scholarly journals Quantitative imaging of water, ice and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data

2019 ◽  
Vol 219 (3) ◽  
pp. 1866-1875 ◽  
Author(s):  
F M Wagner ◽  
C Mollaret ◽  
T Günther ◽  
A Kemna ◽  
C Hauck
Keyword(s):  
Electrical Resistivity ◽  
Liquid Water ◽  
Field Data ◽  
Joint Inversion ◽  
A Priori ◽  
Seismic Refraction ◽  
Rock Matrix ◽  
Data Set ◽  
Water Ice ◽  
Borehole Temperatures

SUMMARY Quantitative estimation of pore fractions filled with liquid water, ice and air is crucial for a process-based understanding of permafrost and its hazard potential upon climate-induced degradation. Geophysical methods offer opportunities to image distributions of permafrost constituents in a non-invasive manner. We present a method to jointly estimate the volumetric fractions of liquid water, ice, air and the rock matrix from seismic refraction and electrical resistivity data. Existing approaches rely on conventional inversions of both data sets and a suitable a priori estimate of the porosity distribution to transform velocity and resistivity models into estimates for the four-phase system, often leading to non-physical results. Based on two synthetic experiments and a field data set from an Alpine permafrost site (Schilthorn, Bernese Alps and Switzerland), it is demonstrated that the developed petrophysical joint inversion provides physically plausible solutions, even in the absence of prior porosity estimates. An assessment of the model covariance matrix for the coupled inverse problem reveals remaining petrophysical ambiguities, in particular between ice and rock matrix. Incorporation of petrophysical a priori information is demonstrated by penalizing ice occurrence within the first two meters of the subsurface where the measured borehole temperatures are positive. Joint inversion of the field data set reveals a shallow air-rich layer with high porosity on top of a lower-porosity subsurface with laterally varying ice and liquid water contents. Non-physical values (e.g. negative saturations) do not occur and estimated ice saturations of 0–50 per cent as well as liquid water saturations of 15–75 per cent are in agreement with the relatively warm borehole temperatures between −0.5  and 3 ° C. The presented method helps to improve quantification of water, ice and air from geophysical observations.

Ice or rock matrix? Improved quantitative imaging of Alpine permafrost evolution through time-lapse petrophysical joint inversion

2021 ◽  
Author(s):  
Johanna Klahold ◽  
Christian Hauck ◽  
Florian Wagner
Keyword(s):  
Liquid Water ◽  
Joint Inversion ◽  
Time Lapse ◽  
Swiss Alps ◽  
Permafrost Degradation ◽  
Rock Matrix ◽  
Validation Data ◽  
Water Ice ◽  
Borehole Temperature ◽  
Ice Content

<p>Quantitative estimation of pore fractions filled with liquid water, ice and air is one of the prerequisites in many permafrost studies and forms the basis for a process-based understanding of permafrost and the hazard potential of its degradation in the context of global warming. The volumetric ice content is however difficult to retrieve, since standard borehole temperature monitoring is unable to provide any ice content estimation. Geophysical methods offer opportunities to image distributions of permafrost constituents in a non-invasive manner. A petrophysical joint inversion was recently developed to determine volumetric water, ice, air and rock contents from seismic refraction and electrical resistivity data. This approach benefits from the complementary sensitivities of seismic and electrical data to the phase change between ice and liquid water. A remaining weak point was the unresolved petrophysical ambiguity between ice and rock matrix. Within this study, the petrophysical joint inversion approach is extended along the time axis and respective temporal constraints are introduced. If the porosity (and other time-invariant properties like pore water resistivity or Archie exponents) can be assumed invariant over the considered time period, water, ice and air contents can be estimated together with a temporally constant (but spatially variable) porosity distribution. It is hypothesized that including multiple time steps in the inverse problem increases the ratio of data and parameters and leads to a more accurate distinction between ice and rock content. Based on a synthetic example and a field data set from an Alpine permafrost site (Schilthorn, Swiss Alps) it is demonstrated that the developed time-lapse petrophysical joint inversion provides physically plausible solutions, in particular improved estimates for the volumetric fractions of ice and rock. The field application is evaluated with independent validation data including thaw depths derived from borehole temperature measurements and shows generally good agreement. As opposed to the conventional petrophysical joint inversion, its time-lapse extension succeeds in providing reasonable estimates of permafrost degradation at the Schilthorn monitoring site without <em>a priori </em>constraints on the porosity model.</p>

Structurally constrained impedance inversion

2016 ◽  
Vol 4 (4) ◽  
pp. T577-T589 ◽  
Author(s):  
Haitham Hamid ◽  
Adam Pidlisecky
Keyword(s):  
Objective Function ◽  
Seismic Data ◽  
Field Data ◽  
A Priori ◽  
Structural Constraints ◽  
Inversion Algorithm ◽  
Data Set ◽  
Impedance Inversion ◽  
Single Trace ◽  
Complex Geology

In complex geology, the presence of highly dipping structures can complicate impedance inversion. We have developed a structurally constrained inversion in which a computationally well-behaved objective function is minimized subject to structural constraints. This approach allows the objective function to incorporate structural orientation in the form of dips into our inversion algorithm. Our method involves a multitrace impedance inversion and a rotation of an orthogonal system of derivative operators. Local dips used to constrain the derivative operators were estimated from migrated seismic data. In addition to imposing structural constraints on the inversion model, this algorithm allows for the inclusion of a priori knowledge from boreholes. We investigated this algorithm on a complex synthetic 2D model as well as a seismic field data set. We compared the result obtained with this approach with the results from single trace-based inversion and laterally constrained inversion. The inversion carried out using dip information produces a model that has higher resolution that is more geologically realistic compared with other methods.

Structural joint inversion on irregular meshes

2019 ◽  
Vol 220 (3) ◽  
pp. 1995-2008 ◽  
Author(s):  
C Jordi ◽  
J Doetsch ◽  
T Günther ◽  
C Schmelzbach ◽  
H Maurer ◽  
...  
Keyword(s):  
Electrical Resistivity ◽  
Joint Inversion ◽  
A Priori ◽  
Structural Similarity ◽  
P Wave ◽  
Parameter Distribution ◽  
Data Sets ◽  
Inversion Algorithm ◽  
Structural Joint ◽  
Irregular Meshes

SUMMARY Structural joint inversion of several data sets on an irregular mesh requires appropriate coupling operators. To date, joint inversion algorithms are primarily designed for the use on regular rectilinear grids and impose structural similarity in the direct neighbourhood of a cell only. We introduce a novel scheme for calculating cross-gradient operators based on a correlation model that allows to define the operator size by imposing physical length scales. We demonstrate that the proposed cross-gradient operators are largely decoupled from the discretization of the modelling domain, which is particularly important for irregular meshes where cell sizes vary. Our structural joint inversion algorithm is applied to a synthetic electrical resistivity tomography and ground penetrating radar 3-D cross-well experiment aiming at imaging two anomalous bodies and extracting the parameter distribution of the geostatistical background models. For both tasks, joint inversion produced superior results compared with individual inversions of the two data sets. Finally, we applied structural joint inversion to two field data sets recorded over a karstified limestone area. By including geological a priori information via the correlation-based operators into the joint inversion, we find P-wave velocity and electrical resistivity tomograms that are in accordance with the expected subsurface geology.

Oriented surface passive seismic location using local slopes

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. KS13-KS25 ◽  
Author(s):  
M. Javad Khoshnavaz ◽  
Kit Chambers ◽  
Andrej Bóna ◽  
Milovan Urosevic
Keyword(s):  
Field Data ◽  
Focal Point ◽  
A Priori ◽  
Velocity Model ◽  
Microseismic Monitoring ◽  
Data Set ◽  
Effective Velocity ◽  
Event Location ◽  
Receiver Arrays ◽  
Layered Models

A common acquisition scenario in microseismic monitoring is the deployment of large areal receiver arrays at or near the surface. This recording geometry has the advantage of providing coverage of the source’s focal hemisphere as well as characterization of the arrival time moveout curve; however, the accuracy of many location techniques applied to these data sets depends on the accuracy of the depth velocity model provided prior to location. We have developed a simple oriented time-domain location technique so that full knowledge of the velocity model is not required a priori. The applicability of the technique is limited to horizontally layered models and also to models with dipping interfaces of small angles; however, this restriction is acceptable in many unconventional reservoirs. Implementation of the technique includes three steps: (1) smoothing of the observed time arrivals by fitting a hyperbolic moveout curve with a broad set of constraints, (2) updating and restricting the constraints using a local-slopes-based location workflow, and (3) estimation of the focal coordinates of passive sources using the updated constraints for the final least-squares fitting of the moveout curves. We have tested the performance of the proposed technique on several 2D examples and a 3D field data set. The results from synthetic examples suggest that, despite the assumption of the method that the arrival moveout can be modeled using a constant effective velocity, a reliable event location is achieved for layered models without considerable lateral heterogeneities. Our tests on the field data set find that the focal point coincides with a previously derived estimate of the source location. To assess the uncertainty of the proposed technique, bootstrap statistics was also used and applied to the field data set.

Joint inversion of Rayleigh-wave dispersion and P-wave refraction data for laterally varying layered models

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. EN49-EN59 ◽  
Author(s):  
Daniele Boiero ◽  
Laura Valentina Socco
Keyword(s):  
Rayleigh Wave ◽  
Joint Inversion ◽  
A Priori ◽  
P Wave ◽  
Inversion Method ◽  
Model Parameters ◽  
Data Set ◽  
Wave Refraction ◽  
Velocity Models ◽  
Refraction Data

We implemented a joint inversion method to build P- and S-wave velocity models from Rayleigh-wave and P-wave refraction data, specifically designed to deal with laterally varying layered environments. A priori information available over the site and any physical law to link model parameters can be also incorporated. We tested and applied the algorithm behind the method. The results from a field data set revealed advantages with respect to individual surface-wave analysis (SWA) and body wave tomography (BWT). The algorithm imposed internal consistency for all the model parameters relaxing the required a priori assumptions (i.e., Poisson’s ratio level of confidence in SWA) and the inherent limitations of the two methods (i.e., velocity decreases for BWT).

Quantification of ground ice through petrophysical joint inversion of seismic and electrical data applied to alpine permafrost

2020 ◽  
Author(s):  
Coline Mollaret ◽  
Florian M. Wagner ◽  
Christin Hilbich ◽  
Christian Hauck
Keyword(s):  
Electrical Resistivity ◽  
Melting Point ◽  
Joint Inversion ◽  
Geophysical Methods ◽  
Ground Truth ◽  
Ground Ice ◽  
Ground Truth Data ◽  
Water Ice ◽  
Refraction Seismic ◽  
Ice Content

<p>Quantification of ground ice is particularly crucial for understanding permafrost systems. The volumetric ice content is however rarely estimated in permafrost studies, as it is particularly difficult to retrieve. Geophysical methods have become more and more popular for permafrost investigations due to their capacity to distinguish between frozen and unfrozen regions and their complementarity to standard ground temperature data. Geophysical methods offer both a second (or third) spatial dimension and the possibility to gain insights on processes happening near the melting point (ground ice gain or loss at the melting point). Geophysical methods, however, may suffer from potential inversion imperfections and ambiguities (no unique solution). To reduce uncertainties and improve the interpretability, geophysical methods are standardly combined with ground truth data or other independent geophysical methods. We developed an approach of joint inversion to fully exploit the sensitivity of seismic and electrical methods to the phase change of water. We choose apparent resistivities and seismic travel times as input data of a petrophysical joint inversion to directly estimate the volumetric fractions of the pores (liquid water, ice and air) and the rock matrix. This approach was successfully validated with synthetic datasets (Wagner et al., 2019). This joint inversion scheme warrants physically-plausible solutions and provides a porosity estimation in addition to the ground ice estimation of interest. Different petrophysical models are applied to several alpine sites (ice-poor to ice-rich) and their advantages and limitations are discussed. The good correlation of the results with the available ground truth data (thaw depth and ice content data) demonstrates the high potential of the joint inversion approach for the typical landforms of alpine permafrost (Mollaret et al., 2020). The ice content is found to be 5 to 15 % at bedrock sites, 20 to 40 % at talus slopes, and up to 95 % at rock glaciers (in good agreement to the ground truth data from boreholes). Moreover, lateral variations of bedrock depth are correctly identified according to outcrops and borehole data (as the porosity is also an output of the petrophysical joint inversion). A time-lapse version of this petrophysical joint inversion may further reduce the uncertainties and will be beneficial for monitoring and modelling studies upon climate-induced degradation.</p><p> </p><p>References:</p><p>Mollaret, C., Wagner, F. M. Hilbich, C., Scapozza, C., and Hauck, C. Petrophysical joint inversion of electrical resistivity and refraction seismic applied to alpine permafrost to image subsurface ice, water, air, and rock contents. Frontiers in Earth Science, 2020, submitted.</p><p>Wagner, F. M., Mollaret, C., Günther, T., Kemna, A., and Hauck, C. Quantitative imaging of water, ice, and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data. Geophysical Journal International, 219 (3):1866–1875, 2019. doi:10.1093/gji/ggz402.</p>

Structural joint inversion coupled with Euler deconvolution of isolated gravity and magnetic anomalies

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. G67-G79 ◽  
Author(s):  
Emilia Fregoso ◽  
Luis A. Gallardo ◽  
Juan García-Abdeslem
Keyword(s):  
Joint Inversion ◽  
Structural Characteristics ◽  
A Priori ◽  
Real Field ◽  
Vertical Position ◽  
Euler Deconvolution ◽  
Data Set ◽  
Gravity And Magnetic ◽  
Structural Joint ◽  
Northwest Region

We generalized the Euler deconvolution method to a joint scheme, which consists of locating the horizontal and vertical positions of the top of potential-field 3D sources. These results were then used to constrain the depth to the top of the models obtained by cross-gradient joint 3D inversions, imposing fixed known values in the a priori models. The coupling of both methods produced more realistic density and magnetization models for separate and joint inversions, relative to those obtained by applying cross-gradient joint inversion only. This strategy was tested on a 3D synthetic experiment, and on a real field data set from the northwest region of the Baja California Peninsula, Mexico. After locating the vertical position of the source, the algorithm uses this information to obtain density and magnetization models that enhanced their structural compatibility and reduces the ambiguity on the interpretation of their structural characteristics laterally and at surface.

Structurally tailored 3D anisotropic controlled-source electromagnetic resistivity inversion with cross-gradient criterion and simultaneous model calibration

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. E387-E402 ◽  
Author(s):  
Max A. Meju ◽  
Randall L. Mackie ◽  
Federico Miorelli ◽  
Ahmad Shahir Saleh ◽  
Roger V. Miller
Keyword(s):  
Field Data ◽  
A Priori ◽  
Calibration Data ◽  
A Priori Information ◽  
Data Set ◽  
Low Contrast ◽  
Controlled Source ◽  
Low Resistivity ◽  
Band Limited ◽  
Priori Information

Geologic interpretation of 3D anisotropic resistivity models from conventional marine controlled-source electromagnetic (CSEM) data inversion faces difficulties in low-resistivity contrast sediments and structurally complex environments that typify the new frontiers for hydrocarbon exploration. Currently, the typically reconstructed horizontal resistivity [Formula: see text] and vertical resistivity [Formula: see text] models often have conflicting depth structures that are difficult to explain in terms of subsurface geology, and the resulting resistivities may not be close to the true formation resistivities required for estimating reservoir parameters. We have investigated the concept that an objective geologically oriented or structurally tailored inversion can be achieved by requiring that the cross-product of the gradient of horizontal resistivity and the gradient of the vertical resistivity is equal to zero at significant geologic boundaries. We incorporate this boundary-shape criterion in our 3D inverse problem formulations, implemented within nonlinear model-space and conjugate-gradient contexts, for cases in which a priori calibration data from wells and/or seismically derived subsurface boundaries are available and for cases in which these are lacking. The resulting fit-for-purpose solutions serve to better analyze the peculiarity of a given data set. We applied these algorithms to synthetic and field CSEM data sets representing a fold-thrust environment with low-resistivity and low-contrast sediments. The resulting [Formula: see text] and [Formula: see text] models from cross-gradient joint inversion of synthetic data of appropriate frequency bandwidth without a priori information are structurally similar and consistent with the test models, whereas those from the inversions of band-limited field data are consistent with the available seismic and resistivity well-log data. This particular approach will thus be useful for lithologic correlation in frontier regions with limited a priori information using broadband CSEM data. For these band-limited field data, we found that the anisotropic bulk resistivities of the low-contrast sediments are better determined by incorporating a priori calibration data from triaxial resistivity logs and seismic horizons.

scholarly journals Joint Inversion of GPR and ER Data

2020 ◽  
Author(s):  
Diego Domenzain
Keyword(s):  
Spatial Frequency ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Joint Inversion ◽  
A Priori ◽  
Waveform Inversion ◽  
High Spatial Frequency ◽  
Seismic Exploration ◽  
Frequency Content ◽  
Resource Monitoring

Imaging the subsurface can shed knowledge on important processes needed in a modern day human's life such as ground-water exploration, water resource monitoring, contaminant and hazard mitigation, geothermal energy exploration and carbon dioxide storage. As computing power expands, it is becoming ever more feasible to increase the physical complexity of Earth's exploration methods, and hence enhance our understanding of the subsurface. We use non-invasive geophysical active source methods that rely on electromagnetic fields to probe the depths of the Earth. In particular, we use Ground penetrating radar (GPR) and Electrical resistivity (ER). Both methods are sensitive to electrical conductivity while GPR is also sensitive to electrical permittivity. We combine both types of data and let the different physical sensitivities of both methods cooperate in order to account for non-uniqueness of the subsurface image. Full-waveform inversion (FWI) of GPR is a promising technique for recovering permittivity and conductivity of the subsurface by using the full response of the electromagnetic wave. While many advances have been made to FWI by the seismic exploration community, using FWI on GPR surface acquired data is a young and growing field of research. Using the full response of ER data is a more common practice in the geophysical community. However, the spatial resolution of the recovered conductivity lacks high spatial-frequency content due to the inherent sensitivity of the data. Fortunately, the sensitivities of GPR and ER are complimentary. GPR is sensitive to conductivity through reflection and attenuation while ER is directly sensitive to conductivity. GPR is sensitive to high spatial-frequency content while ER is sensitive to low spatial-frequency content. We present a novel non-linear joint inversion that iteratively combines the sensitivities of both GPR and ER surface acquired data. Our algorithm uses both GPR and ER sensitivities in order to effectively alleviate the non-uniqueness of the recovered electrical parameters. We join GPR and ER sensitivities within the same computational grid and without the need of petrophysical relationships. By further assuming structural similarities between permittivity and conductivity, we are able to relax a priori assumptions about the subsurface and accurately recover parameters in regions where the GPR data has a signal-to-noise ratio close to one. Furthermore, assuming a good initial model is available our algorithm makes no assumption of the underlying geometry. The demanding computing requirements of GPR-FWI entail an unfeasible amount of memory for existing ER inversion methods. This is due to the very fine discretization of the subsurface required by GPR-FWI. We develop a 2.5d ER adjoint method inversion that is capable of recovering accurate subsurface conductivity from field data and relaxes the amount of required memory. We test our method on field data from an alluvial aquifer site and find agreeable results with existing measurements in the literature. Having feasible computational methods for both GPR and ER inversions is an important step for using our joint inversion algorithms on field data.

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