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.

Comparison of local kinematic rupture joint inversion approaches for tsunami early warning: Examples of the 2017-2020 Mw > 6.3 East Aegean earthquakes

2021 ◽  
Author(s):  
Malte Metz ◽  
Marius Isken ◽  
Rongjiang Wang ◽  
Torsten Dahm ◽  
Haluk Özener ◽  
...  
Keyword(s):  
Early Warning ◽  
Joint Inversion ◽  
Moment Tensor ◽  
Strong Motion ◽  
Aegean Sea ◽  
High Rate ◽  
Source Inversion ◽  
Dynamic Rupture ◽  
Centroid Moment Tensor ◽  
Tsunami Early Warning

<p>The fast inversion of reliable centroid moment tensor and kinematic rupture parameters of earthquakes occurring near coastal margins is a key for the assessment of the tsunamigenic potential and early tsunami warning (TEW). In recent years, more and more multi-channel seismic and geodetic online station networks have been built-up to improve the TEW, for instance the GNSS and strong motion networks in Italy, Greece, and Turkey, additionally to the broadband seismological monitoring. Inclusion of such data for the fast kinematic source inversion can improve the resolution and robustness of its’ solutions. However, methods have to be further developed and tested to fully exploit the potential of such rich joint dataset.</p><p>In this frame, we compare and test two in-house developed, kinematic / dynamic rupture inversion methods which are based on completely different approaches. The IDS (Iterative Deconvolution and Stacking, Zhang et al., 2014) combines an iterative seismic network inversion with back projection techniques to retrieve subfault source time functions. The pseudo dynamic rupture model (Dahm et al., in review) links the rupture front propagation estimate based on the Eikonal equation with the dislocation derived from a boundary element method to model dislocation snapshots. We used the latter in both a fast rupture estimate and a fully probabilistic source inversion.</p><p>We use some Mw > 6.3 earthquakes that occurred in the coastal range of the Aegean Sea as an example for comparison: the Mw 6.3 Lesbos earthquake (12 June 2017), the Mw 6.6 Bodrum earthquake (20 July 2017), and the recent Mw 7.0 earthquake which occurred at Samos on 30 October 2020. The latter earthquake and the resulting tsunami caused fatalities and severe damage at the shorelines of Samos and around the city of Izmir, Turkey.<br>The fast estimates are based on only little data and/or prior information obtained from the regional seismicity catalogue and available active fault information. The large number of seismic (broadband, strong motion) and geodetic (high-rate GNSS) stations in local and regional distance from the earthquake with good azimuthal coverage jointly inverted with InSAR data allows for robust inversion results. These, and other solutions, are used as a reference for the comparison of our fast source estimates.<br>Preliminary results of the slip distribution and the source time function are in good agreement with modelling results from other authors.</p><p>We present our insights into the kinematics of the chosen earthquakes investigated by means of joint inversions. Finally, the accuracy of our first fast source estimates, which could be of potential use in tsunami early warning, will be discussed.</p>

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.

scholarly journals Unexpected Shallow Earthquake of August 1st, 2020 in the North of Indramayu, West Java, Indonesia

2021 ◽  
Vol 873 (1) ◽  
pp. 012043
Author(s):  
Jaya Murjaya ◽  
Pepen Supendi ◽  
Dwikorita Karnawati ◽  
Subagyo Pramumijoyo
Keyword(s):  
Seismic Station ◽  
Moment Tensor ◽  
Focal Mechanism Solution ◽  
West Java ◽  
Waveform Data ◽  
Arrival Times ◽  
S Waves ◽  
The North ◽  
Focus Depth ◽  
Deep Focus

Abstract During the last one hundred years, there are no shallow seismicity in the north of Java. This area is dominated by intermediate and deep focus earthquakes due to the subducted Indo-Australian slab. An earthquake with magnitude ML 4.5 struck Indramayu, north of West Java on August 1, 2020. According to the Agency for Meteorology, Climatology, and Geophysics (BMKG), the earthquake was felt III MMI scale in Indramayu and its vicinity. We used waveform data from BMKG seismic station in West Java, then we picked P-and S-waves arrival times from each station and hypocenter location was determined by Geiger method. We have detected Pn before Pg phase on four BMKG seismic stations, indicating a shallow crustal earthquake. Our inversion show that the earthquake occurred in 6.1805° S, 108.2612° E with 5 km focus depth at 16:24:38 GMT+7. Our focal mechanism solution was determined by using moment tensor inversion shows a strike-slip faulting, which corresponds to the active fault in the north of Indramayu.

gCAPjoint, A Software Package for Full Moment Tensor Inversion of Moderately Strong Earthquakes with Local and Teleseismic Waveforms

2020 ◽  
Vol 91 (6) ◽  
pp. 3550-3562
Author(s):  
Qipeng Bai ◽  
Sidao Ni ◽  
Risheng Chu ◽  
Zhe Jia
Keyword(s):  
Software Package ◽  
Epicentral Distance ◽  
Joint Inversion ◽  
Moment Tensor ◽  
Centroid Moment Tensor ◽  
Waveform Data ◽  
Strong Earthquakes ◽  
Moment Tensors ◽  
Seismic Networks ◽  
Full Moment Tensor

Abstract Earthquake moment tensors and focal depths are crucial to assessing seismic hazards and studying active tectonic and volcanic processes. Although less powerful than strong earthquakes (M 7+), moderately strong earthquakes (M 5–6.5) occur more frequently and extensively, which can cause severe damages in populated areas. The inversion of moment tensors is usually affected by insufficient local waveform data (epicentral distance <5°) in sparse seismic networks. It would be necessary to combine local and teleseismic data (epicentral distance 30°–90°) for a joint inversion. In this study, we present the generalized cut-and-paste joint (gCAPjoint) algorithm to jointly invert full moment tensor and centroid depth with local and teleseismic broadband waveforms. To demonstrate the effectiveness and explore the limitations of this algorithm, we perform case studies on three earthquakes with different tectonic settings and source properties. Comparison of our results with global centroid moment tensor and other catalog solutions illustrates that both non-double-couple compositions of the focal mechanisms and centroid depths can be reliably recovered for very shallow (<10  km) earthquakes and intermediate-depth events with this software package.

Assessing the role of selected constraints in Bayesian dynamic source inversion: Application to the 2017 Mw 6.3 Lesvos earthquake

2021 ◽  
Author(s):  
Filip Kostka ◽  
Jiří Zahradník ◽  
Efthimios Sokos ◽  
František Gallovič
Keyword(s):  
Posterior Distribution ◽  
Moment Tensor ◽  
Strong Motion ◽  
Monte Carlo Algorithm ◽  
Source Inversion ◽  
Dynamic Rupture ◽  
Centroid Moment Tensor ◽  
Trade Offs ◽  
Finite Fault ◽  
The Mean

Summary A dynamic finite-fault source inversion for stress and frictional parameters of the Mw 6.3 2017 Lesvos earthquake is carried out. The mainshock occurred on June 12, offshore the southeastern coast of the Greek island of Lesvos in the north Aegean Sea. It caused 1 fatality, 15 injuries, and extensive damage to the southern part of the island. Dynamic rupture evolution is modeled on an elliptic patch, using the linear slip-weakening friction law. The inversion is posed as a Bayesian problem and the Parallel Tempering Markov Chain Monte Carlo algorithm is used to obtain posterior probability distributions by updating the prior distribution with progressively more constraints. To calculate the first posterior distribution, only the constraint that the model should expand beyond the nucleation patch is used. Then, we add the constraint that the model should reach a moment magnitude similar to that obtained from our centroid moment tensor inversion. For the final posterior distribution, 15 acceleration records from Greek and Turkish strong motion networks at near regional distances ($\approx 30 - 150$ km) in the frequency range of 0.05–0.15 Hz are used. The three posterior distributions are compared to understand how much each constraint contributes to resolving different quantities. The most probable values and uncertainties of individual parameters are also calculated, along with their mutual trade-offs. The features best determined by seismograms in the final posterior distribution include the position of the nucleation region, the mean direction of rupture (towards WNW), the mean rupture speed (with 68 per cent of the distribution lying between 1.4–2.6 km/s), radiated energy (12–65 TJ), radiation efficiency (0.09–0.38), and the mean stress drop (2.2–6.5 MPa).

scholarly journals Bayesian Inference of Centroid Moment Tensors of the 2019 Ambon (Mw 6.5) Aftershock Earthquake Sequence, Indonesia: A Preliminary Result

2021 ◽  
Vol 873 (1) ◽  
pp. 012022
Author(s):  
A W Baskara ◽  
D P Sahara ◽  
A D Nugraha ◽  
A Muhari ◽  
A A Rusdin ◽  
...  
Keyword(s):  
Moment Tensor ◽  
Strike Slip ◽  
Fault Reactivation ◽  
Model Parameters ◽  
The South ◽  
Centroid Moment Tensor ◽  
Waveform Data ◽  
Moment Tensors ◽  
The Impact ◽  
Dextral Strike Slip

Abstract The Ambon Mw 6.5 earthquake on September 26th, 2019, had contributed to give severe damages and significantly increased seismicity around Ambon Island and surrounding areas. Mainshock was followed by aftershocks with spatial distribution added to the impact of destructions in this region. We investigated aftershocks sequences to reveal the effect of mainshock toward the change in the in-situ stress field, including the possibility of the existing faults reactivation and the generation of aftershocks. We inferred centroid moment tensor (CMT) for significant aftershock events with Mw more than 4.0 using waveform data recorded from October 18th to December 15th, 2019. The aftershock focal mechanism was determined using the Bayesian full-waveform inversion code ISOLA-Obspy. This approach provides the uncertainty of the CMT model parameters. From ten CMT solution we had inferred in three seismic clusters, we found that majority of events have a strike-slip mechanism. Four events located on the south of the N-S trendings have a dextral strike-slip fault type, reflected the rupture of the mainshocks fault plane. Three events in the cluster of Ambon Island are dextral strike-slip, confirming the presence of the fault reactivation. Meanwhile, three CMT solutions in the north show the dextral strike-slip faulting and may belong to the mainshock main fault, connected with the cluster in the south.

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 A CROSS-CORRELATION TECHNIQUE FOR RELOCATION OF SEISMICITY IN THE WESTERN CORINTH RIFT

2017 ◽  
Vol 43 (4) ◽  
pp. 2015
Author(s):  
V. Kapetanidis ◽  
P. Papadimitriou ◽  
K. Makropoulos
Keyword(s):  
Cross Correlation ◽  
Time Lag ◽  
P Wave ◽  
Double Difference ◽  
Correlation Technique ◽  
S Wave ◽  
Waveform Data ◽  
Arrival Times ◽  
Linkage Algorithm ◽  
Seismogenic Structures

Local seismological networks provide data that allow the location of microearthquakes which otherwise would be dismissed due to low magnitudes and low signal-to-noise ratios of their seismic signals. The Corinth Rift Laboratory (CRL) network, installed in the western Corinth rift, has been providing digital waveform data since 2000. In this work, a semi-automatic picking technique has been applied which exploits the similarity between waveforms of events that have occurred in approximately the same area of an active fault. Similarity is measured by the crosscorrelation maxi-mum of full signals. Events with similar waveforms are grouped in multiplet clusters using the nearest-neighbour linkage algorithm. Manually located events act as masters, while automatically located events of each multiplet cluster act as slaves. By cross-correlating the P-wave or S-wave segments of a master event with the corresponding segments of each of its slave events, after appropriately aligning their offsets, the measured time-lag at the cross-correlation maximum can be subtracted from the arrival-time of the slave event. After the correction of the arrival-times, a double-difference technique is applied to the modified catalogue to further improve the locations of clusters and distinguish the active seismogenic structures in the tectonically complex Western Corinth rift.

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.

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