Hypocenter Analysis of Aftershocks Data of the Mw 6.3, 27 May 2006 Yogyakarta Earthquake Using Oct-Tree Importance Sampling Method

2018 ◽  
Vol 881 ◽  
pp. 89-97 ◽  
Author(s):  
Asri Wulandari ◽  
Ade Anggraini ◽  
Wiwit Suryanto
Keyword(s):  
Importance Sampling ◽  
Sampling Method ◽  
Velocity Model ◽  
Fault Scarp ◽  
P Wave ◽  
Double Difference ◽  
S Wave ◽  
Maximum Probability ◽  
Importance Sampling Method ◽  
Dip Angle

Yogyakarta earthquake, Mw 6.3, 27 May 2006 had killed 5,571 victims and destroyed more than 1 million buildings. This incident became the most destructive earthquake disaster over the last 11 years in Indonesia. Earthquake mitigation plan in the area has been carried out by understands the location of the fault. The location of the fault is still unclear among geoscientists until now. In this case, analysis of the aftershocks using oct-tree importance sampling method was applied to support the location of the fault that responsible for the 2006 Yogyakarta earthquake. Oct-tree importance sampling is a method that is recursively subdividing the solution domain into exactly eight children for estimating properties of a particular distribution. The final result of the subdividing process is a cell that has a maximum Probability Density Function (PDF) and identified as the location of the hypocenter. Input data consists of the arrival time of the P wave and S wave of the aftershocks catalog from 3-7 June 2006 and the coordinate of the 12 seismometers, and 1D velocity model of the study area. Based on the hypocenter distribution of the aftershocks data with the proposed method show a clearer trend of the fault compared with the aftershocks distribution calculated with theHypo71program. The fault trend has a strike orientation of N 42° E with a dip angle of 80° parallel with the fault scarp along the Opak River at the distance of about 15 km to the east. This fault trend is similar with the fault orientation obtained using the Double Difference Algorithm.

Passive seismic tomography using recorded microseismicity: Application to mining-induced seismicity

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. B41-B57 ◽  
Author(s):  
Himanshu Barthwal ◽  
Mirko van der Baan
Keyword(s):  
Seismic Tomography ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
Lateral Velocity ◽  
Study Region ◽  
Arrival Times ◽  
3D Velocity Model ◽  
Passive Seismic ◽  
Dip Angle

Microseismicity is recorded during an underground mine development by a network of seven boreholes. After an initial preprocessing, 488 events are identified with a minimum of 12 P-wave arrival-time picks per event. We have developed a three-step approach for P-wave passive seismic tomography: (1) a probabilistic grid search algorithm for locating the events, (2) joint inversion for a 1D velocity model and event locations using absolute arrival times, and (3) double-difference tomography using reliable differential arrival times obtained from waveform crosscorrelation. The originally diffusive microseismic-event cloud tightens after tomography between depths of 0.45 and 0.5 km toward the center of the tunnel network. The geometry of the event clusters suggests occurrence on a planar geologic fault. The best-fitting plane has a strike of 164.7° north and dip angle of 55.0° toward the west. The study region has known faults striking in the north-northwest–south-southeast direction with a dip angle of 60°, but the relocated event clusters do not fall along any mapped fault. Based on the cluster geometry and the waveform similarity, we hypothesize that the microseismic events occur due to slips along an unmapped fault facilitated by the mining activity. The 3D velocity model we obtained from double-difference tomography indicates lateral velocity contrasts between depths of 0.4 and 0.5 km. We interpret the lateral velocity contrasts in terms of the altered rock types due to ore deposition. The known geotechnical zones in the mine indicate a good correlation with the inverted velocities. Thus, we conclude that passive seismic tomography using microseismic data could provide information beyond the excavation damaged zones and can act as an effective tool to complement geotechnical evaluations.

Joint microseismic event location and anisotropic velocity inversion with the cross double-difference method using downhole microseismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. KS63-KS73
Author(s):  
Yangyang Ma ◽  
Congcong Yuan ◽  
Jie Zhang
Keyword(s):  
Arrival Time ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
S Wave ◽  
Arrival Times ◽  
Monitoring Well ◽  
The Cross ◽  
Microseismic Event ◽  
Wave Arrival

We have applied the cross double-difference (CDD) method to simultaneously determine the microseismic event locations and five Thomsen parameters in vertically layered transversely isotropic media using data from a single vertical monitoring well. Different from the double-difference (DD) method, the CDD method uses the cross-traveltime difference between the S-wave arrival time of one event and the P-wave arrival time of another event. The CDD method can improve the accuracy of the absolute locations and maintain the accuracy of the relative locations because it contains more absolute information than the DD method. We calculate the arrival times of the qP, qSV, and SH waves with a horizontal slowness shooting algorithm. The sensitivities of the arrival times with respect to the five Thomsen parameters are derived using the slowness components. The derivations are analytical, without any weak anisotropic approximation. The input data include the cross-differential traveltimes and absolute arrival times, providing better constraints on the anisotropic parameters and event locations. The synthetic example indicates that the method can produce better event locations and anisotropic velocity model. We apply this method to the field data set acquired from a single vertical monitoring well during a hydraulic fracturing process. We further validate the anisotropic velocity model and microseismic event locations by comparing the modeled and observed waveforms. The observed S-wave splitting also supports the inverted anisotropic results.

Cross double-difference inversion for microseismic event location using data from a single monitoring well

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. KS183-KS194 ◽  
Author(s):  
Xiao Tian ◽  
Wei Zhang ◽  
Jie Zhang
Keyword(s):  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
Separate Analysis ◽  
S Wave ◽  
Monitoring Wells ◽  
Monitoring Well ◽  
Single Well ◽  
Microseismic Events ◽  
Wave Arrival

The double-difference (DD) location method has long been applied for locating a cluster of earthquakes with data recorded at surface seismic stations. This method has also been used for locating microseismic events with multiple monitoring wells during hydraulic fracturing. We first extended the approach for locating a cluster of microseismic events with data recorded from a single well. To do this, we have reduced the 3D location problem to 2D by projecting all of the events onto a vertical ([Formula: see text]) plane, with a vertical [Formula: see text]-axis and a horizontal [Formula: see text]-axis representing the distance to the monitoring well, considering the symmetric character of the 1D velocity model and the vertical monitoring well. We then performed a 2D location inversion and projected the results back to 3D using the event azimuths, which were determined from a separate analysis of the initial P-wave polarizations. However, although the DD method could determine relative locations of events reasonably well, it yielded poor absolute locations. We have developed a cross DD (CDD) approach using the cross traveltime difference between the P-wave arrival of one event and the S-wave arrival of another event for inversion instead of the arrival-time differences of the same phases as in the DD method. The CDD method contains more information on absolute locations than the DD method, resulting in a much more stable absolute location determination. The synthetic and field data tests indicated that the CDD method could improve the accuracy of relative and absolute event locations in microseismic clusters.

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

Fault parameters determined by near- and far-field data: The Wakasa Bay earthquake of March 26, 1963

1974 ◽  
Vol 64 (5) ◽  
pp. 1369-1382 ◽  
Author(s):  
Katsuyuki Abe
Keyword(s):  
Effective Stress ◽  
Stress Drop ◽  
P Wave ◽  
Slip Angle ◽  
Polarization Angle ◽  
S Wave ◽  
Seismological Data ◽  
Fault Parameters ◽  
Wakasa Bay ◽  
Dip Angle

Abstract The source process of the Wakasa Bay earthquake (M = 6.9, 35.80°N, 135.76°E, depth 4 km) which occurred near the west coast of Honshu Island, Japan, on March 26, 1963, is studied on the basis of the seismological data. Dynamic and static parameters of the faulting are determined by directly comparing synthetic seismograms with observed seismograms recorded at seismic near and far distances. The De Hoop-Haskell method is used for the synthesis. The average dislocation is determined to be 60 cm. The overall dislocation velocity is estimated to be 30 cm/sec, the rise time of the slip dislocation being determined as 2 sec. The other fault parameters determined, with supplementary data on the P-wave first motion, the S-wave polarization angle, and the aftershocks, are: source geometry, dip direction N 144°E, dip angle 68°, slip angle 22° (right-lateral strike-slip motion with some dip-slip component); fault dimension, 20 km length by 8 km width; rupture velocity, 2.3 km/sec (bilateral); seismic moment, 3.3 × 1025 dyne-cm; stress drop, 32 bars. The effective stress available to accelerate the fault motion is estimated to be about 40 bars. The approximate agreement between the effective stress and the stress drop suggests that most of the effective stress was released at the time of the earthquake.

scholarly journals On the optimal importance process for piecewise deterministic Markov process

2019 ◽  
Vol 23 ◽  
pp. 893-921
Author(s):  
H. Chraibi ◽  
A. Dutfoy ◽  
T. Galtier ◽  
J. Garnier
Keyword(s):  
Importance Sampling ◽  
Simulation Study ◽  
Variance Reduction ◽  
Sampling Method ◽  
Piecewise Deterministic Markov Process ◽  
Importance Sampling Method ◽  
Markovian Processes ◽  
Component Systems ◽  
Reference Measure ◽  
Reduction Methods

In order to assess the reliability of a complex industrial system by simulation, and in reasonable time, variance reduction methods such as importance sampling can be used. We propose an adaptation of this method for a class of multi-component dynamical systems which are modeled by piecewise deterministic Markovian processes (PDMP). We show how to adapt the importance sampling method to PDMP, by introducing a reference measure on the trajectory space. This reference measure makes it possible to identify the admissible importance processes. Then we derive the characteristics of an optimal importance process, and present a convenient and explicit way to build an importance process based on theses characteristics. A simulation study compares our importance sampling method to the crude Monte-Carlo method on a three-component systems. The variance reduction obtained in the simulation study is quite spectacular.

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