studies where sampling of the Earth is nonuniform, we devise a procedure to generate a validation dataset with sampling as uniform as possible. We then show that the regularization parameter can be determined using this validation set, and this determination is apparently robust to the ratio of data split between training and validation sets. For both synthetic tests and realistic inversions, we find that our procedure can produce a minimal point that can be easily identified on the misfit curves calculated on the validation sets, and avoids the nuances encountered in the traditional L-curve analysis.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119572","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}
Francesco Pintori, Federica Sparacino, Federica Riguzzi
We analyze the interplay between hydrology, deformation, and seismicity in the Matese massif, located in the Italian Southern Apennines. We find that this area is characterized by the concurrent action of two hydrologically driven processes: the first is the deformation detected by the Global Navigation Satellite Systems (GNSS) data in the shallowest part (above the elevation of the major springs) of the Earth crust, in phase with the hydrological forcing; the second is the triggering of seismicity at depth with a delay suggesting a downward diffusive process. We study the first process by applying a principal component analysis to the GNSS displacements time series, aiming to identify a common signal describing the largest data variance. We find that the maximum horizontal displacements associated with the first principal component (PC1) are larger than 1 cm in two GNSS sites, and the PC1 temporal evolution is well correlated and in phase with the flow of the largest spring of the region, which we consider as proxy of the water content of the massif. This suggests that the main source of horizontal deformation is the water content fluctuations in the shallow portion of the Matese aquifer, in particular within fractures located in correspondence of the main mapped faults. The deformation rates caused by this process are one order of magnitude larger than the tectonic ones. Finally, we infer the second process by observing the correlation between the background seismicity and the spring discharge with a time lag of 121 days. In our interpretation, downward diffusive processes, driven by aquifer water content variations, propagate pore‐pressure waves that affect the fault’s strength favoring the occurrence of microearthquakes. This is supported by the values of hydraulic diffusivity (1.5 m2/s) and rock permeability (3.2–3.8×10−13 m2), which are compatible with what is observed in karstified limestones.
{"title":"Hydrology Drives Crustal Deformation and Modulates Seismicity in the Matese Massif (Italy)","authors":"Francesco Pintori, Federica Sparacino, Federica Riguzzi","doi":"10.1785/0220230239","DOIUrl":"https://doi.org/10.1785/0220230239","url":null,"abstract":"We analyze the interplay between hydrology, deformation, and seismicity in the Matese massif, located in the Italian Southern Apennines. We find that this area is characterized by the concurrent action of two hydrologically driven processes: the first is the deformation detected by the Global Navigation Satellite Systems (GNSS) data in the shallowest part (above the elevation of the major springs) of the Earth crust, in phase with the hydrological forcing; the second is the triggering of seismicity at depth with a delay suggesting a downward diffusive process. We study the first process by applying a principal component analysis to the GNSS displacements time series, aiming to identify a common signal describing the largest data variance. We find that the maximum horizontal displacements associated with the first principal component (PC1) are larger than 1 cm in two GNSS sites, and the PC1 temporal evolution is well correlated and in phase with the flow of the largest spring of the region, which we consider as proxy of the water content of the massif. This suggests that the main source of horizontal deformation is the water content fluctuations in the shallow portion of the Matese aquifer, in particular within fractures located in correspondence of the main mapped faults. The deformation rates caused by this process are one order of magnitude larger than the tectonic ones. Finally, we infer the second process by observing the correlation between the background seismicity and the spring discharge with a time lag of 121 days. In our interpretation, downward diffusive processes, driven by aquifer water content variations, propagate pore‐pressure waves that affect the fault’s strength favoring the occurrence of microearthquakes. This is supported by the values of hydraulic diffusivity (1.5 m2/s) and rock permeability (3.2–3.8×10−13 m2), which are compatible with what is observed in karstified limestones.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140803271","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}
This study develops data‐driven global and region‐specific ground‐motion models (GMMs) for subduction earthquakes using a weighted average ensemble model to combine four different nonparametric supervised machine‐learning (ML) algorithms, including an artificial neural network, a kernel ridge regressor, a random forest regressor, and a support vector regressor. To achieve this goal, we train individual models using a subset of the Next Generation Attenuation‐Subduction (NGA‐Sub) data set, including 9559 recordings out of 153 interface and intraslab earthquakes recorded at 3202 different stations. A grid search is used to find each model’s best hyperparameters. Then, we use an equally weighted average ensemble approach to combine these four models. Ensemble modeling is a technique that combines the strengths of multiple ML algorithms to mitigate their weaknesses. The ensemble model considers moment magnitude (M), rupture distance (Rrup), time‐averaged shear‐wave velocity in the upper 30 m (VS30), and depth to the top of the rupture plane (Ztor), as well as tectonic and region as input parameters, and predicts various median orientation‐independent horizontal component ground‐motion intensity measures such as peak ground displacement, peak ground velocity, peak ground acceleration, and 5%‐damped pseudospectral acceleration values at spectral periods of 0.01–10 s in log scale. Although no functional form is defined, the response spectra and the distance and magnitude scaling trends of the weighted average ensemble model are consistent and comparable with the NGA‐Sub GMMs, with slightly lower standard deviations. A mixed effects regression analysis is used to partition the total aleatory variability into between‐event, between‐station, and event‐site‐corrected components. The derived global GMMs are applicable to interface earthquakes with M 4.9–9.12, 14≤Rrup≤1000 km, and Ztor≤47 km for sites having VS30values between 95 and 2230 m/s. For intraslab events, the derived global GMMs are applicable to M 4.0–8.0, 28≤Rrup≤1000 km, and 30≤Ztor≤200 km for sites having VS30 values between 95 and 2100 m/s.
本研究使用加权平均集合模型，结合四种不同的非参数监督机器学习（ML）算法，包括人工神经网络、核脊回归器、随机森林回归器和支持向量回归器，为俯冲地震开发数据驱动的全球和特定区域地动模型（GMM）。为了实现这一目标，我们使用下一代衰减-减弱（NGA-Sub）数据集的子集来训练各个模型，其中包括在 3202 个不同站点记录的 153 次界面和实验室内地震中的 9559 次记录。我们使用网格搜索来找到每个模型的最佳超参数。然后，我们使用加权平均集合方法来组合这四个模型。集合建模是一种结合多种 ML 算法的优点以减轻其缺点的技术。集合模型将力矩大小（M）、断裂距离（Rrup）、上部 30 米的时间平均剪切波速度（VS30）、到断裂面顶部的深度（Ztor）以及构造和区域作为输入参数，并预测各种与方位无关的水平分量地动强度中值，如地表位移峰值、地表速度峰值、地表加速度峰值以及频谱周期为 0.01-10 秒的对数标度。虽然没有定义函数形式，但加权平均集合模型的响应谱以及距离和幅度缩放趋势与 NGA-Sub GMMs 一致并具有可比性，标准偏差略低。通过混合效应回归分析，将总的人工变异性划分为事件间、站点间和事件-站点校正部分。推导出的全球 GMM 适用于 VS30 值在 95 至 2230 m/s 之间的站点，M 值为 4.9-9.12、14≤Rrup≤1000 km 和 Ztor≤47 km 的界面地震。对于台内事件，得出的全球 GMM 适用于 M 4.0-8.0、28≤Rrup≤1000 km 和 30≤Ztor≤200 km（VS30 值在 95 至 2100 m/s 之间）的站点。
{"title":"Ensemble Region‐Specific GMMs for Subduction Earthquakes","authors":"Farhad Sedaghati, Shahram Pezeshk","doi":"10.1785/0220230070","DOIUrl":"https://doi.org/10.1785/0220230070","url":null,"abstract":"This study develops data‐driven global and region‐specific ground‐motion models (GMMs) for subduction earthquakes using a weighted average ensemble model to combine four different nonparametric supervised machine‐learning (ML) algorithms, including an artificial neural network, a kernel ridge regressor, a random forest regressor, and a support vector regressor. To achieve this goal, we train individual models using a subset of the Next Generation Attenuation‐Subduction (NGA‐Sub) data set, including 9559 recordings out of 153 interface and intraslab earthquakes recorded at 3202 different stations. A grid search is used to find each model’s best hyperparameters. Then, we use an equally weighted average ensemble approach to combine these four models. Ensemble modeling is a technique that combines the strengths of multiple ML algorithms to mitigate their weaknesses. The ensemble model considers moment magnitude (M), rupture distance (Rrup), time‐averaged shear‐wave velocity in the upper 30 m (VS30), and depth to the top of the rupture plane (Ztor), as well as tectonic and region as input parameters, and predicts various median orientation‐independent horizontal component ground‐motion intensity measures such as peak ground displacement, peak ground velocity, peak ground acceleration, and 5%‐damped pseudospectral acceleration values at spectral periods of 0.01–10 s in log scale. Although no functional form is defined, the response spectra and the distance and magnitude scaling trends of the weighted average ensemble model are consistent and comparable with the NGA‐Sub GMMs, with slightly lower standard deviations. A mixed effects regression analysis is used to partition the total aleatory variability into between‐event, between‐station, and event‐site‐corrected components. The derived global GMMs are applicable to interface earthquakes with M 4.9–9.12, 14≤Rrup≤1000 km, and Ztor≤47 km for sites having VS30values between 95 and 2230 m/s. For intraslab events, the derived global GMMs are applicable to M 4.0–8.0, 28≤Rrup≤1000 km, and 30≤Ztor≤200 km for sites having VS30 values between 95 and 2100 m/s.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140803334","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 Seismological Society of America (SSA) topical conference, Future Directions for Physics‐Based Ground Motion Modeling, was held in Vancouver, Canada, on 10–13 October 2023, co‐sponsored by the Seismological Society of Japan and co‐chaired by Annemarie Baltay of the U.S. Geological Survey and Hiroshi Kawase of Kyoto University. This meeting brought together many researchers and practitioners interested in modeling, observing, and utilizing ground‐motion models (GMMs). Scientists gathered to discuss complex kinematic and dynamic rupture simulation approaches, empirical representations of the earthquake source, site and path effects, physical modeling of the recording site, challenges for model extrapolation, and overall prediction accuracy and...
{"title":"Summary of the Discussions During the 2023 SSA Topical Meeting on “Future Directions for Physics‐Based Ground Motion Modeling”","authors":"Hiroshi Kawase, Annemarie Baltay","doi":"10.1785/0220240084","DOIUrl":"https://doi.org/10.1785/0220240084","url":null,"abstract":"The Seismological Society of America (SSA) topical conference, Future Directions for Physics‐Based Ground Motion Modeling, was held in Vancouver, Canada, on 10–13 October 2023, co‐sponsored by the Seismological Society of Japan and co‐chaired by Annemarie Baltay of the U.S. Geological Survey and Hiroshi Kawase of Kyoto University. This meeting brought together many researchers and practitioners interested in modeling, observing, and utilizing ground‐motion models (GMMs). Scientists gathered to discuss complex kinematic and dynamic rupture simulation approaches, empirical representations of the earthquake source, site and path effects, physical modeling of the recording site, challenges for model extrapolation, and overall prediction accuracy and...","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140803337","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 2024 Annual Meeting","authors":"","doi":"10.1785/0220240136","DOIUrl":"https://doi.org/10.1785/0220240136","url":null,"abstract":"Abstract not available","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140628005","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}
Y. Sheng, A. Mordret, F. Brenguier, Lisa Tomasetto, Q. Higueret, Coralie Aubert, Dan Hollis, Frank Vernon, Y. Ben‐Zion
We present results based on data of a dense nodal array composed of 147 stations, deployed in 2022 near the epicenter of the 2019 Mw 7.1 Ridgecrest earthquake to investigate characteristics of the seismic wavefields. Through array analyses, we identified two primary components. First, we observed far-field P waves dominating the 0.5–1.2 Hz frequency range, which are likely primarily generated by wind-driven oceanic swell activity. Second, we detected near-field body waves resulting from anthropogenic activities in the frequency range 2–8 Hz. We examined noise correlation functions derived from data of the dense deployment and regional stations to explore fault-zone seismic velocity changes using ballistic arrivals, with a focus on velocity perturbation shortly before and after the Ridgecrest earthquake sequence. Our findings exhibit distinct behavior compared to results obtained through standard coda-wave interferometry. Particularly, we observed a decrease in P-wave travel time on certain station pairs prior to the 2019 earthquake sequence. Supported by detailed investigation of the local seismic wavefields, we interpret the decreasing P-wave travel time as likely caused by a velocity increase away from the fault, possibly related to fluid migration. However, additional information is necessary to verify this hypothesis.
2022 年，我们在 2019 年里奇克雷斯特 7.1 级地震震中附近部署了由 147 个台站组成的密集节点阵列，以研究地震波场的特征。通过阵列分析，我们确定了两个主要组成部分。首先，我们观察到远场 P 波在 0.5-1.2 Hz 频率范围内占主导地位，这可能主要是由风驱动的海洋膨胀活动产生的。其次，我们在频率为 2-8 Hz 的范围内检测到了人为活动产生的近场体波。我们研究了从密集部署和区域台站数据中得出的噪声相关函数，利用弹道到达来探索断层带地震速度的变化，重点是里奇克雷斯特地震序列发生前后不久的速度扰动。与通过标准科达波干涉测量法获得的结果相比，我们的研究结果显示出与众不同的行为。特别是，我们观察到在 2019 年地震序列之前，某些站对的 P 波传播时间有所减少。在对当地地震波场进行详细调查的支持下，我们将 P 波传播时间的减少解释为可能是由远离断层的速度增加引起的，可能与流体迁移有关。然而，要验证这一假设，还需要更多的信息。
{"title":"Tracking Seismic Velocity Perturbations at Ridgecrest Using Ballistic Correlation Functions","authors":"Y. Sheng, A. Mordret, F. Brenguier, Lisa Tomasetto, Q. Higueret, Coralie Aubert, Dan Hollis, Frank Vernon, Y. Ben‐Zion","doi":"10.1785/0220230348","DOIUrl":"https://doi.org/10.1785/0220230348","url":null,"abstract":"\u0000 We present results based on data of a dense nodal array composed of 147 stations, deployed in 2022 near the epicenter of the 2019 Mw 7.1 Ridgecrest earthquake to investigate characteristics of the seismic wavefields. Through array analyses, we identified two primary components. First, we observed far-field P waves dominating the 0.5–1.2 Hz frequency range, which are likely primarily generated by wind-driven oceanic swell activity. Second, we detected near-field body waves resulting from anthropogenic activities in the frequency range 2–8 Hz. We examined noise correlation functions derived from data of the dense deployment and regional stations to explore fault-zone seismic velocity changes using ballistic arrivals, with a focus on velocity perturbation shortly before and after the Ridgecrest earthquake sequence. Our findings exhibit distinct behavior compared to results obtained through standard coda-wave interferometry. Particularly, we observed a decrease in P-wave travel time on certain station pairs prior to the 2019 earthquake sequence. Supported by detailed investigation of the local seismic wavefields, we interpret the decreasing P-wave travel time as likely caused by a velocity increase away from the fault, possibly related to fluid migration. However, additional information is necessary to verify this hypothesis.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225630","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}
T. Amashukeli, L. Farfuliak, O. Haniiev, Kostiantyn Petrenko
{"title":"Ukrainian Seismic Network: Current Status and Challenges","authors":"T. Amashukeli, L. Farfuliak, O. Haniiev, Kostiantyn Petrenko","doi":"10.1785/0220230337","DOIUrl":"https://doi.org/10.1785/0220230337","url":null,"abstract":"","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140231445","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}
Junhui Xing, Haowei Xu, Wei Gong, Boxue Yang, Chuang Liu
The current research focus at Chukchi Boardland (CB) revolves around sediment stratification and crustal structure, but investigations into deep stress fields and mantle dynamics are limited. This article presents a study on the anisotropic characteristics of the CB. Shear-wave splitting measurements were conducted using the transverse energy minimization at six stations recovered from the 11th Chinese National Arctic Research Expedition. The observation period for these six stations ranged from 2 August 2020 to 8 September 2020. The results demonstrate significant anisotropy within the CB, with the fast shear-wave polarization direction ranging from N60°E to N70°E. The time delays between fast and slow shear waves were found to be ∼0.7 s. By comparing the anisotropy observed at the CB with that at land stations in Arctic Alaska, this study suggested that the genesis of anisotropy beneath the CB was related to the formation of the Amerasian basin. The tectonic processes of rifting during basin evolution and midocean ridge spreading led to the development of anisotropy in the lithosphere beneath the CB during expansion.
{"title":"Anisotropic Characterization of the Chukchi Boardland Based on Ocean-Bottom Seismic Experiment during N11-CHINARE","authors":"Junhui Xing, Haowei Xu, Wei Gong, Boxue Yang, Chuang Liu","doi":"10.1785/0220230349","DOIUrl":"https://doi.org/10.1785/0220230349","url":null,"abstract":"\u0000 The current research focus at Chukchi Boardland (CB) revolves around sediment stratification and crustal structure, but investigations into deep stress fields and mantle dynamics are limited. This article presents a study on the anisotropic characteristics of the CB. Shear-wave splitting measurements were conducted using the transverse energy minimization at six stations recovered from the 11th Chinese National Arctic Research Expedition. The observation period for these six stations ranged from 2 August 2020 to 8 September 2020. The results demonstrate significant anisotropy within the CB, with the fast shear-wave polarization direction ranging from N60°E to N70°E. The time delays between fast and slow shear waves were found to be ∼0.7 s. By comparing the anisotropy observed at the CB with that at land stations in Arctic Alaska, this study suggested that the genesis of anisotropy beneath the CB was related to the formation of the Amerasian basin. The tectonic processes of rifting during basin evolution and midocean ridge spreading led to the development of anisotropy in the lithosphere beneath the CB during expansion.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241908","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}
Zhifan Wan, Rendong Dong, Dun Wang, Shiqing Xu, Zhifeng Wang, Qi Wang
On 6 February 2023, an Mw 7.8 earthquake occurred along the East Anatolian fault zone (EAFZ) in southeastern Türkiye, representing the strongest earthquake in the region in nearly 80 yr. We investigate rupture characteristics and aftershock patterns of the earthquake through focal mechanism calculation, backprojection analysis, and finite-fault inversion. The results show bilateral rupture propagation of the mainshock with transient supershear speed in the southwest portion of the EAFZ, as well as shallower coseismic slip and abundant normal-faulting aftershocks in the same portion. We attribute these earthquake behaviors to the along-strike variation of fault structure of the EAFZ, which features a more complex fault geometry accompanied by numerous short normal faults in the southwest portion. These results shed light on fault segmentation, rupture speed variation, and slip partitioning along the EAFZ, advancing our understanding of fault structural control on earthquake behaviors in a complex multisegment fault system.
{"title":"Along-Strike Variation of Rupture Characteristics and Aftershock Patterns of the 2023 Mw 7.8 Türkiye Earthquake Controlled by Fault Structure","authors":"Zhifan Wan, Rendong Dong, Dun Wang, Shiqing Xu, Zhifeng Wang, Qi Wang","doi":"10.1785/0220230378","DOIUrl":"https://doi.org/10.1785/0220230378","url":null,"abstract":"\u0000 On 6 February 2023, an Mw 7.8 earthquake occurred along the East Anatolian fault zone (EAFZ) in southeastern Türkiye, representing the strongest earthquake in the region in nearly 80 yr. We investigate rupture characteristics and aftershock patterns of the earthquake through focal mechanism calculation, backprojection analysis, and finite-fault inversion. The results show bilateral rupture propagation of the mainshock with transient supershear speed in the southwest portion of the EAFZ, as well as shallower coseismic slip and abundant normal-faulting aftershocks in the same portion. We attribute these earthquake behaviors to the along-strike variation of fault structure of the EAFZ, which features a more complex fault geometry accompanied by numerous short normal faults in the southwest portion. These results shed light on fault segmentation, rupture speed variation, and slip partitioning along the EAFZ, advancing our understanding of fault structural control on earthquake behaviors in a complex multisegment fault system.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140244438","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}
G. Tepp, I. Stubailo, Monica Kohler, Richard Guy, Yousef Bozorgnia
Large music festivals and stadium concerts are known to produce unique vibration signals that resemble harmonic tremor, particularly at frequencies around 1–10 Hz. This study investigates the seismic signals of a Taylor Swift concert performed on 5 August 2023 (UTC) as part of a series at SoFi Stadium in Inglewood, California, with an audience of ∼70,000. Signals were recorded on regional seismic network stations located within ∼9 km of the stadium, as well as on strong-motion sensors placed near and inside the stadium prior to the concert series. We automatically identified the seismic signals from spectrograms using a Hough transform approach and characterized their start times, durations, frequency content, particle motions, radiated energy, and equivalent magnitudes. These characteristics allowed us to associate the signals with individual songs and explore the nature of the seismic source. The signal frequencies matched the song beat rates well, whereas the signal and song durations were less similar. Radiated energy was determined to be a more physically relevant measure of strength than magnitude, given the tremor-like nature of the signals. The structural response of the stadium showed nearly equal shaking intensities in the vertical and horizontal directions at frequencies that match the seismic signals recorded outside the stadium. In addition, we conducted a brief experiment to further evaluate whether the harmonic tremor signals could be generated by the speaker system and instruments, audience motions, or something else. All evidence considered, we interpret the signal source as primarily crowd motion in response to the music. The particle motions of the strongest harmonics are consistent with Rayleigh waves influenced by scattered body waves and likely reflect how the crowd is moving. Results from three other musical performances at SoFi in summer 2023 were similar, although differences in the signals may relate to the musical genre and variations in audience motions.
{"title":"Shake to the Beat: Exploring the Seismic Signals and Stadium Response of Concerts and Music Fans","authors":"G. Tepp, I. Stubailo, Monica Kohler, Richard Guy, Yousef Bozorgnia","doi":"10.1785/0220230385","DOIUrl":"https://doi.org/10.1785/0220230385","url":null,"abstract":"\u0000 Large music festivals and stadium concerts are known to produce unique vibration signals that resemble harmonic tremor, particularly at frequencies around 1–10 Hz. This study investigates the seismic signals of a Taylor Swift concert performed on 5 August 2023 (UTC) as part of a series at SoFi Stadium in Inglewood, California, with an audience of ∼70,000. Signals were recorded on regional seismic network stations located within ∼9 km of the stadium, as well as on strong-motion sensors placed near and inside the stadium prior to the concert series. We automatically identified the seismic signals from spectrograms using a Hough transform approach and characterized their start times, durations, frequency content, particle motions, radiated energy, and equivalent magnitudes. These characteristics allowed us to associate the signals with individual songs and explore the nature of the seismic source. The signal frequencies matched the song beat rates well, whereas the signal and song durations were less similar. Radiated energy was determined to be a more physically relevant measure of strength than magnitude, given the tremor-like nature of the signals. The structural response of the stadium showed nearly equal shaking intensities in the vertical and horizontal directions at frequencies that match the seismic signals recorded outside the stadium. In addition, we conducted a brief experiment to further evaluate whether the harmonic tremor signals could be generated by the speaker system and instruments, audience motions, or something else. All evidence considered, we interpret the signal source as primarily crowd motion in response to the music. The particle motions of the strongest harmonics are consistent with Rayleigh waves influenced by scattered body waves and likely reflect how the crowd is moving. Results from three other musical performances at SoFi in summer 2023 were similar, although differences in the signals may relate to the musical genre and variations in audience motions.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140245685","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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