Optimization of Geophone Array for Monitoring Geologic Carbon Sequestration Using Double-Difference Tomography

Synthetic data were analyzed to determine the most cost-effective tomographic monitoring system for a geologic carbon sequestration injection site. Double-difference tomographic inversion was performed on 125 synthetic data sets: five stages of CO2 plume growth, five seismic event regions, and five geophone arrays. Each resulting velocity model was compared quantitatively to its respective synthetic velocity model to determine accuracy. The results were examined to determine a relationship between cost and accuracy in monitoring, verification, and accounting applications using double-difference-tomography. The geophone arrays with widely varying geophone locations, both laterally and vertically, performed best.

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Tomographic inversion by matrix transformation

Geophysics ◽

10.1190/1.2963496 ◽

2008 ◽

Vol 73 (5) ◽

pp. VE35-VE38 ◽

Cited By ~ 2

Author(s):

Jonathan Liu ◽

Lorie Bear ◽

Jerry Krebs ◽

Raffaella Montelli ◽

Gopal Palacharla

Keyword(s):

Linear System ◽

Seismic Velocity ◽

Synthetic Data ◽

Velocity Model ◽

Matrix Transformation ◽

Uniform Grid ◽

Complex Structures ◽

Tomographic Inversion ◽

Velocity Models ◽

The Stability

We have developed a new method to build seismic velocity models for complex structures. In our approach, we use a spatially nonuniform parameterization of the velocity model in tomography and a uniform grid representation of the same velocity model in ray tracing to generate the linear system of tomographic equations. Subsequently, a matrix transformation is applied to the system of equations to produce a new linear system of tomographic equations using nonuniform parameterization. In this way, we improved the stability of tomographic inversion without adding computing costs. We tested the effectiveness of our process on a 3D synthetic data example.

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A robust first-arrival picking workflow using convolutional and recurrent neural networks

Geophysics ◽

10.1190/geo2019-0437.1 ◽

2020 ◽

Vol 85 (5) ◽

pp. U109-U119

Author(s):

Pengyu Yuan ◽

Shirui Wang ◽

Wenyi Hu ◽

Xuqing Wu ◽

Jiefu Chen ◽

...

Keyword(s):

Deep Learning ◽

Model Building ◽

Signal To Noise Ratio ◽

Synthetic Data ◽

Velocity Model ◽

Surface Velocity ◽

Data Sets ◽

Near Surface ◽

Segmentation Boundary ◽

First Arrival

A deep-learning-based workflow is proposed in this paper to solve the first-arrival picking problem for near-surface velocity model building. Traditional methods, such as the short-term average/long-term average method, perform poorly when the signal-to-noise ratio is low or near-surface geologic structures are complex. This challenging task is formulated as a segmentation problem accompanied by a novel postprocessing approach to identify pickings along the segmentation boundary. The workflow includes three parts: a deep U-net for segmentation, a recurrent neural network (RNN) for picking, and a weight adaptation approach to be generalized for new data sets. In particular, we have evaluated the importance of selecting a proper loss function for training the network. Instead of taking an end-to-end approach to solve the picking problem, we emphasize the performance gain obtained by using an RNN to optimize the picking. Finally, we adopt a simple transfer learning scheme and test its robustness via a weight adaptation approach to maintain the picking performance on new data sets. Our tests on synthetic data sets reveal the advantage of our workflow compared with existing deep-learning methods that focus only on segmentation performance. Our tests on field data sets illustrate that a good postprocessing picking step is essential for correcting the segmentation errors and that the overall workflow is efficient in minimizing human interventions for the first-arrival picking task.

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From migration to inversion velocity analysis

Geophysics ◽

10.1190/geo2016-0359.1 ◽

2017 ◽

Vol 82 (3) ◽

pp. S207-S223 ◽

Cited By ~ 18

Author(s):

Hervé Chauris ◽

Emmanuel Cocher

Keyword(s):

Objective Function ◽

Synthetic Data ◽

Velocity Model ◽

Specific Power ◽

Model Parameters ◽

Data Sets ◽

Velocity Analysis ◽

Low Velocity ◽

Image Domain ◽

New Approach

Migration velocity analysis (MVA) is a technique defined in the image domain to determine the background velocity model controlling the kinematics of wave propagation. In the presence of discontinuous interfaces, the velocity gradient used to iteratively update the velocity model exhibits spurious oscillations. For more stable results, we replace the migration part by an inversion scheme. By definition, migration is the adjoint of the Born modeling operator, whereas inversion is its asymptotic inverse. We have developed new expressions in 1D and 2D cases based on two-way wave-equation operators. The objective function measures the quality of the images obtained by inversion in the extended domain depending on the subsurface offset. In terms of implementation, the new approach is very similar to classic MVA. A 1D analysis found that oscillatory terms around the interface positions can be removed by multiplying the inversion result with the velocity at a specific power before evaluating the objective function. Several 2D synthetic data sets are discussed through the computation of the gradient needed to update the model parameters. Even for discontinuous reflectivity models, the new approach provides results without artificial oscillations. The model update corresponds to a gradient of an existing objective function, which was not the case for the horizontal contraction approach proposed as an alternative to deal with gradient artifacts. It also correctly handles low-velocity anomalies, contrary to the horizontal contraction approach. Inversion velocity analysis offers new perspectives for the applicability of image-domain velocity analysis.

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The finite-frequency sensitivity kernel for migration residual moveout and its applications in migration velocity analysis

Geophysics ◽

10.1190/1.2993536 ◽

2008 ◽

Vol 73 (6) ◽

pp. S241-S249 ◽

Cited By ~ 47

Author(s):

Xiao-Bi Xie ◽

Hui Yang

Keyword(s):

Wave Equation ◽

Synthetic Data ◽

Velocity Model ◽

Migration Velocity ◽

Data Sets ◽

Velocity Analysis ◽

Depth Migration ◽

Sensitivity Kernel ◽

Migration Velocity Analysis ◽

Tomography Method

We have derived a broadband sensitivity kernel that relates the residual moveout (RMO) in prestack depth migration (PSDM) to velocity perturbations in the migration-velocity model. We have compared the kernel with the RMO directly measured from the migration image. The consistency between the sensitivity kernel and the measured sensitivity map validates the theory and the numerical implementation. Based on this broadband sensitivity kernel, we propose a new tomography method for migration-velocity analysis and updating — specifically, for the shot-record PSDM and shot-index common-image gather. As a result, time-consuming angle-domain analysis is not required. We use a fast one-way propagator and multiple forward scattering and single backscattering approximations to calculate the sensitivity kernel. Using synthetic data sets, we can successfully invert velocity perturbations from the migration RMO. This wave-equation-based method naturally incorporates the wave phenomena and is best teamed with the wave-equation migration method for velocity analysis. In addition, the new method maintains the simplicity of the ray-based velocity analysis method, with the more accurate sensitivity kernels replacing the rays.

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A cyber-threat analytic model for autonomous detection of virtual property theft

Information and Computer Security ◽

10.1108/ics-11-2016-0087 ◽

2017 ◽

Vol 25 (4) ◽

pp. 358-381

Author(s):

Nicholas Patterson ◽

Michael Hobbs ◽

Tianqing Zhu

Keyword(s):

Virtual World ◽

Synthetic Data ◽

Cost Effective ◽

Data Sets ◽

Data Set ◽

Content Type ◽

Virtual Property ◽

Attack Data ◽

Virtual World Environments ◽

Privacy Issues

Purpose The purpose of this study is to provide a framework to detect and prevent virtual property theft in virtual world environments. The issue of virtual property theft is a serious problem which has ramifications in both the real and virtual world. Virtual world users invest a considerable amount of time, effort and often money to collect virtual property, only to have them stolen by thieves. Many virtual property thefts go undetected and often only discovered after the incident has occurred. Design/methodology/approach This paper presents the design of an autonomic detection framework to identify virtual property theft at two key stages: account intrusion and virtual property trades. Account intrusion is an unauthorized user attempting to gain access to an account and unauthorized virtual property trades are trading of items between two users which exhibit theft characteristics. Findings Initial tests of this framework on a synthetic data set show an 80 per cent detection rate. This framework allows virtual world developers to tailor and extend it to suit their specific requirements. It provides an effective way of detecting virtual property theft while being low maintenance, user friendly and cost effective. Originality/value To the author’s knowledge, there is no detection framework, system or tool that works on virtual property theft detection in virtual world environments without access to authentic virtual world data or attack data (because of privacy issues and unwillingness of virtual world environments companies to collaborate). The topic of virtual property theft, lack of existing labelled data sets, user anonymity, size of virtual world environments data sets and privacy issues with virtual world companies and a number of other critical factors distinguish this paper from previous studies.

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Coupling direct inversion to common-shot image-domain velocity analysis

Geophysics ◽

10.1190/geo2017-0825.1 ◽

2018 ◽

Vol 83 (5) ◽

pp. R497-R514 ◽

Cited By ~ 2

Author(s):

Yubing Li ◽

Hervé Chauris

Keyword(s):

Objective Function ◽

Velocity Gradient ◽

Large Scale ◽

Synthetic Data ◽

Essential Element ◽

Velocity Model ◽

Data Sets ◽

Velocity Analysis ◽

Image Domain ◽

Direct Inversion

Migration velocity analysis is a technique used to estimate the large-scale structure of the subsurface velocity model controlling the kinematics of wave propagation. For more stable results, recent studies have proposed to replace migration, adjoint of Born modeling, by the direct inverse of the modeling operator in the context of extended subsurface-offset domain. Following the same strategy, we have developed a two-way-wave-equation-based inversion velocity analysis (IVA) approach for the original surface-oriented shot gathers. We use the differential semblance optimization (DSO) objective function to evaluate the quality of inverted images depending on shot positions and to derive the associated gradient, an essential element to update the macromodel. We evaluate the advantages and limitations through applications of 2D synthetic data sets, first on simple models with a single-reflector embedded in various background velocities and then on the Marmousi model. The direct inverse attenuates migration smiles by compensating for geometric spreading and uneven illuminations. We slightly modified the original DSO objective function to remove spurious oscillations around interface positions in the velocity gradient. These oscillations are related to the fact that the locations of events in the image domain depend on the macromodel. We pay attention to the presence of triplicated wavefields. It appears that IVA is robust even if artifacts are observed in the seismic migrated section. The velocity gradient leads to a stable update, especially after a Gaussian smoothing over a wavelength distance. Coupling common-shot direct inversion to velocity analysis offers new possibilities for the extension to 3D in the future.

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Localizing microseismic events on field data using a U-Net based convolutional neural network trained on synthetic data

Geophysics ◽

10.1190/geo2020-0868.1 ◽

2021 ◽

pp. 1-47

Author(s):

N. A. Vinard ◽

G. G. Drijkoningen ◽

D. J. Verschuur

Keyword(s):

Hydraulic Fracturing ◽

Field Data ◽

Synthetic Data ◽

Region Of Interest ◽

Velocity Model ◽

Mitigation Measures ◽

Large Field ◽

Data Sets ◽

Data Set ◽

Full Waveforms

Hydraulic fracturing plays an important role when it comes to the extraction of resources in unconventional reservoirs. The microseismic activity arising during hydraulic fracturing operations needs to be monitored to both improve productivity and to make decisions about mitigation measures. Recently, deep learning methods have been investigated to localize earthquakes given field-data waveforms as input. For optimal results, these methods require large field data sets that cover the entire region of interest. In practice, such data sets are often scarce. To overcome this shortcoming, we propose initially to use a (large) synthetic data set with full waveforms to train a U-Net that reconstructs the source location as a 3D Gaussian distribution. As field data set for our study we use data recorded during hydraulic fracturing operations in Texas. Synthetic waveforms were modelled using a velocity model from the site that was also used for a conventional diffraction-stacking (DS) approach. To increase the U-Nets ability to localize seismic events, we augmented the synthetic data with different techniques, including the addition of field noise. We select the best performing U-Net using 22 events that have previously been identified to be confidently localized by DS and apply that U-Net to all 1245 events. We compare our predicted locations to DS and the DS locations refined by a relative location (DSRL) method. The U-Net based locations are better constrained in depth compared to DS and the mean hypocenter difference with respect to DSRL locations is 163 meters. This shows potential for the use of synthetic data to complement or replace field data for training. Furthermore, after training, the method returns the source locations in near real-time given the full waveforms, alleviating the need to pick arrival times.

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Hierarchical cluster analysis and multiple event relocation of seismic event clusters in Hungary between 2000 and 2016

Journal of Seismology ◽

10.1007/s10950-019-09868-5 ◽

2019 ◽

Vol 23 (6) ◽

pp. 1313-1326 ◽

Cited By ~ 3

Author(s):

Barbara Czecze ◽

István Bondár

Keyword(s):

Cluster Analysis ◽

Hierarchical Cluster Analysis ◽

Seismic Event ◽

Pannonian Basin ◽

Hierarchical Cluster ◽

Velocity Model ◽

Double Difference ◽

Arrival Times ◽

Aftershock Sequences ◽

Waveform Cross Correlation

Abstract The objective of our paper is to develop a workflow that allows us to calculate more accurate hypocenter locations in seismic event clusters of aftershock sequences or artificial events. Due to the increased sensitivity of the seismological instruments and density of the network, we are able to record small natural and artificial events. The discrimination of these events is necessary to investigate the recent tectonic movements in the Pannonian Basin. As a first step, we performed a hierarchical cluster analysis on the events in the Hungarian National Seismological Bulletin using the spatial distances between the events to obtain event clusters. We selected 5 different test clusters from the list of clusters where two clusters consist of quarry blasts, another two consist of earthquakes, and the last one is a mixture of earthquakes and anthropogenic events. In the second step, to prepare for the double-difference multiple event location analysis, we manually revised the arrival time picks in the Hungarian National Seismological Bulletin in order to increase the consistency and accuracy of the arrival times. We obtained improved single-event locations with the iLoc algorithm using the RSTT 3D global velocity model to provide initial locations for the double-difference relocation. We applied waveform cross-correlation at every station to obtain the differential times and correlation matrices. In order to discriminate the events in the mixed event cluster, we repeated the hierarchical cluster analysis, but this time, we used the correlation matrix as a distance metric. Examining the shape of the resulting dendrogram, it is clear that certain subclusters are well separated. In these subclusters, the coordinates of the events are close to the mines, where explosive quarrying takes place. With this technique, we are able to identify explosions that were listed as earthquakes in the catalogue.

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