An accurate relocation of the 2017 Ms 7.0 Sichuan Jiuzhaigou earthquake sequence and the seismicity analysis

2020 ◽  
Author(s):  
Xiangwei Yu ◽  
Qian Song ◽  
Shanquan Deng
Keyword(s):  
P Wave ◽  
Depth Range ◽  
Jiuzhaigou Earthquake ◽  
Waveform Data ◽  
Arrival Times ◽  
Location Method ◽  
Seismic Waveform ◽  
Waveform Cross Correlation ◽  
Seismic Location ◽  
Seismicity Analysis

<p>The 2017 Ms 7.0 Sichuan Jiuzhaigou earthquake occurred at the intersection of the Tazang, Minjiang, and Huya faults on the eastern margin of the Tibetan Plateau. Since it occurred on an unmarked blind fault, it is still a controversial issue whether the fault, which triggered the earthquake, was the extension of the East Kunlun fault or the northern branch of the Huya fault. Therefore, the accurate source location is of great significance for studying the deep distribution of seismogenic faults and seismicity analysis.</p><p>We have not only collected seismic phase arrival data recorded by 24 permanent stations and 6 temporary stations, but also picked up the seismic waveform data recorded by partial permanent stations in this study. Using absolute seismic location method and relative seismic location method, we relocated the earthquake events with magnitude greater than or equal to 1.0 occurred in the Jiuzhaigou area from August to December 2017. In order to ensure reliable data quality, we selected 23422 P-wave absolute arrival times, 24734 S-wave absolute arrival times and 124519 high quality P-waveform cross correlation data of 3449 earthquake events for relocation research.</p><p>The mean value of root mean square residuals of travel time of all earthquakes decrease from 0.21s to 0.08s after relocation. The average location errors in the E-W, N-S, and vertical directions are 0.11km, 0.12km, and 0.16km, respectively. Ninety-nine percent of the earthquake events are distributed in the depth range of 1-25 km, and the dominant distribution range is 5-15 km. The result shows that the earthquakes are distributed along the strike of northwest and southeast, and the Jiuzhaigou mainshock divided these events into two clusters: northwest and southeast. From the parallel strike section, we conclude that the depth of the northwest seismic cluster is shallow with the depth range of 2-15 km, and the depth of the southeast seismic cluster is deeper with the depth range of 6-18 km. Moreover, the number of aftershocks in the northwest cluster is greater than that in the southeast cluster, but after an M 4.9 aftershock occurred in the northwest cluster on the ninety-first day after the Jiuzhaigou mainshock, the number of aftershocks in the northwest cluster began to decrease. The result provides a basis for studying the seismogenic background and seismicity of the Jiuzhaigou earthquake.</p>

scholarly journals An automatically generated high-resolution earthquake catalogue for the 2016–2017 Central Italy seismic sequence, including P and S phase arrival times

2020 ◽  
Author(s):  
D Spallarossa ◽  
M Cattaneo ◽  
D Scafidi ◽  
M Michele ◽  
L Chiaraluce ◽  
...  
Keyword(s):  
Real Time ◽  
Central Italy ◽  
Earthquake Catalogue ◽  
Hazard Evaluation ◽  
Depth Range ◽  
Data Set ◽  
Epicentral Area ◽  
Earthquake Parameters ◽  
Waveform Data ◽  
Arrival Times

Summary The 2016–17 central Italy earthquake sequence began with the first mainshock near the town of Amatrice on August 24 (MW 6.0), and was followed by two subsequent large events near Visso on October 26 (MW 5.9) and Norcia on October 30 (MW 6.5), plus a cluster of 4 events with MW > 5.0 within few hours on January 18, 2017. The affected area had been monitored before the sequence started by the permanent Italian National Seismic Network (RSNC), and was enhanced during the sequence by temporary stations deployed by the National Institute of Geophysics and Volcanology and the British Geological Survey. By the middle of September, there was a dense network of 155 stations, with a mean separation in the epicentral area of 6–10 km, comparable to the most likely earthquake depth range in the region. This network configuration was kept stable for an entire year, producing 2.5 TB of continuous waveform recordings. Here we describe how this data was used to develop a large and comprehensive earthquake catalogue using the Complete Automatic Seismic Processor (CASP) procedure. This procedure detected more than 450,000 events in the year following the first mainshock, and determined their phase arrival times through an advanced picker engine (RSNI-Picker2), producing a set of about 7 million P- and 10 million S-wave arrival times. These were then used to locate the events using a non-linear location (NLL) algorithm, a 1D velocity model calibrated for the area, and station corrections and then to compute their local magnitudes (ML). The procedure was validated by comparison of the derived data for phase picks and earthquake parameters with a handpicked reference catalogue (hereinafter referred to as ‘RefCat’). The automated procedure takes less than 12 hours on an Intel Core-i7 workstation to analyse the primary waveform data and to detect and locate 3000 events on the most seismically active day of the sequence. This proves the concept that the CASP algorithm can provide effectively real-time data for input into daily operational earthquake forecasts, The results show that there have been significant improvements compared to RefCat obtained in the same period using manual phase picks. The number of detected and located events is higher (from 84,401 to 450,000), the magnitude of completeness is lower (from ML 1.4 to 0.6), and also the number of phase picks is greater with an average number of 72 picked arrival for a ML = 1.4 compared with 30 phases for RefCat using manual phase picking. These propagate into formal uncertainties of ± 0.9km in epicentral location and ± 1.5km in depth for the enhanced catalogue for the vast majority of the events. Together, these provide a significant improvement in the resolution of fine structures such as local planar structures and clusters, in particular the identification of shallow events occurring in parts of the crust previously thought to be inactive. The lower completeness magnitude provides a rich data set for development and testing of analysis techniques of seismic sequences evolution, including real-time, operational monitoring of b-value, time-dependent hazard evaluation and aftershock forecasting.

scholarly journals P-wave first-motion polarity determination of waveform data in western Japan using deep learning

2019 ◽  
Vol 71 (1) ◽  
Author(s):  
Shota Hara ◽  
Yukitoshi Fukahata ◽  
Yoshihisa Iio
Keyword(s):  
Training Data ◽  
P Wave ◽  
Data Sets ◽  
Waveform Data ◽  
Arrival Times ◽  
Distribution Maps ◽  
Seismic Waveforms ◽  
Polarity Determination ◽  
Western Japan ◽  
First Motion

AbstractP-wave first-motion polarity is the most useful information in determining the focal mechanisms of earthquakes, particularly for smaller earthquakes. Algorithms have been developed to automatically determine P-wave first-motion polarity, but the performance level of the conventional algorithms remains lower than that of human experts. In this study, we develop a model of the convolutional neural networks (CNNs) to determine the P-wave first-motion polarity of observed seismic waveforms under the condition that P-wave arrival times determined by human experts are known in advance. In training and testing the CNN model, we use about 130 thousand 250 Hz and about 40 thousand 100 Hz waveform data observed in the San-in and the northern Kinki regions, western Japan, where three to four times larger number of waveform data were obtained in the former region than in the latter. First, we train the CNN models using 250 Hz and 100 Hz waveform data, respectively, from both regions. The accuracies of the CNN models are 97.9% for the 250 Hz data and 95.4% for the 100 Hz data. Next, to examine the regional dependence, we divide the waveform data sets according to the observation region, and then we train new CNN models with the data from one region and test them using the data from the other region. We find that the accuracy is generally high ($${ \gtrsim }$$≳ 95%) and the regional dependence is within about 2%. This suggests that there is almost no need to retrain the CNN model by regions. We also find that the accuracy is significantly lower when the number of training data is less than 10 thousand, and that the performance of the CNN models is a few percentage points higher when using 250 Hz data compared to 100 Hz data. Distribution maps, on which polarities determined by human experts and the CNN models are plotted, suggest that the performance of the CNN models is better than that of human experts.

Crustal velocity structure beneath the NW Dinarides from 1-D hypocenter-velocity inversion

2021 ◽  
Author(s):  
Gregor Rajh ◽  
Josip Stipčević ◽  
Mladen Živčić ◽  
Marijan Herak ◽  
Andrej Gosar
Keyword(s):  
Velocity Structure ◽  
Velocity Model ◽  
P Wave ◽  
Depth Range ◽  
Velocity Inversion ◽  
Arrival Times ◽  
Simultaneous Inversion ◽  
Velocity Modeling ◽  
Velocity Models ◽  
Station Corrections

<p>The investigated area of the NW Dinarides is bordered by the Adriatic foreland, the Southern Alps, and the Pannonian basin at the NE corner of the Adriatic Sea. Its complex crustal structure is the result of interactions among different tectonic units. Despite numerous seismic studies taking place in this region, there still exists a need for a detailed, smaller scale study focusing mainly on the brittle part of the Earth's crust. Therefore, we decided to investigate the velocity structure of the crust using concepts of local earthquake tomography (LET) and minimum 1-D velocity model. Here, we present the results of the 1-D velocity modeling and the catalogue of the relocated seismicity. A minimum 1-D velocity model is computed by simultaneous inversion for hypocentral and velocity parameters together with seismic station corrections and represents the best fit to the observed arrival times.</p><p>We used 15,579 routinely picked P wave arrival times from 631 well-located earthquakes that occurred in Slovenia and in its immediate surroundings (mainly NW Croatia). Various initial 1-D velocity models, differing in velocity and layering, were used as input for velocity inversion in the VELEST program. We also varied several inversion parameters during the inversion runs. Most of the computed 1-D velocity models converged to a stable solution in the depth range between 0 and 25 km. We evaluated the inversion results using rigorous testing procedures and selected two best performing velocity models. Each of these models will be used independently as the initial model in the simultaneous hypocenter-velocity inversion for a 3-D velocity structure in LET. Based on the results of the 1-D velocity modeling, seismicity distribution, and tectonics, we divided the study area into three parts, redefined the earthquake-station geometry, and performed the inversion for each part separately. This way, we gained a better insight into the shallow velocity structure of each subregion and were able to demonstrate the differences among them.</p><p>Besides general structural implications and a potential to improve the results of LET, the new 1-D velocity models along with station corrections can also be used in fast routine earthquake location and to detect systematic travel time errors in seismological bulletins, as already shown by some studies using similar methods.</p>

Temporal stability of P-velocity anisotropy before earthquakes in central California

1977 ◽  
Vol 67 (4) ◽  
pp. 1075-1090 ◽  
Author(s):  
J. Alan Steppe ◽  
William H. Bakun ◽  
Charles G. Bufe
Keyword(s):  
Travel Time ◽  
Source Region ◽  
Temporal Stability ◽  
Focal Depth ◽  
Temporal Variations ◽  
P Wave ◽  
Depth Range ◽  
Arrival Times ◽  
Central California ◽  
Velocity Anisotropy

Abstract An examination of P-wave travel-time residuals from small earthquakes (source events) located near three larger earthquakes (4 ≦ M ≦ 5) that occurred on the San Andreas fault, near Bear Valley in central California, shows no temporal variations in the residuals extending over broad azimuth ranges (Δφ > ∼40°). Such variations could have resulted from changes in horizontal velocity anisotropy precursory to the larger events. The examination also shows (1) numerous azimuthal variations in the residuals within narrow azimuth bands (Δφ < ∼30°), apparently due to spatial heterogeneity of crustal velocity, (2) a dependence of residual on magnitude for a few stations but not for most stations, and (3) trends of residual versus source event focal depth for about one-third of the 75 source region-station pairs examined. Residuals in these cases typically change by 0.1 sec, and occasionally by 0.2 to 0.3 sec, over the 2- to 12-km focal depth range sampled. The trends vary from station to station in a complex manner. The assumption that traveltime changes are reflected in the residuals is tested by modifying arrival times from some source events according to assumed forms for the travel-time change, relocating those events, and comparing the resulting residuals with those from the unmodified data.

First-arrival picking with a U-net convolutional network

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. U45-U57 ◽  
Author(s):  
Lianlian Hu ◽  
Xiaodong Zheng ◽  
Yanting Duan ◽  
Xinfei Yan ◽  
Ying Hu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Synthetic Data ◽  
Training Data ◽  
Convolutional Network ◽  
Data Set ◽  
Waveform Data ◽  
Near Surface ◽  
Arrival Times ◽  
Seismic Waveform ◽  
First Arrival

In exploration geophysics, the first arrivals on data acquired under complicated near-surface conditions are often characterized by significant static corrections, weak energy, low signal-to-noise ratio, and dramatic phase change, and they are difficult to pick accurately with traditional automatic procedures. We have approached this problem by using a U-shaped fully convolutional network (U-net) to first-arrival picking, which is formulated as a binary segmentation problem. U-net has the ability to recognize inherent patterns of the first arrivals by combining attributes of arrivals in space and time on data of varying quality. An effective workflow based on U-net is presented for fast and accurate picking. A set of seismic waveform data and their corresponding first-arrival times are used to train the network in a supervised learning approach, then the trained model is used to detect the first arrivals for other seismic data. Our method is applied on one synthetic data set and three field data sets of low quality to identify the first arrivals. Results indicate that U-net only needs a few annotated samples for learning and is able to efficiently detect first-arrival times with high precision on complicated seismic data from a large survey. With the increasing training data of various first arrivals, a trained U-net has the potential to directly identify the first arrivals on new seismic data.

Azimuthal anisotropy from OBS observations in Mahanadi offshore, India

2013 ◽  
Vol 1 (2) ◽  
pp. T187-T198 ◽  
Author(s):  
Nittala Satyavani ◽  
Mrinal K. Sen ◽  
Maheswar Ojha ◽  
Kalachand Sain
Keyword(s):  
Gas Hydrate ◽  
Seismic Anisotropy ◽  
Azimuthal Anisotropy ◽  
P Wave ◽  
Seismic Survey ◽  
Ocean Bottom Seismometer ◽  
Waveform Data ◽  
Seismic Waveform ◽  
Seismic Image ◽  
Obs Data

We have carried out an ocean bottom seismometer (OBS) survey in a grid along with multichannel seismic survey for gas hydrate exploration in the Mahanadi offshore, India. Here, we report on some interesting observations in seismic waveform data and their interpretations. These include sudden amplitude dimming in the multichannel data that is azimuth- and space-dependent and a clear manifestation of seismic anisotropy in the region. We observe significant patterns of shear wave splitting in the azimuthal gathers in the OBS data, clearly isolating the fast (S1) and slow (S2) axes of propagation in the radial azimuthal gathers. Further, amplitude nulls and amplitude maxima are observed in the transverse azimuthal gathers. These two features are diagnostic of the existence and orientation of anisotropy which is also modeled by generating full waveform synthetic seismograms. We interpret the occurrence of anisotropy to be due to the presence of fractures. The strike of this fracture set is inferred to be [Formula: see text] from the S1 and S2 orientation and variation in the P-wave amplitude with azimuth. The density of fracture network is estimated by full wave modeling of the OBS data. A good match between the synthetic and observed data is noticed for a near vertical fracture (dip angle of about 85°). The seismic image obtained from the 2D high-resolution multichannel profiles correlate well with the OBS results. Based on these analyses, we are able to delineate a fracture zone, which is linked to the near vertical faulting in the gas hydrate layers.

scholarly journals A Teleseismic Study of the 2002 Denali Fault, Alaska, Earthquake and Implications for Rapid Strong-Motion Estimation

2004 ◽  
Vol 20 (3) ◽  
pp. 617-637 ◽  
Author(s):  
Chen Ji ◽  
Don V. Helmberger ◽  
David J. Wald
Keyword(s):  
Moment Tensor ◽  
Strong Motion ◽  
P Wave ◽  
Phase Iii ◽  
Peak Ground Velocity ◽  
Centroid Moment Tensor ◽  
Waveform Data ◽  
Arrival Times ◽  
Denali Fault ◽  
Alaska Earthquake

Slip histories for the 2002 M7.9 Denali fault, Alaska, earthquake are derived rapidly from global teleseismic waveform data. In phases, three models improve matching waveform data and recovery of rupture details. In the first model (Phase I), analogous to an automated solution, a simple fault plane is fixed based on the preliminary Harvard Centroid Moment Tensor mechanism and the epicenter provided by the Preliminary Determination of Epicenters. This model is then updated (Phase II) by implementing a more realistic fault geometry inferred from Digital Elevation Model topography and further (Phase III) by using the calibrated P-wave and SH-wave arrival times derived from modeling of the nearby 2002 M6.7 Nenana Mountain earthquake. These models are used to predict the peak ground velocity and the shaking intensity field in the fault vicinity. The procedure to estimate local strong motion could be automated and used for global real-time earthquake shaking and damage assessment.

Radial thermo-chemical structure beneath Western and Northern Pacific inferred from seismic waveform inversion

2020 ◽  
Author(s):  
Frederic Deschamps ◽  
Kensuke Konishi ◽  
Nobuaki Fuji ◽  
Laura Cobden
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Depth Range ◽  
Seismic Structure ◽  
Waveform Data ◽  
Trade Off ◽  
Seismic Waveform ◽  
The Pacific ◽  
Northern Pacific

<p>The Earth’s deep mantle seismic structure is dominated by two large low shear-wave velocity provinces (LLSVPs) located beneath Africa and the Pacific. These structures have been observed by many studies and data sets, but their nature, purely thermal or thermo-chemical, is still debated. Due to trade-off between temperature and composition, maps of shear-wave velocity anomalies (dln<em>V</em><sub><em>S</em></sub>) alone are unable to discriminate between purely thermal and thermo-chemical hypotheses. Seismic shear-wave attenuation, measured by the quality factor <em>Q</em><sub>S</sub>, strongly depends on temperature and may bring additional information on this parameter, allowing to resolve the trade-off between temperature and composition. Here, we invert seismic waveform data jointly for radial models of dln<em>V</em><sub><em>S</em></sub>, and <em>Q</em><sub>S</sub> at two different locations beneath the Pacific, and from a depth of 2000 km down to the core-mantle boundary (CMB). At the Northern Pacific (NP) location, sampling a region around 50º N latitude and 180º E longitude, around <em>V</em><sub><em>S</em></sub> and <em>Q</em><sub>S</sub> remain close to the PREM values, representing the horizontal average mantle, throughout the investigated depth-range, with dln<em>V</em><sub><em>S</em></sub> ~ -0.1% and <em>Q</em><sub>S</sub> ~ 300 (compared to <em>Q</em><sub>PREM</sub> = 312). At the Western Pacific (WP) location, sampling the western tip of the Pacific LLSVP and the Caroline plume, both <em>V</em><sub><em>S</em></sub> and <em>Q</em><sub>S</sub> are substantially lower than PREM. Importantly, dln<em>V</em><sub><em>S</em></sub> and <em>Q</em><sub>S</sub> sharply decrease in the lowermost 500 km, from -0.6 % and 255 at 2500 km, to -2.5% and 215 close to the CMB. We then show that WP models cannot be explained by thermal anomalies alone, but require excess in iron of 3.5 to 4.5 % from the CMB up to 2600 km, and about 0.4 to 1.0 % at shallower depths. This later enrichment may be due to the entrainment of small amounts of the Pacific LLSVP material by the Caroline plume. The values of <em>Q</em><sub>S</sub> we observe give an estimate of the temperature anomalies, around 300-400 K close to the CMB, and 150 K at shallower depths. By contrast, NP models may have a purely thermal origin and can be explained by a temperature excess of about 50 K.</p>

scholarly journals Moho and uppermost mantle structure in the greater Alpine area from S-to-P converted waves

2021 ◽  
Author(s):  
Rainer Kind ◽  
Stefan M. Schmid ◽  
Xiaohui Yuan ◽  
Ben Heit ◽  
Thomas Meier ◽  
...  
Keyword(s):  
Pannonian Basin ◽  
P Wave ◽  
Mantle Structure ◽  
Uppermost Mantle ◽  
Waveform Data ◽  
Arrival Times ◽  
Tauern Window ◽  
Lateral Extrusion ◽  
Alpine Area ◽  
Teleseismic Data

Abstract. In the frame of the AlpArray project we analyze teleseismic data from permanent and temporary stations of the greater Alpine region to study seismic discontinuities down to about 140 km depth. We average broadband teleseismic S waveform data to retrieve S-to-P converted signals from below the seismic stations. In order to avoid processing artefacts, no deconvolution or filtering is applied and S arrival times are used as reference. We show a number of north-south and east-west profiles through the greater Alpine area. The Moho signals are always seen very clearly, and also negative velocity gradients below the Moho are visible in a number of profiles. A Moho depression is visible along larger parts of the Alpine chain. It reaches its largest depth of 60 km beneath the Tauern Window. The Moho depression ends however abruptly near about 13° E below the eastern Tauern Window. The Moho depression may represent the mantle trench, where the Eurasian lithosphere is subducted below the Adriatic lithosphere. East of 13° E an important along-strike change occurs; the image of the Moho changes completely. No Moho deepening is found in this easterly region; instead the Moho is updoming along the contact between the European and the Adriatic lithosphere all the way into the Pannonian Basin. An important along strike change was also detected in the upper mantle structure at about 14° E. There, the lateral disappearance of a zone of negative P-wave velocity gradient indicates that the S-dipping European slab laterally terminates east of the Tauern Window in the axial zone of the Alps. The area east of about 13° E is known to have been affected by severe late-stage modifications of the structure of crust and uppermost mantle during the Miocene when the ALCAPA (Alpine, Carpathian, Pannonian) block was subject to E-directed lateral extrusion.

scholarly journals Time-lapse slip variation associated with a medium-size earthquake revealed by "repeating" micro-earthquakes: the 1999 Xiuyan, Liaoning, <i>M</i><sub>S</sub> = 5.4 earthquake

2011 ◽  
Vol 11 (7) ◽  
pp. 1969-1981 ◽  
Author(s):  
Y. T. Li ◽  
Z. L. Wu ◽  
H. P. Peng ◽  
C. S. Jiang ◽  
G. P. Li
Keyword(s):  
Slip Rate ◽  
Time Lapse ◽  
Medium Size ◽  
Seismogenic Fault ◽  
Quarter Century ◽  
Waveform Data ◽  
Seismic Waveform ◽  
Waveform Cross Correlation ◽  
Before And After ◽  
Xiuyan Earthquake

Abstract. We obtained the time-lapse cumulative slip before and after the 29 November 1999, Xiuyan, Liaoning, China, Ms = 5.4 earthquake by using "repeating events" defined by waveform cross-correlation. We used the seismic waveform data from the Liaoning Regional Seismograph Network from June 1999 to December 2006. Two "multiplets" located near the seismogenic fault of the 1999 Xiuyan earthquake and the 4 February 1975, Haicheng Ms = 7.3 earthquake, respectively, were investigated. For the "multiplet" that occurred before and after the 1999 Xiuyan earthquake, apparent pre-shock accelerating-like slip behavior, clear immediate-post-seismic change, and relaxation-like post-seismic change can be observed. As a comparison, for the "multiplet" near the 1975 Haicheng earthquake which occurred a quarter century ago, the cumulative slip appears linear with a much smaller slip rate.

Sign in / Sign up

Export Citation Format

Share Document