1. Joint Inversion of Crosshole GPR and Seismic Traveltime Data

Linde, Niklas; Doetsch, Joseph

doi:10.1190/1.9781560802259.ch1

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…Geostatistical regularization (Jordi et al, ; Linde & Doetsch, ), denoted as

\overset{{boldC}_{bolds}}{true}

, is used to add constraints to the given inverse problem. Similar to equation , the stochastic regularization operator

bold Ĉ_{s}

is a diagonal block matrix.…”

Section: Active Seismic Monitoringmentioning

confidence: 99%

“…Similar to equation , the stochastic regularization operator

bold Ĉ_{s}

is a diagonal block matrix. Stochastic operators are typically based on exponential covariance functions with integral scales that define the spatial correlation of the individual model parameters (Linde & Doetsch, ). The stochastic regularization operator is scaled by a factor of

1 0^{λ}

with

λ \approx 2.1

to fit the inverted models to the error level of the data.…”

Section: Active Seismic Monitoringmentioning

confidence: 99%

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On the Variability of Pressure Propagation During Hydraulic Stimulation Based on Seismic Velocity Observations

et al. 2020

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Permeability enhancement of a reservoir through hydraulic stimulation is ever so often accompanied by potentially hazardous induced seismicity. Monitoring of in situ pressure propagation remains an important step in understanding the underlying seismo‐hydromechanical processes during hydraulic stimulation and mitigating hazardous induced seismicity. In an effort to monitor pressure propagation remotely, active seismic monitoring was performed during decameter‐scale hydraulic stimulation experiments at the Grimsel Test Site in Switzerland. Using cross‐correlation schemes, subtle traveltime variations that correlate well with the high‐pressure fluid injections during hydraulic stimulation become apparent. The 4‐D seismic tomograms obtained through time‐lapse differential traveltime inversion reveal transient changes in the seismic velocity within the stimulated volume that depend on the effective stress. In‐depth comparison of the seismic velocity variations and measured pressure data indicates that the seismic velocity variations can be used as a proxy for pressure propagation within an equilibrated system. Observations show that traveltime tomography is insensitive to isolated, high‐pressure variations such as a breakthrough within the fracture network. Joint interpretation of the 4‐D seismic tomograms with geological and hydromechanical data reveals fundamental differences between the different experiments due to a large variability in the rock mass response to the hydraulic stimulation. Using the observed velocity variations as a proxy for pressure propagation, we can infer characteristics of the shear zones and the rock volume at the Grimsel Test Site. The observed velocity variations provide not only information on the characteristics of the reservoir itself but also its response to different stimulation techniques.

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“…Geostatistical regularization (Jordi et al, ; Linde & Doetsch, ), denoted as

\overset{{boldC}_{bolds}}{true}

, is used to add constraints to the given inverse problem. Similar to equation , the stochastic regularization operator

bold Ĉ_{s}

is a diagonal block matrix.…”

Section: Active Seismic Monitoringmentioning

confidence: 99%

“…Similar to equation , the stochastic regularization operator

bold Ĉ_{s}

1 0^{λ}

with

λ \approx 2.1

to fit the inverted models to the error level of the data.…”

Section: Active Seismic Monitoringmentioning

confidence: 99%

On the Variability of Pressure Propagation During Hydraulic Stimulation Based on Seismic Velocity Observations

et al. 2020

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“…Similarly, joint inversion could be used to (i) better understand field‐scale petrophysical relationships (e.g., Linde & Doetsch, ) and to (ii) enable clustering into zones of distinct soil structure. One way to achieve this is joint inversion using structural constraints in which no petrophysical relationship is imposed (Doetsch et al, ; Gallardo & Meju, ).…”

Section: Opportunities For Geophysical Soil Structure Characterizationmentioning

confidence: 99%

A Review of Geophysical Methods for Soil Structure Characterization

Romero-Ruiz

Linde

Keller

et al. 2018

Reviews of Geophysics

Self Cite

129

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The growing interest in the maintenance of favorable soil structure is largely motivated by its central role in plant growth, soil ecological functioning, and impacts on surface water and energy fluxes. Soil structure pertains to the spatial arrangement of voids and solid constituents, their aggregation, and mechanical state. As a fragile product of soil biological activity that includes invisible ingredients (mechanical and ecological states), soil structure is difficult to define rigorously, and measurements of relevant metrics often rely on core samples or on episodic point measurements. The presence of soil structure has not yet been explicitly incorporated in climate and Earth systems models, partially due to incomplete methodological means to characterize it at relevant scales and to parameterize it in spatially extensive models. We seek to review the potential of harnessing geophysical methods to fill the scale gap in characterization of soil structure directly (via impact of soil pores, transport, and mechanical properties on geophysical signals) or indirectly by measurement of surrogate variables (wetness and rates of drainage). We review basic aspects of soil structure and challenges of characterization across spatial and temporal scales and how geophysical methods could be used for the task. Additionally, we propose the use of geophysical models, inversion techniques, and combination of geophysical methods for extracting soil structure information at previously unexplored spatial and temporal scales.

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“…This is because of its predictive abilities (KVAMME 2006) in subsurface target detection using scattered fields (CHATURVEDI and PLUMB 1995). In the joint inversion method, an external constraint is imposed on the models (e.g., PRIDE 1994;TRYGGVASON et al 2002;LINDE et al 2006;LINDE and DOETSCH 2010) as the approach seeks an existing empirical or mathematical relationship between the models, whereas the image classification technique looks for a natural grouping/pattern in the data sets.…”

Section: Image Classification Techniquesmentioning

confidence: 99%

Combining Multiple Electrode Arrays for Two-Dimensional Electrical Resistivity Imaging Using the Unsupervised Classification Technique

Ishola

Nawawi

Abdullah

2014

Pure Appl. Geophys.

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This article describes the use of k-means clustering, an unsupervised image classification technique, to help interpret subsurface targets. The k-means algorithm is employed to combine and classify the two-dimensional (2D) inverse resistivity models obtained from three different electrode arrays. The algorithm is initialized through the selection of the number of clusters, number of iterations and other parameters such as stopping criteria. Automatically, it seeks to find groups of closely related resistivity values that belong to the same cluster and are more similar to each other than resistivity values belonging to other clusters. The approach is applied to both synthetic and field data. The 2D postinversions of the resistivity data were preprocessed by resampling and interpolating to the same coordinate. Following the preprocessing, the three images are combined into a single classified image. All the image preprocessing, manipulation and analysis are performed using the PCI Geomatics software package. The results of the clustering and classification are presented as classified images. An assessment of the performance of the individual and combined images for the synthetic models is carried out using an error matrix, mean absolute error and mean absolute percent error. The estimated errors show that images obtained from maximum values of the reconstructed resistivity for the different models give the best representation of the true models. Additionally, the overall accuracy and kappa values show good agreement between the combined classified images and true models. Depending on the model, the overall accuracy ranges from 86 to 99 %, while the kappa coefficient is in the range of 54-98 %. Classified images with kappa coefficients greater than 0.8 show strong agreement, while images with kappa coefficients greater than 0.5 but less than 0.8 give moderate agreement. For the field data, the k-mean classifier produces images that incorporate structural features of the three electrode array configurations. Consequently, some clusters that overwhelmingly correspond to the lithologic units of the investigated areas are better identified than the tomographic images of each data set considered separately, underscoring the relevance of the unsupervised classification technique in this study.

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1. Joint Inversion of Crosshole GPR and Seismic Traveltime Data

Cited by 9 publications

References 51 publications

On the Variability of Pressure Propagation During Hydraulic Stimulation Based on Seismic Velocity Observations

On the Variability of Pressure Propagation During Hydraulic Stimulation Based on Seismic Velocity Observations

A Review of Geophysical Methods for Soil Structure Characterization

Combining Multiple Electrode Arrays for Two-Dimensional Electrical Resistivity Imaging Using the Unsupervised Classification Technique

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