Won-Young Kim, Minyoung Seo, Jun Yong Park, Sangwoo Han, Young Oh Son, Younghee Kim
We identified the causative fault of the 2022 Goesan, Korea, earthquake sequence based on the precise relocation of the sequence that revealed a 0.8 km-long fault plane striking east-southeast–west-northwest. The fault plane encompasses the largest foreshock, the mainshock, and the majority of the aftershocks. The orientation of the fault plane is consistent with the left-lateral strike-slip motion along the east-southeast (106°) striking nodal plane of the focal mechanism. The Jogok fault system recently mapped in the source area runs through the mainshock epicenter with a consistent strike and left-lateral strike-slip motion, which suggests that it is the likely causative fault of the 2022 Mw 3.8 Goesan earthquake sequence. It is a rare case of assigning a causative fault for a small-sized (Mw 3.8) earthquake with some confidence in a typical stable continental region setting, albeit no surface break observed due to deep focal depth (~13 km) and the small size of the event. Aftershocks on the main fault plane, and on the adjacent subparallel fault patches seemed to be triggered by the increase in Coulomb stress caused by the mainshock. Two large aftershocks on the subparallel fault patches show slightly higher stress drops than the large foreshock and mainshock on the main fault plane, likely due to high frictional strength on those fault patches. Events of the 2022 Goesan earthquake sequence progressed rapidly in time and appear to be high stress-drop events compared with other earthquakes that occurred in other regions in Korea, probably due to the long quiescent period in the Goesan earthquake epicentral region.
{"title":"The 28 October 2022 Mw 3.8 Goesan Earthquake Sequence in Central Korea: Stress Drop, Aftershock Triggering, and Fault Interaction","authors":"Won-Young Kim, Minyoung Seo, Jun Yong Park, Sangwoo Han, Young Oh Son, Younghee Kim","doi":"10.1785/0120230078","DOIUrl":"https://doi.org/10.1785/0120230078","url":null,"abstract":"\u0000 We identified the causative fault of the 2022 Goesan, Korea, earthquake sequence based on the precise relocation of the sequence that revealed a 0.8 km-long fault plane striking east-southeast–west-northwest. The fault plane encompasses the largest foreshock, the mainshock, and the majority of the aftershocks. The orientation of the fault plane is consistent with the left-lateral strike-slip motion along the east-southeast (106°) striking nodal plane of the focal mechanism. The Jogok fault system recently mapped in the source area runs through the mainshock epicenter with a consistent strike and left-lateral strike-slip motion, which suggests that it is the likely causative fault of the 2022 Mw 3.8 Goesan earthquake sequence. It is a rare case of assigning a causative fault for a small-sized (Mw 3.8) earthquake with some confidence in a typical stable continental region setting, albeit no surface break observed due to deep focal depth (~13 km) and the small size of the event. Aftershocks on the main fault plane, and on the adjacent subparallel fault patches seemed to be triggered by the increase in Coulomb stress caused by the mainshock. Two large aftershocks on the subparallel fault patches show slightly higher stress drops than the large foreshock and mainshock on the main fault plane, likely due to high frictional strength on those fault patches. Events of the 2022 Goesan earthquake sequence progressed rapidly in time and appear to be high stress-drop events compared with other earthquakes that occurred in other regions in Korea, probably due to the long quiescent period in the Goesan earthquake epicentral region.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"31 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73232602","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}
We detected the mechanically coupled areas, or high stress rate patches, on the plate interface in the Kanto region, central Japan, by analyzing the Global Navigation Satellite Systems data. The estimated patches correspond well with the focal areas of past great earthquakes and slow-slip events (Mw∼6.5) occurring every ∼5 yr. Using one of the estimated patches, we created a model of a slow-slip event as a stress release with a recurrence interval of 5 yr. This synthetic can reproduce observed features of the slow-slip events such as the slip distribution and the magnitude. We use the strain-energy magnitude Mw0 defined by the minimum strain-energy release to quantify the magnitude. This is useful to compare slow-slip events with ordinary earthquakes in terms of the strain energy release, whereas the moment magnitude does not represent the difference of the energy release in this case. The strain-energy magnitude of the slow-slip event was Mw0 4.9, which was considerably smaller than the moment magnitude, because the smaller stress drop of the slow-slip event results in a smaller strain-energy release. Furthermore, by assuming that stress has accumulated at the other patches corresponding to the source region of past earthquakes since the occurrence of the last earthquakes, we obtain a model of the stress accumulation in 2023. We then create various rupture scenarios of great earthquakes as combinations of ruptures of the different patches. When two or three of the patches release the accumulated stress simultaneously, an interplate earthquake with Mw≥7.8 can occur in the Kanto region.
{"title":"Mechanically Coupled Areas on the Plate Interface in the Kanto Region, Central Japan, Generating Great Earthquakes and Slow-Slip Events","authors":"Tatsuhiko Saito, A. Noda","doi":"10.1785/0120230073","DOIUrl":"https://doi.org/10.1785/0120230073","url":null,"abstract":"\u0000 We detected the mechanically coupled areas, or high stress rate patches, on the plate interface in the Kanto region, central Japan, by analyzing the Global Navigation Satellite Systems data. The estimated patches correspond well with the focal areas of past great earthquakes and slow-slip events (Mw∼6.5) occurring every ∼5 yr. Using one of the estimated patches, we created a model of a slow-slip event as a stress release with a recurrence interval of 5 yr. This synthetic can reproduce observed features of the slow-slip events such as the slip distribution and the magnitude. We use the strain-energy magnitude Mw0 defined by the minimum strain-energy release to quantify the magnitude. This is useful to compare slow-slip events with ordinary earthquakes in terms of the strain energy release, whereas the moment magnitude does not represent the difference of the energy release in this case. The strain-energy magnitude of the slow-slip event was Mw0 4.9, which was considerably smaller than the moment magnitude, because the smaller stress drop of the slow-slip event results in a smaller strain-energy release. Furthermore, by assuming that stress has accumulated at the other patches corresponding to the source region of past earthquakes since the occurrence of the last earthquakes, we obtain a model of the stress accumulation in 2023. We then create various rupture scenarios of great earthquakes as combinations of ruptures of the different patches. When two or three of the patches release the accumulated stress simultaneously, an interplate earthquake with Mw≥7.8 can occur in the Kanto region.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"42 6 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80668503","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 hidden Haicheng fault system is an earthquake-prone zone on the Liaodong Peninsula, China. Its seismogenic structure is still unclear and needs further study. In this study, we used the differential evolution algorithm to invert the waveform data of the 2008 ML 4.8 Haicheng earthquake and the 2012 ML 4.8 Gaizhou earthquake and obtained an updated 1D crustal velocity model. The model reveals a low-velocity zone with a depth of 18–24 km below the Haicheng fault zone. Based on the velocity model, we used the arrival-time data to accurately locate the earthquakes that occurred in the Haicheng area from 2008 to 2018. The relocated earthquakes show that the Az 300°-trending Haicheng fault consists of two segments spaced about 2 km apart, namely the western and the eastern segments. They are about 12 and 22 km long and inclined to the northeast with dips of 70° and 80°, respectively. These seismogenic structures of the Haicheng fault zone are important for assessing the future seismic risk in the region.
{"title":"Seismogenic Structure of the Hidden Haicheng Fault System in China Revealed by Seismic Observations from 2008 to 2018","authors":"Pengda Zhang, Z. Dai, Shi-King Yang, X. Zha","doi":"10.1785/0120230046","DOIUrl":"https://doi.org/10.1785/0120230046","url":null,"abstract":"\u0000 The hidden Haicheng fault system is an earthquake-prone zone on the Liaodong Peninsula, China. Its seismogenic structure is still unclear and needs further study. In this study, we used the differential evolution algorithm to invert the waveform data of the 2008 ML 4.8 Haicheng earthquake and the 2012 ML 4.8 Gaizhou earthquake and obtained an updated 1D crustal velocity model. The model reveals a low-velocity zone with a depth of 18–24 km below the Haicheng fault zone. Based on the velocity model, we used the arrival-time data to accurately locate the earthquakes that occurred in the Haicheng area from 2008 to 2018. The relocated earthquakes show that the Az 300°-trending Haicheng fault consists of two segments spaced about 2 km apart, namely the western and the eastern segments. They are about 12 and 22 km long and inclined to the northeast with dips of 70° and 80°, respectively. These seismogenic structures of the Haicheng fault zone are important for assessing the future seismic risk in the region.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"20 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74025690","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}
Array processing is an integral part of automatic seismic event detection pipelines for measuring apparent velocity and backazimuth of seismic arrivals. Both quantities are usually measured under the plane-wave assumption, and are essential to classify the phase type and to determine the direction toward the event epicenter. However, structural inhomogeneities can lead to deviations from the plane-wave model, which must be taken into account for phase classification and back-azimuth estimation. We suggest a combined classification and regression neural network, which we call ArrayNet, to determine the phase type and backazimuth directly from the arrival-time differences between all combinations of stations of a given seismic array without assuming a plane-wave model. ArrayNet is trained using regional P- and S-wave arrivals of over 30,000 seismic events from reviewed regional bulletins in northern Europe from the past three decades. ArrayNet models are generated and trained for each of the ARCES, FINES, and SPITS seismic arrays. We observe excellent performance for the seismic phase classification (up to 99% accuracy), and the derived back-azimuth residuals are significantly improved in comparison with traditional array processing results using the plane-wave assumption. The SPITS array in Svalbard exhibits particular issues when it comes to array processing in the form of high apparent seismic velocities and a multitude of frost quake signals inside the array, and we show how our new approach better handles these obstacles. Furthermore, we demonstrate the performance of ArrayNet on 20 months of continuous phase detections from the ARCES array and investigate the results for a selection of regional seismic events of interest. Our results demonstrate that automatic event detection at seismic arrays can be further enhanced using a machine learning approach that takes advantage of the unique array data recorded at these stations.
{"title":"ArrayNet: A Combined Seismic Phase Classification and Back-Azimuth Regression Neural Network for Array Processing Pipelines","authors":"A. Köhler, E. B. Myklebust","doi":"10.1785/0120230056","DOIUrl":"https://doi.org/10.1785/0120230056","url":null,"abstract":"\u0000 Array processing is an integral part of automatic seismic event detection pipelines for measuring apparent velocity and backazimuth of seismic arrivals. Both quantities are usually measured under the plane-wave assumption, and are essential to classify the phase type and to determine the direction toward the event epicenter. However, structural inhomogeneities can lead to deviations from the plane-wave model, which must be taken into account for phase classification and back-azimuth estimation. We suggest a combined classification and regression neural network, which we call ArrayNet, to determine the phase type and backazimuth directly from the arrival-time differences between all combinations of stations of a given seismic array without assuming a plane-wave model. ArrayNet is trained using regional P- and S-wave arrivals of over 30,000 seismic events from reviewed regional bulletins in northern Europe from the past three decades. ArrayNet models are generated and trained for each of the ARCES, FINES, and SPITS seismic arrays. We observe excellent performance for the seismic phase classification (up to 99% accuracy), and the derived back-azimuth residuals are significantly improved in comparison with traditional array processing results using the plane-wave assumption. The SPITS array in Svalbard exhibits particular issues when it comes to array processing in the form of high apparent seismic velocities and a multitude of frost quake signals inside the array, and we show how our new approach better handles these obstacles. Furthermore, we demonstrate the performance of ArrayNet on 20 months of continuous phase detections from the ARCES array and investigate the results for a selection of regional seismic events of interest. Our results demonstrate that automatic event detection at seismic arrays can be further enhanced using a machine learning approach that takes advantage of the unique array data recorded at these stations.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"68 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89816653","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}
Fluid injection associated with oil field operations can induce earthquakes through perturbation to the balance between fault strength and tectonic stress. Induced seismicity generally does not respond immediately to changes in injection due to time-delayed diffusion of pressure and heterogeneous prestress conditions on seismogenic faults. After exploitation for over a century without significant seismicity, the Midland basin experienced a rapid increase in activity since mid-2020, including events as large as an ML 5.2 with many felt throughout the Midland and Odessa metropolitan area. In response to societal and industry concerns, the Texas Railroad Commission established Seismic Response Areas around Stanton and Gardendale, to address the possibility that deep wastewater disposal was triggering earthquakes. In this study, we present a detailed earthquake catalog covering 2020 and 2021 for the Midland basin derived from regional and private seismic network data. Hypocenters are computed using a velocity model calibrated with sonic logs. We compare the location and timing of seismicity with development, production, and disposal operations. Seismicity predominantly occurs within the Precambrian basement deeper than wastewater disposal and oil production. Faults delineated by relocated seismicity are optimally oriented for failure in the tectonic stress field, and their focal mechanisms are consistent with the inferred fault geometries. Neither the onset of seismicity nor the occurrence of large events correlates directly in time with hydraulic fracturing or changes in deep injection. Rather, faults appear to activate in response to cumulative deep disposal. However, we suspect that both pore pressure diffusion from deep disposal and remote poroelastic stress changes associated with fluid injection and extraction influence the recent increase in seismicity in the Midland basin. In either case, the regulation of deep wastewater injection in the seismic response areas has the potential to reduce the seismic hazard in the Midland basin.
{"title":"Reactivation of Precambrian Faults by Deep Wastewater Injection in Midland Basin, Texas, and Performance Evaluation of Seismic Response Areas","authors":"J. Woo, W. Ellsworth","doi":"10.1785/0120230086","DOIUrl":"https://doi.org/10.1785/0120230086","url":null,"abstract":"\u0000 Fluid injection associated with oil field operations can induce earthquakes through perturbation to the balance between fault strength and tectonic stress. Induced seismicity generally does not respond immediately to changes in injection due to time-delayed diffusion of pressure and heterogeneous prestress conditions on seismogenic faults. After exploitation for over a century without significant seismicity, the Midland basin experienced a rapid increase in activity since mid-2020, including events as large as an ML 5.2 with many felt throughout the Midland and Odessa metropolitan area. In response to societal and industry concerns, the Texas Railroad Commission established Seismic Response Areas around Stanton and Gardendale, to address the possibility that deep wastewater disposal was triggering earthquakes. In this study, we present a detailed earthquake catalog covering 2020 and 2021 for the Midland basin derived from regional and private seismic network data. Hypocenters are computed using a velocity model calibrated with sonic logs. We compare the location and timing of seismicity with development, production, and disposal operations. Seismicity predominantly occurs within the Precambrian basement deeper than wastewater disposal and oil production. Faults delineated by relocated seismicity are optimally oriented for failure in the tectonic stress field, and their focal mechanisms are consistent with the inferred fault geometries. Neither the onset of seismicity nor the occurrence of large events correlates directly in time with hydraulic fracturing or changes in deep injection. Rather, faults appear to activate in response to cumulative deep disposal. However, we suspect that both pore pressure diffusion from deep disposal and remote poroelastic stress changes associated with fluid injection and extraction influence the recent increase in seismicity in the Midland basin. In either case, the regulation of deep wastewater injection in the seismic response areas has the potential to reduce the seismic hazard in the Midland basin.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"25 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73168573","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}
Two sets of predictive models are developed based on the machine learning (ML) and general orthogonal regression (GOR) approaches for predicting the seismic source parameters including rupture width, rupture length, rupture area, and two slip parameters (i.e., the average and maximum slips of rupture surface). The predictive models are developed based on a compiled catalog consisting of 1190 sets of estimated source parameters. First, the Light Gradient Boosting Machine (LightGBM), which is a gradient boosting framework that uses tree-based learning algorithms, is utilized to develop the ML-based predictive models by employing five predictor variables consisting of moment magnitude (Mw), hypocenter depth, dip angle, fault-type, and subduction indicators. It is found that the developed ML-based models exhibit good performance in terms of predictive efficiency and generalization. Second, multiple source-scaling models are developed for predicting the source parameters based on the GOR approach, in which each functional form has one predictor variable only, that is, Mw. The performance of the GOR-based models is compared with existing source-scaling relationships. Both sets of the models developed are applicable in estimating the five source parameters in earthquake engineering-related applications.
{"title":"Predictive Models for Seismic Source Parameters Based on Machine Learning and General Orthogonal Regression Approaches","authors":"Qing-Yang Liu, Dianqing Li, Xiao-Song Tang, W. Du","doi":"10.1785/0120230069","DOIUrl":"https://doi.org/10.1785/0120230069","url":null,"abstract":"\u0000 Two sets of predictive models are developed based on the machine learning (ML) and general orthogonal regression (GOR) approaches for predicting the seismic source parameters including rupture width, rupture length, rupture area, and two slip parameters (i.e., the average and maximum slips of rupture surface). The predictive models are developed based on a compiled catalog consisting of 1190 sets of estimated source parameters. First, the Light Gradient Boosting Machine (LightGBM), which is a gradient boosting framework that uses tree-based learning algorithms, is utilized to develop the ML-based predictive models by employing five predictor variables consisting of moment magnitude (Mw), hypocenter depth, dip angle, fault-type, and subduction indicators. It is found that the developed ML-based models exhibit good performance in terms of predictive efficiency and generalization. Second, multiple source-scaling models are developed for predicting the source parameters based on the GOR approach, in which each functional form has one predictor variable only, that is, Mw. The performance of the GOR-based models is compared with existing source-scaling relationships. Both sets of the models developed are applicable in estimating the five source parameters in earthquake engineering-related applications.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"52 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90619985","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}
Kappa (the high-frequency spectral decay slope at near-source distances; often referred to as κ0) is determined at 25 seismograph stations in Eastern Canada using broadband ground-motion modeling approaches. The database comprises Fourier spectra (effective amplitude spectrum for the horizontal component and the vertical component, 0.8–40 Hz) computed from 3318 earthquakes of moment magnitude M 1.5–5 recorded on stations within 150 km. Average kappa values for bedrock sites, having shear-wave velocities from 850 to 2400 m/s, are highly variable, ranging from −29 to +21 ms (horizontal) and −28 to +11 ms (vertical), but appear on average to be near-zero. The values obtained are sensitive to methodology, especially the necessary adjustments to the spectra to account for site amplification effects. Kappa values do not appear to correlate well with site parameters such as rock shear-wave velocity, average shear-wave velocity in the upper 30 m, primary wave velocity, site class, type and age of rock, or instrument housing. This lack of correlation may reflect the noted sensitivities to methodological factors. We conclude that kappa values in rock environments are not reliably estimated from such proxies and should be determined from recorded ground motions at a given location. On average, there is little evidence of significant high-frequency attenuation on rock sites beyond that already accounted for in ground-motion modeling by the empirical parameterization of regional Q-related path effects.
{"title":"Uncertainties in Broadband Determination of the High-Frequency Spectral Decay, Kappa, in Eastern Canada","authors":"Samantha M. Palmer, G. Atkinson","doi":"10.1785/0120220043","DOIUrl":"https://doi.org/10.1785/0120220043","url":null,"abstract":"\u0000 Kappa (the high-frequency spectral decay slope at near-source distances; often referred to as κ0) is determined at 25 seismograph stations in Eastern Canada using broadband ground-motion modeling approaches. The database comprises Fourier spectra (effective amplitude spectrum for the horizontal component and the vertical component, 0.8–40 Hz) computed from 3318 earthquakes of moment magnitude M 1.5–5 recorded on stations within 150 km. Average kappa values for bedrock sites, having shear-wave velocities from 850 to 2400 m/s, are highly variable, ranging from −29 to +21 ms (horizontal) and −28 to +11 ms (vertical), but appear on average to be near-zero. The values obtained are sensitive to methodology, especially the necessary adjustments to the spectra to account for site amplification effects. Kappa values do not appear to correlate well with site parameters such as rock shear-wave velocity, average shear-wave velocity in the upper 30 m, primary wave velocity, site class, type and age of rock, or instrument housing. This lack of correlation may reflect the noted sensitivities to methodological factors. We conclude that kappa values in rock environments are not reliably estimated from such proxies and should be determined from recorded ground motions at a given location. On average, there is little evidence of significant high-frequency attenuation on rock sites beyond that already accounted for in ground-motion modeling by the empirical parameterization of regional Q-related path effects.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"2 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83025801","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}
How does an earthquake affect buried pipeline networks? It is well known that the seismic performance of buried pipelines depends on ground failures (GFs) as well as strong ground shaking (SGS), but it is unclear how the various types of earthquake hazards should be collectively combined, as existing methodologies tend to examine each of the earthquake hazards separately. In this article, we develop a probability-based methodology to consistently combine SGS with three types of GF (surface faulting, liquefaction, and landslide) for forecasting seismic damage in buried pipeline networks from a given earthquake rupture scenario. Using a gas transmission pipeline example, we illustrate how the proposed methodology enables others (e.g., researchers, pipeline operators who manage distribution lines, and consultants) to modularly combine various models such as those for estimating probability of GF, permanent ground displacements, and pipeline fragility. Finally, we compare the proposed methodology against the Hazus methodology to explore implications from considering each hazard one at a time.
{"title":"A Methodology to Combine Shaking and Ground Failure Models for Forecasting Seismic Damage to Buried Pipeline Networks","authors":"N. S. Kwong, K. Jaiswal","doi":"10.1785/0120220132","DOIUrl":"https://doi.org/10.1785/0120220132","url":null,"abstract":"\u0000 How does an earthquake affect buried pipeline networks? It is well known that the seismic performance of buried pipelines depends on ground failures (GFs) as well as strong ground shaking (SGS), but it is unclear how the various types of earthquake hazards should be collectively combined, as existing methodologies tend to examine each of the earthquake hazards separately. In this article, we develop a probability-based methodology to consistently combine SGS with three types of GF (surface faulting, liquefaction, and landslide) for forecasting seismic damage in buried pipeline networks from a given earthquake rupture scenario. Using a gas transmission pipeline example, we illustrate how the proposed methodology enables others (e.g., researchers, pipeline operators who manage distribution lines, and consultants) to modularly combine various models such as those for estimating probability of GF, permanent ground displacements, and pipeline fragility. Finally, we compare the proposed methodology against the Hazus methodology to explore implications from considering each hazard one at a time.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"6 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76122283","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}
Site-specific seismic hazard studies require the knowledge of the shear-wave velocity VS and the high-frequency site attenuation parameter κ0 at the reference rock level at depth. The latter one (called κ0,ref) is often not available and hard to derive. In this study, we make use of the KiK-net database in Japan that consists of surface and colocated downhole sensors. We select 175 sites where the bottom sensor is deployed at rock or hard-rock conditions resulting in a database with many recordings at VS≥1500 m/s. This allows us to tackle two questions: first, is it possible to derive κ0,ref from surface recordings? Second, does κ0 reach an asymptotic level at high VS that could be used as a κ0,ref in site-specific seismic hazard studies? Our results show that measures of κ0 derived from S and coda waves are equivalent. Thus, it is not possible to obtain κ0,ref from surface recordings using coda waves. On the other hand, S-wave measurements of κ0 from surface rock sites are close to κ0,ref if VS≥760 m/s or if the sedimentary cover is thin. The values of κ0 decrease with increasing VS and reach an asymptotic value. The scatter in the so obtained κ0,ref is high, but it can be reduced by selecting subregions with similar geological conditions. Finally, we observe that borehole and surface κ0 are correlated, and that the variability of κ0,ref is only slightly reduced compared to κ0 at the surface. Although we cannot exclude any influence of source effects, our findings indicate that κ0,ref has to be considered as a deep site parameter.
{"title":"Hard-Rock κ0 at KiK-Net Sites in Japan","authors":"A. Haendel, M. Pilz, F. Cotton","doi":"10.1785/0120220246","DOIUrl":"https://doi.org/10.1785/0120220246","url":null,"abstract":"\u0000 Site-specific seismic hazard studies require the knowledge of the shear-wave velocity VS and the high-frequency site attenuation parameter κ0 at the reference rock level at depth. The latter one (called κ0,ref) is often not available and hard to derive. In this study, we make use of the KiK-net database in Japan that consists of surface and colocated downhole sensors. We select 175 sites where the bottom sensor is deployed at rock or hard-rock conditions resulting in a database with many recordings at VS≥1500 m/s. This allows us to tackle two questions: first, is it possible to derive κ0,ref from surface recordings? Second, does κ0 reach an asymptotic level at high VS that could be used as a κ0,ref in site-specific seismic hazard studies? Our results show that measures of κ0 derived from S and coda waves are equivalent. Thus, it is not possible to obtain κ0,ref from surface recordings using coda waves. On the other hand, S-wave measurements of κ0 from surface rock sites are close to κ0,ref if VS≥760 m/s or if the sedimentary cover is thin. The values of κ0 decrease with increasing VS and reach an asymptotic value. The scatter in the so obtained κ0,ref is high, but it can be reduced by selecting subregions with similar geological conditions. Finally, we observe that borehole and surface κ0 are correlated, and that the variability of κ0,ref is only slightly reduced compared to κ0 at the surface. Although we cannot exclude any influence of source effects, our findings indicate that κ0,ref has to be considered as a deep site parameter.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"41 1 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78217265","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}
Magnitudes are common and important measures for the size of seismic events. The International Data Centre (IDC) of the Comprehensive Nuclear-Test Ban Treaty Organization estimates an event magnitude by averaging the magnitudes calculated by individual stations that detected the event, excluding outliers. This approach assumes that all station magnitudes have the same error level and are unbiased, namely, they have no systematic errors. We show that the body-wave and surface-wave magnitudes published in the Reviewed Event Bulletin (REB) of the IDC are inconsistent with these assumptions. We thus consider a model where each station has an unknown bias and error level. Given a large collection of reported event magnitudes by a network of monitoring stations, we propose a novel approach to estimate each individual station’s bias and error level. From a statistical perspective, this is a challenging problem involving a huge number of variables, because in addition to the stations’ biases and error levels, the event magnitudes are also unknown. Our approach is based on analyzing differences between reported magnitude values at pairs of stations, which cancels out the unknown event magnitudes and allows us to derive a simple and computationally efficient algorithm. We use the estimated station biases as station correction terms and the estimated error levels to compute weights for event magnitude estimation. Using a large data set from the REB with millions of reported station magnitudes, we show that our approach yields more consistent station and event magnitudes.
{"title":"A Statistical Approach to Estimate Seismic Monitoring Stations’ Biases and Error Levels","authors":"Y. Radzyner, M. Galun, B. Nadler","doi":"10.1785/0120230009","DOIUrl":"https://doi.org/10.1785/0120230009","url":null,"abstract":"\u0000 Magnitudes are common and important measures for the size of seismic events. The International Data Centre (IDC) of the Comprehensive Nuclear-Test Ban Treaty Organization estimates an event magnitude by averaging the magnitudes calculated by individual stations that detected the event, excluding outliers. This approach assumes that all station magnitudes have the same error level and are unbiased, namely, they have no systematic errors. We show that the body-wave and surface-wave magnitudes published in the Reviewed Event Bulletin (REB) of the IDC are inconsistent with these assumptions. We thus consider a model where each station has an unknown bias and error level. Given a large collection of reported event magnitudes by a network of monitoring stations, we propose a novel approach to estimate each individual station’s bias and error level. From a statistical perspective, this is a challenging problem involving a huge number of variables, because in addition to the stations’ biases and error levels, the event magnitudes are also unknown. Our approach is based on analyzing differences between reported magnitude values at pairs of stations, which cancels out the unknown event magnitudes and allows us to derive a simple and computationally efficient algorithm. We use the estimated station biases as station correction terms and the estimated error levels to compute weights for event magnitude estimation. Using a large data set from the REB with millions of reported station magnitudes, we show that our approach yields more consistent station and event magnitudes.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"276 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86734158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：