Seismological Research Letters最新文献_第3页

Using Convolutional Neural Network to Determine Time Window for Analyzing Local Shear-Wave Splitting Measurements 利用卷积神经网络确定分析局部剪切波分裂测量的时间窗口

Seismological Research Letters Pub Date : 2024-07-08 DOI: 10.1785/0220230410

Yanwei Zhang, Stephen S. Gao

{"title":"Using Convolutional Neural Network to Determine Time Window for Analyzing Local Shear-Wave Splitting Measurements","authors":"Yanwei Zhang, Stephen S. Gao","doi":"10.1785/0220230410","DOIUrl":"https://doi.org/10.1785/0220230410","url":null,"abstract":"\u0000 The time window for analyzing local shear-wave splitting (SWS) phases significantly affects the quality of measurements, revealing a noteworthy domain influence. In this study, an approach using convolutional neural network (CNN) is applied to determine the end of time window (e), which has a similar idea of the phase-picking CNNs. The start of time window is 0.5 s before e. Our data set contains 803 human-labeled measurements, recorded from three stations located in Ridgecrest, California. These measurements are foreshocks and aftershocks of an M 7.1 earthquake on 6 July 2019. After 21 times shifting on each measurement, 90% of the data set is applied as the training data set, with the remaining 10% as the testing data set. The performance of CNN with the testing data set is compared with a nonmachine learning method, multiple filter automatic splitting technique (MFAST). The results reveal that the CNN yields more similar results with human-labeled outcomes than MFAST, as evidenced by lower absolute error and standard deviation for e, SWS time, the orientation of fast-wave polarization, and more consistent results on the map. The CNN also performs well when applied to data recorded by a station in Parkfield, California. This study shows the outstanding performance of CNN in picking the time window and the reliable automatic determination of this time window, and it is also a crucial step for future development of automatic ranking methodologies.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141668619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Earthquake Detectability and Depth Resolution with Dense Arrays in Long Beach, California: Further Evidence for Upper-Mantle Seismicity within a Continental Setting 加利福尼亚长滩密集阵列的地震可探测性和深度分辨率：大陆环境下上地幔地震的进一步证据

Seismological Research Letters Pub Date : 2024-07-08 DOI: 10.1785/0220240035

A. Inbal, J. Ampuero, Robert W. Clayton

{"title":"Earthquake Detectability and Depth Resolution with Dense Arrays in Long Beach, California: Further Evidence for Upper-Mantle Seismicity within a Continental Setting","authors":"A. Inbal, J. Ampuero, Robert W. Clayton","doi":"10.1785/0220240035","DOIUrl":"https://doi.org/10.1785/0220240035","url":null,"abstract":"\u0000 The Newport–Inglewood fault (NIF) is a slowly deforming fault cutting through a thin continental crust with a normal geothermal; yet it hosts some of the deepest earthquakes in southern California. The nucleation of deep earthquakes in such a continental setting is not well understood. Moreover, the deep seismogenic zone implies that the maximum NIF earthquake magnitude may be larger than expected. Here, we quantify the resolution of the Long Beach (LB) and the Extended Long Beach (ELB) dense arrays used to study deep NIF seismicity. Previous study of the regional catalog and of downward-continued LB array data found NIF seismicity extending into the upper mantle beneath LB. Later studies, which analyzed the ELB raw data, found little evidence for such deep events. To resolve this inconsistency, we quantify the array’s microearthquake detectability and resolution power via analysis of pre- and postdownward migrated LB seismograms and benchmark tests. Downward migration focuses energy onto the source region and deamplifies the surface noise, thus significantly improving detectability and resolution. The detectability is also improved with the increase in the array aperture-to-source-depth ratio. The LB array maximum aperture is only 20% larger than the ELB aperture, yet its resolution for deep (>20 km) events is improved by about a factor of two, suggesting that small changes to the array geometry may yield significant improvement to the resolution power. Assuming a constant aperture, we find the LB array maintain resolution with 1% of its sensors used for backprojection. However, the high-sensor density is essential for improving the signal-to-noise ratio. Analysis of the regional and array-derived NIF catalogs together with newly acquired Moho depths beneath the NIF suggests that mantle seismicity beneath LB may be a long-lived feature of this fault.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 1276","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Memories of the 1989 Loma Prieta Earthquake 回忆 1989 年洛马普列塔地震

Seismological Research Letters Pub Date : 2024-07-08 DOI: 10.1785/0220240236

W. Bakun

引用次数: 0

Reactivated Seismogenic Faults and Earthquake Source Mechanisms in the Snyder Area of Texas 得克萨斯州斯奈德地区重新激活的地震断层和震源机制

Seismological Research Letters Pub Date : 2024-07-08 DOI: 10.1785/0220240048

Guo-Chin Dino Huang, Alexandras Savvaidis

{"title":"Reactivated Seismogenic Faults and Earthquake Source Mechanisms in the Snyder Area of Texas","authors":"Guo-Chin Dino Huang, Alexandras Savvaidis","doi":"10.1785/0220240048","DOIUrl":"https://doi.org/10.1785/0220240048","url":null,"abstract":"\u0000 Stretching across New Mexico and Texas of the United States, the greater Permian basin is composed of two subunits—the Delaware and the Midland basins. Induced seismicity in the greater Permian basin has significantly increased since 2008, which has revealed previously unmapped seismogenic structures in several geographic regions. Among them, the Snyder area of northwest Texas has a long history of oil and gas activities, resulting in a higher rate of induced seismicity. In this study, we investigated these previously unknown seismogenic structures using three main approaches: (1) relocated and delineated seismicity, (2) performed waveform moment tensor inversion to determine earthquake source mechanisms, as well as (3) conducted stress inversion to assess the stress state. The results show that the overall depth range of seismicity is 0–5.5 km and concentrated in a range of 2–3 km below mean sea level, in the top portion of the crystalline basement. As we have determined 297 source mechanisms, their collective pattern presents a mix of strike-slip and normal faulting, suggesting an extensional strain field at the edge of the Midland basin. We have identified nine significant seismogenic episodes by distinctive increases of seismic moment release in 2017–March 2024. The results also demonstrate a temporal variation of b-value spanning across the seismogenic episodes, associated with the progression of fault reactivation initiated by fluid injection.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 879","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the Predictive Power of GPS-Based Ground Deformation Data for Aftershock Forecasting 评估基于 GPS 的地面形变数据对余震预测的预测能力

Seismological Research Letters Pub Date : 2024-07-03 DOI: 10.1785/0220240008

Vincenzo Maria Schimmenti, Giuseppe Petrillo, Alberto Rosso, F. Landes

引用次数: 0

Integrated Amphibious Distributed Acoustic Sensing for Seismic Monitoring in the Xinfengjiang Reservoir 用于新丰江水库地震监测的水陆两栖分布式综合声学传感技术

Seismological Research Letters Pub Date : 2024-07-03 DOI: 10.1785/0220240001

Chao Li, Xingda Jiang, Min Xu, Haocai Huang, Zhuo Xiao, Yuejin Li, Zehui Lin, Hongxing Cui, Siyuan Cang, Xiaoming Cui, Yong Zhou, Huayong Yang

{"title":"Integrated Amphibious Distributed Acoustic Sensing for Seismic Monitoring in the Xinfengjiang Reservoir","authors":"Chao Li, Xingda Jiang, Min Xu, Haocai Huang, Zhuo Xiao, Yuejin Li, Zehui Lin, Hongxing Cui, Siyuan Cang, Xiaoming Cui, Yong Zhou, Huayong Yang","doi":"10.1785/0220240001","DOIUrl":"https://doi.org/10.1785/0220240001","url":null,"abstract":"\u0000 The rapidly advancing technology of distributed acoustic sensing (DAS) has profoundly impacted the field of underwater geophysics. Our study investigates the effectiveness of DAS in underwater geological stability monitoring, with a particular focus on microseismic monitoring in the Xinfengjiang reservoir. The 6.2 km long acquisition setup, covering both land and reservoir bottom, was verified using temporary shore-based short-period seismometers to ensure reliable data acquisition in various environments. Higher background noise was observed on the land section compared with the lakebed section during the day, whereas both sections exhibited similar noise levels at night. We confirmed that the DAS system was capable of detecting distant microseismic events, some of which were previously unreported. These detections exhibited temporal and phase consistency with neighboring seismometers. Comparison of signal-to-noise ratios indicates that the lakebed section demonstrates higher sensitivity. This system delivers cost-effective performance through natural settling, negating the requirement for costly embedding methods. Moreover, the DAS system identified “comet-like” small-scale signals on the lakebed that had eluded shore-based seismometers. This exemplifies the exceptional high-density and high-resolution capabilities of DAS technology in both aquatic and terrestrial environments. This study underscores the pivotal role of the DAS technology in conducting underwater microseismic monitoring, real-time seismic monitoring, seismic mechanism research, and earthquake hazard assessment.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"53 s43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141837731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning–Based Denoising Improves Receiver Function Imaging Using Dense Short-Period Teleseismic Data 基于深度学习的去噪改进了使用密集短周期远震数据的接收函数成像

Seismological Research Letters Pub Date : 2024-07-03 DOI: 10.1785/0220240017

Mingye Feng, Ling Chen, S. Wei, U. Muksin, Andrean V. H. Simanjuntak, Yukuan Chen, Chang Gong

{"title":"Deep Learning–Based Denoising Improves Receiver Function Imaging Using Dense Short-Period Teleseismic Data","authors":"Mingye Feng, Ling Chen, S. Wei, U. Muksin, Andrean V. H. Simanjuntak, Yukuan Chen, Chang Gong","doi":"10.1785/0220240017","DOIUrl":"https://doi.org/10.1785/0220240017","url":null,"abstract":"\u0000 Receiver function (RF) imaging using seismic data from dense short-period arrays has gained increasing importance in recent years in investigating fine-scale structures of the crust and uppermost mantle. A crucial step in such studies is to remove the instrument response (IR) to enhance teleseismic signals at ∼0.01 to 5 Hz, thereby simulating broadband records. However, this procedure also amplifies noise within the same frequency band. For weak signals, distinguishing them from noise is often challenging and in some cases is even impossible with traditional denoising methods such as filtering. To address this challenge, we develop a new convolutional neural network model, NodalWaden, using decades of high-quality global broadband teleseismic body waves for training. The broadband data exhibit the characteristics we target to achieve by removing the IR from the short-period records. The applicability of NodalWaden is justified by denoising the three-component short-period records of more than 18 months from 155 nodes deployed in northern Sumatra. We find that NodalWaden substantially improves the signal-to-noise ratio (SNR), upgrading ∼50% of the teleseismic data from the “very-low-SNR” (∼1) to “very-high-SNR” (>10) categories. RFs calculated from the denoised dataset show better separation of merged phases and noticeable enhancement of weak signals, resulting in improvement in the quality of structure imaging. In particular, a positive phase is consistently detected at ~2 s throughout the dataset and interpreted as the Conrad discontinuity, which is unresolvable in the original RFs. This denoising technique would be particularly useful for short-duration (e.g., one month) deployment with limited teleseismic data, both from the past and in the future.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":" 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seis-PnSn: A Global Million-Scale Benchmark Data Set of Pn and Sn Seismic Phases for Deep Learning Seis-PnSn：用于深度学习的全球百万级 Pn 和 Sn 地震相位基准数据集

Seismological Research Letters Pub Date : 2024-07-02 DOI: 10.1785/0220230379

Hua Kong, Zhuowei Xiao, Yan Lü, Juan Li

{"title":"Seis-PnSn: A Global Million-Scale Benchmark Data Set of Pn and Sn Seismic Phases for Deep Learning","authors":"Hua Kong, Zhuowei Xiao, Yan Lü, Juan Li","doi":"10.1785/0220230379","DOIUrl":"https://doi.org/10.1785/0220230379","url":null,"abstract":"\u0000 The seismic phases Pn and Sn play a crucial role in investigating the velocity and anisotropic characteristics of the uppermost mantle. However, manually annotating these phases can be time-intensive and prone to subjective interpretation. Consequently, the use of travel-time data for these seismic phases remains limited. Despite the potential of deep learning to address this challenge, the scarcity of extensive training data sets for Pn and Sn presents significant constraints. To address this challenge, our research compiled a global million-scale benchmark data set of Pn and Sn seismic phases, namely Seis–PnSn. The data set is derived from earthquake events with epicenter distances ranging from 1.8° to 18°. The high-quality travel-time data used in this study are all from the International Seismological Centre and span the period 2000 to 2019. The waveform data were sourced from data centers located in different regions of the world under the International Federation of Digital Seismograph Networks. By leveraging the unique attributes of this data set, we trained baseline models and explored the prevailing challenges in deep-learning-based Pn and Sn phase picking as the scope transitions from local to regional epicenter distances. Our results show that the performance of the model is considerably enhanced after training on the proposed data set. Our study is a significant complement to the data foundation for future data-driven Pn and Sn seismic phase-picking studies, which will contribute to enhancing our understanding of the uppermost mantle structure of Earth, for example, the seismic velocity, anisotropy, and attenuation characteristics.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"33 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Double-Difference Tomography with a Deep Learning–Based Phase Arrival Weighting Scheme and Its Application to the Anninghe–Xiaojiang Fault Zone 基于深度学习的相位到达加权方案的双差分断层成像及其在安宁河-小江断裂带中的应用

Seismological Research Letters Pub Date : 2024-07-02 DOI: 10.1785/0220230362

Ting Yang, L. Fang, Jianping Wu, Stephen Monna, Weimin Xu

{"title":"Double-Difference Tomography with a Deep Learning–Based Phase Arrival Weighting Scheme and Its Application to the Anninghe–Xiaojiang Fault Zone","authors":"Ting Yang, L. Fang, Jianping Wu, Stephen Monna, Weimin Xu","doi":"10.1785/0220230362","DOIUrl":"https://doi.org/10.1785/0220230362","url":null,"abstract":"\u0000 High-precision seismic phase arrivals are a prerequisite for building reliable velocity models with travel-time tomography. There has recently been a growing use of seismic phase arrival data obtained through deep learning techniques in travel-time tomography research. Nevertheless, a significant challenge that has emerged pertains to the assessment of the quality of these automatic arrivals. In this article, we used PhaseNet, a deep learning method, to automatically detect the arrival times of the P wave and S wave of 3086 seismic events recorded by dense seismic arrays, obtaining 87,553 high-quality arrivals. To evaluate the quality of the arrival times subsequently used for travel-time tomography inversion, we applied a weighting scheme that includes both detection probability value and signal-to-noise ratio. This new weighting scheme can effectively reduce the overall travel-time residual by 7%. The weighted data were then used in the double-difference tomography method to invert for the crustal velocity structure of the Anninghe–Xiaojiang fault zone. The resulting new model exhibits a lateral resolution of up to 0.25° and reveals velocity anomalies that exhibit a strong correlation with major geological features and block boundaries. Notably, the presence of low-VP and low-VS in the middle crust of the Ludian–Qiaojia seismic zone suggests the existence of hot and weak felsic rocks, as well as possible fluid presence beneath the seismogenic layer of this area. This study not only validates the practicality of using deep learning-based phase picking arrivals in travel-time tomography but also proposes a new weighting scheme to refine the tomographic velocity models.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"56 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian Merging of Earthquake Magnitudes Determined by Multiple Seismic Networks 贝叶斯法合并多个地震网络确定的地震震级

Seismological Research Letters Pub Date : 2024-07-02 DOI: 10.1785/0220230404

Zhengya Si, Jiancang Zhuang, Stefania Gentili, Changsheng Jiang, Weitao Wang

{"title":"A Bayesian Merging of Earthquake Magnitudes Determined by Multiple Seismic Networks","authors":"Zhengya Si, Jiancang Zhuang, Stefania Gentili, Changsheng Jiang, Weitao Wang","doi":"10.1785/0220230404","DOIUrl":"https://doi.org/10.1785/0220230404","url":null,"abstract":"\u0000 We introduce a Bayesian algorithm designed to integrate earthquake magnitudes of the same type reported by various seismic networks, aiming to create unified and standardized catalogs suitable for widespread use. The fundamental concept underpinning this algorithm is the utilization of the inherent consistency within each individual network’s magnitude determination process. Assuming that the magnitudes for an earthquake measured by all networks conform to a Gaussian distribution, with a linear function of the unknown true magnitude serving as its mean, we derive the posterior probability distribution of the true magnitude under four different assumptions for the prior distribution: the uninformative uniform distribution, the unbounded Gutenberg–Richter (GR) magnitude–frequency law, the GR magnitude–frequency relationship restricted by the detection rate, and the truncated GR law as priors. We assess the robustness of the method by a test on several synthetic catalogs and then use it to merge the catalogs compiled by five seismic networks in Italy. The results demonstrate that our proposed magnitude-merging algorithm effectively combines the catalogs, resulting in robust and unified data sets that are suitable for seismic hazard assessment and seismicity analysis.","PeriodicalId":508466,"journal":{"name":"Seismological Research Letters","volume":"20 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0