A Compact Program for 3D Passive Seismic Source-Location Imaging

2021 ◽  
Author(s):  
Yangkang Chen ◽  
Omar M. Saad ◽  
Min Bai ◽  
Xingye Liu ◽  
Sergey Fomel
Keyword(s):  
Open Source ◽  
Seismic Source ◽  
Velocity Model ◽  
Source Location ◽  
Imaging Method ◽  
Python Script ◽  
Passive Seismic ◽  
Synthetic Test ◽  
Microseismic Source Location ◽  
C Program

Abstract Microseismic source-location imaging is important for inferring the dynamic status of reservoirs during hydraulic fracturing. The accuracy and resolution of the located microseismic sources are closely related to the imaging technique. We present an open-source program for high-fidelity and high-resolution 3D microseismic source-location imaging. The presented code is compact in the sense that all required subroutines are included in one single C program, based on which seismic wavefields can be propagated either forward during a synthetic test or backward during a real time-reversal imaging process. The compact C program is accompanied by a Python script known as the SConstruct file in the Madagascar open-source platform to compile and run the C program. The velocity model and recorded microseismic data can be input using the Python script. This compact program is useful for educational purposes and for future algorithm development. We introduce the basics of the imaging method used in the presented package and present one representative synthetic example and a field data example. The results show that the presented program can be reliably used to locate source locations using a passive seismic dataset.

Microseismic velocity model inversion and source location: The use of neighborhood algorithm and master station method

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. KS49-KS63 ◽  
Author(s):  
Yuyang Tan ◽  
Chuan He ◽  
Zhonghua Mao
Keyword(s):  
Velocity Model ◽  
Source Location ◽  
Model Inversion ◽  
Tuning Parameters ◽  
Velocity Models ◽  
Systematic Methodology ◽  
Microseismic Source Location ◽  
Microseismic Events ◽  
Master Station ◽  
Half Length

The accuracy of the velocity model strongly affects the accuracy of microseismic source location and hence the reliability of fracture imaging. We have developed a systematic methodology for microseismic velocity model inversion and source location. A new misfit function is used for both problems, which yields more reliable result than the conventional ones. Using the same measure of misfit, the location errors resulting from the use of different misfit functions are eliminated. The neighborhood algorithm and master station method (MSM) are adopted for calculating the velocity model and source location, respectively. The reason for using the neighborhood algorithm is that it has fewer tuning parameters and is easy to be tuned, whereas the advantage of the MSM is that it can automatically remove the mispicks. The performance of the proposed methods is illustrated using the ball-hit events with known locations, and the validity of the inversion results is verified by the relocations of these events. We also used the inverted velocity models to locate the microseismic events detected from the monitoring data. The location result indicates that the fractures have an average half-length of 280 m and height of 55 m and the fracture azimuth is approximately N77°W.

Sectional velocity model for microseismic source location in tunnels

2015 ◽  
Vol 45 ◽  
pp. 73-83 ◽  
Author(s):  
Guang-Liang Feng ◽  
Xia-Ting Feng ◽  
Bing-Rui Chen ◽  
Ya-Xun Xiao ◽  
Quan Jiang
Keyword(s):  
Velocity Model ◽  
Source Location ◽  
Microseismic Source Location

Interpretation of physical status of arrival picks for microseismic source location

1990 ◽  
Vol 80 (6A) ◽  
pp. 1643-1660
Author(s):  
Maochen Ge ◽  
P. K. Kaiser
Keyword(s):  
Time Window ◽  
Velocity Model ◽  
Source Location ◽  
Physical Status ◽  
Voltage Level ◽  
Window Current ◽  
Unique Approach ◽  
Microseismic Source Location ◽  
Event Based ◽  
Microseismic Event

Abstract The automatic recognition of a microseismic event usually relies on two criteria: threshold voltage level and event recognition time window. Current microseismic source location techniques are severaly limited because the physical status of an arrival pick is not known. This paper presents a theory for the identification of the physical status of an arrival pick. It consists of an arrival time difference analysis and a residual analysis. The theory can be used to discriminate various types of arrival picks critical for microseismic source location. An event-based velocity model can then be established and used to determine the source location more accurately. The theory provides a unique approach that may be utilized in various automatic acoustic/seismic processing systems.

Iterative passive-source location estimation and velocity inversion using geometric-mean reverse-time migration and full-waveform inversion

2020 ◽  
Vol 223 (3) ◽  
pp. 1935-1947
Author(s):  
Bin Lyu ◽  
Nori Nakata
Keyword(s):  
Geometric Mean ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Source Location ◽  
Location Estimation ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Velocity Inversion ◽  
Time Migration ◽  
Passive Seismic

SUMMARY Passive-seismic provides useful information for reservoir monitoring and structural imaging; for example, the locations of microseismic events are helpful to understand the extension of the hydraulic fracturing. However, passive-seismic imaging still faces some challenges. First, it is not easy to know where the passive-seismic events happened, which is known as passive-source locating. Additionally, the accuracy of the subsurface velocity model will influence the accuracy of the estimated passive-source locations and the quality of the structural imaging obtained from the passive-seismic data. Therefore the velocity inversion using the passive-seismic data is required to provide the velocity with higher accuracy. Focusing on these challenges, we develop an iterative passive-source location estimation and velocity inversion method using geometric-mean reverse-time migration (GmRTM) and full-waveform inversion (FWI). In each iteration, the source location is estimated using a high-resolution GmRTM method, which provides a better focusing of passive-source imaging compared to conventional wavefield scanning method. The passive-source FWI is then followed to optimize the velocity model using the estimated source location provided by GmRTM. The source location estimation and velocity inversion are implemented sequentially. We evaluate this iterative method using the Marmousi model data set. The experiment result and sensitivity analysis indicate that the proposed method is effective to locate the sources and optimize velocity model in the areas with complicated subsurface structures and noisy recordings.

scholarly journals Using the Morris sensitivity analysis method to assess the importance of input variables on time-reversal imaging of seismic sources

2019 ◽  
Vol 67 (6) ◽  
pp. 1525-1533 ◽  
Author(s):  
Anna Franczyk
Keyword(s):  
Sensitivity Analysis ◽  
Time Reversal ◽  
Seismic Source ◽  
Source Location ◽  
Seismic Network ◽  
Imaging Method ◽  
Analysis Method ◽  
Sensitivity Analysis Method ◽  
Input Variables ◽  
Seismic Source Location

Abstract The time-reversal imaging method has become a standard technique for seismic source location using both acoustic and elastic wave equations. Although there are many studies on the determination of the relevant parameter for visualization of the time-reversal method, little has been done so far to investigate the accuracy of seismic source location depending on parameters such as the geometry of the seismic network or underestimation of the velocity model. This paper investigates the importance of the accuracy of seismic source location using the time-reversal imaging method of input variables such as seismic network geometry and the assumed geological model. For efficient visualization of seismic wave propagation and interference, peak-to-average power ratio was used. Identification of the importance of variables used in seismic source location was obtained using the Morris elementary effect method, which is a global sensitivity analysis method.

Microseismic source location method based on a velocity model database and statistical analysis

2021 ◽  
Vol 14 (19) ◽  
Author(s):  
Bing-Rui Chen ◽  
Tao Li ◽  
Xin-Hao Zhu ◽  
Fan-Bo Wei ◽  
Xu Wang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Velocity Model ◽  
Source Location ◽  
Location Method ◽  
Microseismic Source Location ◽  
Model Database

Gaussian beam migration

Geophysics ◽  
1990 ◽  
Vol 55 (11) ◽  
pp. 1416-1428 ◽  
Author(s):  
N. Ross Hill
Keyword(s):  
Gaussian Beam ◽  
Wave Field ◽  
Seismic Source ◽  
Velocity Model ◽  
Downward Continuation ◽  
Gaussian Beams ◽  
Seismic Velocities ◽  
Migration Method ◽  
Gaussian Beam Migration ◽  
Lateral Variations

Just as synthetic seismic data can be created by expressing the wave field radiating from a seismic source as a set of Gaussian beams, recorded data can be downward continued by expressing the recorded wave field as a set of Gaussian beams emerging at the earth’s surface. In both cases, the Gaussian beam description of the seismic‐wave propagation can be advantageous when there are lateral variations in the seismic velocities. Gaussian‐beam downward continuation enables wave‐equation calculation of seismic propagation, while it retains the interpretive raypath description of this propagation. This paper describes a zero‐offset depth migration method that employs Gaussian beam downward continuation of the recorded wave field. The Gaussian‐beam migration method has advantages for imaging complex structures. Like finite‐difference migration, it is especially compatible with lateral variations in velocity, but Gaussian beam migration can image steeply dipping reflectors and will not produce unwanted reflections from structure in the velocity model. Unlike other raypath methods, Gaussian beam migration has guaranteed regular behavior at caustics and shadows. In addition, the method determines the beam spacing that ensures efficient, accurate calculations. The images produced by Gaussian beam migration are usually stable with respect to changes in beam parameters.

3D passive wavefield imaging using the energy norm

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. KS13-KS27 ◽  
Author(s):  
Daniel Rocha ◽  
Paul Sava ◽  
Jeffrey Shragge ◽  
Ben Witten
Keyword(s):  
Wave Mode ◽  
Source Location ◽  
Time Step ◽  
Elastic Wave Equation ◽  
Imaging Condition ◽  
Mode Decomposition ◽  
Passive Seismic ◽  
Anisotropic Elastic ◽  
Wavefield Extrapolation ◽  
Multicomponent Data

In passive seismic monitoring of microseismicity, full-wavefield imaging offers a robust approach for the estimation of source location and mechanism. With multicomponent data and the full 3D anisotropic elastic wave equation, the coexistence of P- and S-modes at the source location in time-reversal wavefield extrapolation allows the development of imaging conditions that identify the source position and radiation pattern. We have developed an imaging condition for passive wavefield imaging that is based on energy conservation and is related to the source mechanism. Similar to the correlation between the decomposed P- and S-wavefields — the most common imaging condition used in passive elastic wavefield imaging — our proposed imaging condition compares the different modes present in the displacement field producing a strong and focused correlation at the source location without costly wave-mode decomposition at each time step. Numerical experiments demonstrate the advantages of the proposed imaging condition (compared to PS correlation with decomposed wave modes), its sensitivity with respect to velocity inaccuracy, and its quality and efficacy in estimating the source location.

scholarly journals Rapid estimation of earthquake locations using waveform traveltimes

2019 ◽  
Vol 217 (3) ◽  
pp. 1727-1741 ◽  
Author(s):  
D W Vasco ◽  
Seiji Nakagawa ◽  
Petr Petrov ◽  
Greg Newman
Keyword(s):  
Standard Error ◽  
Random Noise ◽  
Grid Point ◽  
Synthetic Data ◽  
Velocity Model ◽  
Source Location ◽  
Model Errors ◽  
Average Deviation ◽  
Origin Time ◽  
Data Set

SUMMARY We introduce a new approach for locating earthquakes using arrival times derived from waveforms. The most costly computational step of the algorithm scales as the number of stations in the active seismographic network. In this approach, a variation on existing grid search methods, a series of full waveform simulations are conducted for all receiver locations, with sources positioned successively at each station. The traveltime field over the region of interest is calculated by applying a phase picking algorithm to the numerical wavefields produced from each simulation. An event is located by subtracting the stored traveltime field from the arrival time at each station. This provides a shifted and time-reversed traveltime field for each station. The shifted and time-reversed fields all approach the origin time of the event at the source location. The mean or median value at the source location thus approximates the event origin time. Measures of dispersion about this mean or median time at each grid point, such as the sample standard error and the average deviation, are minimized at the correct source position. Uncertainty in the event position is provided by the contours of standard error defined over the grid. An application of this technique to a synthetic data set indicates that the approach provides stable locations even when the traveltimes are contaminated by additive random noise containing a significant number of outliers and velocity model errors. It is found that the waveform-based method out-performs one based upon the eikonal equation for a velocity model with rapid spatial variations in properties due to layering. A comparison with conventional location algorithms in both a laboratory and field setting demonstrates that the technique performs at least as well as existing techniques.

Sign in / Sign up

Export Citation Format

Share Document