Simultaneous Joint Inversion Research Articles

Evaluation of the Possibilities Validation of Interval Velocity Models Using Non-Seismic Data and Its Impact on Geological Interpretation of PreSDM Results

The paper presents the problem of generation and validation of Velocity Interval Depth (VID) models with the application of non-seismic geophysical and geological data. The study area is a part of the Carpathian Foredeep located close to its contact with the Carpathian Overthrust. In this area of complicated geological structure, hydrocarbon deposits have been successfully explored for decades with seismic methods and drilling. The research applied the Simultaneous Joint Inversion (SJI) of independent geophysical data, which is a modern methodology of geophysical data processing, that is still under development. Such an attempt was necessary due to the lack of a sufficiently dense grid of wells in the study area, in which seismic velocities would be correctly recorded. Such data would be then applied for the generation of relevant VID models, which in turn, could be used to perform the Prestack Depth Migration (PreSDM) procedures. The application of procedures taking advantage of independent geophysical and geological data enabled researchers to control the generation process of the spatial VID model in the areas without wells. The analyses aimed to verify the correctness of VID model evaluation and its influence on the quality of seismic imaging in the area of the Carpathian Overthrust. Precisely, the influence was tested of such non-standard generation procedure of seismic velocity fields, not only on the PreSDM results but also on the geological interpretation of both the Rączyna and the Jodłówka gas deposits. The latter aspect of the presented results seems to be crucial to the effectiveness of petroleum exploration in the transition zone between the Carpathian Orogen and the Carpathian Foredeep.

Simultaneous Joint Inversion of Gravity and Self-Potential Data Measured Along Profile: Theory, Numerical Examples, and a Case Study From Mineral Exploration With Cross Validation From Electromagnetic Data

This article describes a robust simultaneous joint inversion scheme for the interpretation of gravity and self-potential data measured along the profile. The developed scheme jointly inverts the two data sets of the causative targets by some geometrically simple idealized bodies (the so-called approximative/interpretive models) in the restricted class of spheres and cylinders. It simultaneously recovers the characteristic inverse parameters of all interpretive bodies (that is, the depth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> , amplitude coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> , electric dipole moment <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> , and polarization angle <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> of each body). It employs the Gauss–Newton (GN) method in the space of the logarithmed and nonlogarithmed model parameters ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log (|A|)$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log (K)$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log (z)$ </tex-math></inline-formula> of each body) (rather than in the space of the model parameters themselves ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> )) to maintain the convergence. The scheme has been successfully validated on a number of noise-free numerical examples, after which the accuracy and stability of the scheme have been carefully assessed on various noisy data. The influence of the scaling factors of the objective functional subjected to minimization on the convergence of the GN method and on the nonuniqueness of the approximative solution that describes and resembles the underlying buried targets has been investigated. It has been found that the developed scheme is very robust and capable of extracting accurate and useful information that is of some significance in mineral exploration. Finally, the scheme has been applied to a real data example from Germany. Careful analysis of this case study suggests new results that are of some value in mining geophysics.

Hidden layer imaging using joint inversion of P‐wave travel‐time and electrical resistivity data

ABSTRACTThe combination of geophysical surface‐based imaging techniques, including seismic and electrical resistivity tomography (ERT), is now common practice to obtain a more accurate characterization of subsurface structures. Due to model non‐uniqueness and geological heterogeneity, conventional travel‐time tomography cannot solely reveal hidden layers (i.e., low‐velocity zones embedded between layers of higher velocities) in the subsurface. Hence, we present a joint inversion algorithm based on a normalized cross‐gradients function to detect hidden low‐velocity layers. The structural similarity between P‐wave velocity (Vp) and resistivity fields is enhanced by incorporating the normalized cross‐gradients constraint in the joint inversion algorithm. Improved structural similarity can mitigate the problem of the recovery of a hidden layer. We also take advantage of a priori information derived from borehole geological data to reduce the continuous range of possible solutions (i.e., exact‐data non‐uniqueness). In both joint and separate inversions, an auxiliary damping factor is used to ensure convergence, and also the smoothness constraints are applied to deal with instability stemming from error in the data. To verify the performance of the joint inversion procedure, the algorithm is tested on synthetic and real data examples with emphasis on hidden low‐velocity layer detection. Numerical experiments demonstrate that the joint inversion strategy can produce more reliable and better velocity models of the subsurface structures as compared with those obtained through individual inversions. We conclude that this simultaneous joint inversion of Vp and ERT integrates the best of both schemes and makes it possible to improve resolution, and, hence, reduces uncertainties in hidden low‐velocity layer problems.

Inversion of multiple data sets acquired by different array configuration of geoelectrical resistivity method

The geoelectrical resistivity method is one of the most commonly used geophysical methods. This method uses different electrodes configuration, electrode array, depending on the purpose and conditions of the field, each type of array has its advantages and disadvantages. Due to the development of data acquisition technology, it is common for geoelectrical instruments enable to record data arising from different electrode arrays with negligible real-time construction. However, current software’s only allows to process for each individual electrode array. Inverted models of different electrode array can be integrated to build a common earth model. However, due to the nature of the geophysical inversion is non-unique solutions, it means that there will be an infinite of models that can be suitable for a measurement in a certain noise level. This leads to the same measurement data in an area with different electrode array may produce different geoelectrical models making the dificulty for integration process. To solve this problem, we utilise the simultaneous joint inversion algorithm of data sets arising from multiple electrode arrays. The test results on synthetic data show that this combination is better than the solution of each individual electrode array. The best result is a combination of pole - dipole (PD), dipole - pole (DP) and dipole - dipole (DD).

Individual and joint 2-D elastic full-waveform inversion of Rayleigh and Love waves

We investigate the performance of the individual 2-D elastic full-waveform inversion (FWI) of Rayleigh and Love waves as well as the feasibility of a simultaneous joint FWI of both wave types. The FWI of surface waves can provide a valuable contribution to near-surface investigations, since they are mainly sensitive to the S-wave velocity and hold a high signal-to-noise ratio. In synthetic reconstruction tests we compare the performance of the individual wave type inversions and explore the benefits of a simultaneous joint inversion. In these tests both individual wave type inversions perform similarly well, given that the initial P-wave velocity model is accurate enough. In this case the joint FWI further improves the result. For an inaccurate initial P-wave velocity model, we observe artifacts in the results of the Rayleigh wave FWI and the joint FWI. Subsequently, we recorded a near-surface field dataset to verify the results by a realistic example. In the field data application the Love wave FWI is superior to the Rayleigh wave FWI, possibly due to the initial P-wave velocity model. Also in this case the joint FWI further improves the inversion result.

Sequential and simultaneous joint inversion of resistivity and IP sounding data using particle swarm optimization

In order to interpret the vertical electrical sounding data more reliably and effectively in the case of lacking proper priori information, two inverse schemes are proposed to invert combined resistivity and induced polarization data by using particle swarm optimization technique. Based on the computational formula of induced polarization, the inversion for chargeability/polarizability data can be transformed into inverting equivalent resistivity data. Then, the inversion for combined data can be decomposed into two procedures: inverting resistivity data and inverting equivalent resistivity data. A sequential inversion scheme is presented to run the two procedures sequentially. Contrast to the sequential scheme, a simultaneous one is proposed to invert resistivity and induced polarization data simultaneously. Both the sequential and simultaneous schemes are performed via centered-progressive particle swarm optimization algorithm for more exploratory purpose. Numerical experiments show that both the designed inversion algorithms can invert resistivity and induced polarization data successfully with fast convergence and high accuracy, even performed in a large search space. The inverse results are comparable to the results from generalized linear method. As an approximate importance sampler, the particle swarm optimization based algorithm can provide posterior analysis conveniently. We employ the posterior probability distributions of inverted model parameters to evaluate the performance and uncertainty of inversion. The posterior analysis and further field data testing show that the proposed inversion algorithms perform good sampling of the equivalence region and make sure that the global optimum can locate in the high probability areas.

Near-surface modeling: A multiphysics approach

With the growth in geographic scale of land seismic exploration, increasingly complex near-surface modeling capabilities are being employed for static corrections in data processing. Variations in subsurface properties can be detected by gravity and electromagnetic measurements. The principles of complementarity and of noncontradiction are employed to integrate the diverse sources of information in a cooperative or simultaneous joint inversion formalism. In common practice, geophysical methods are subject to equivalency in solution. Also, inversions of first breaks are based on the user's interpretation of early arrivals, and, therefore, they are exposed to systematic errors, especially if picking is automated as for large 3D data sets. In such circumstances, it is desirable to avoid overfitting of observations in inversion. Velocity models developed through a multiphysics approach do not suffer from velocity-depth ambiguity and provide a shallow model that is well protected from the influence of inaccuracy in seismic picking.

A Global Integration Platform for Optimizing Cooperative Modeling and Simultaneous Joint Inversion of Multi-domain Geophysical Data

This paper reviews the theoretical aspects and the practical issues of different types of geophysical integration approaches. Moreover it shows how these approaches can be combined and optimized into the same platform. We discuss both cooperative modeling and Simultaneous Joint Inversion (SJI) as complementary methods for integration of multi-domain geophysical data: these data can be collected at surface (seismic, electromagnetic, gravity) as well as in borehole (composite well logs). The main intrinsic difficulties of any SJI approach are the high computational requirements, the non-uniqueness of the final models, the proper choice of the relations between the different geophysical domains, the quantitative evaluation of reliability indicators. In order to face efficiently all these problems we propose and describe here a “systemic approach”: the algorithms of modeling and SJI are merged with an integration architecture that permits the selection of workflows and links between different algorithms, the management of data and models coming from different domains, the smart visualization of partial and final results. This Quantitative Integration System (QUIS) has been implemented into a complex software and hardware platform, comprising many advanced codes working in cooperation and running on powerful computer clusters. The paper is divided into two main parts. First we discuss the theoretical formulation of SJI and the key concepts of the QUIS platform. In the second part we present a synthetic SJI test and a case history of QUIS application to a real exploration problem.

A Global Integration Platform for Optimizing Cooperative Modeling and Simultaneous Joint Inversion of Multi-domain Geophysical Data

A Global Integration Platform for Optimizing Cooperative Modeling and Simultaneous Joint Inversion of Multi-domain Geophysical Data

Using nonseismic data to aid velocity model building and complex imaging: A case study from western China

A workflow for inverting high-velocity conglomerates by simultaneous joint inversion (SJI) of seismic and nonseismic data was developed during a depth-migration project using land 3D data sets from western China. Large localized velocity variations caused by conglomerates severely limited the effectiveness of ray-based seismic tomography in generating an accurate velocity field. Single-domain inversion indicated different responses of particular lithologies for various nonseismic methods, and well-log crossplots enabled estimation of the relationships between models generated from different geophysical measurements. Using the high-resistivity property of the conglomerates, SJI of seismic first-break and magnetotelluric (MT) data was conducted to estimate spatial thickness and velocity. Combining the relatively low-resolution information derived from the MT data with the higher-resolution seismic image, the distribution of conglomerates could be interpreted with the help of well markers and well-logging horizons. The velocity model then was adjusted iteratively. The model that was updated by using the SJI workflow showed better consistency with borehole data compared with the initial tomographic model. The depth-migrated image created by using the updated model showed more geologically plausible structures both at target level and in the overburden.

Integrated Interpretation and Simultaneous Joint Inversion of 3D Marine CSEM and Seismic Datasets

One of the most complex seismic challenges is the imaging of thick salt bodies, the detection of their base and flanks, and imaging underlying units. To achieve good seismic imaging, the complementary use of non-seismic methods is one of the recommended solutions. Electromagnetic (EM) methods, such as magnetotellurics (MT) and controlled source electromagnetics (CSEM) are sensitive to the presence of salt bodies thanks to the high resistivity contrast with respect to sedimentary units. We present an integrated workflow applied to re-image wide azimuth (WAZ) seismic data acquired by Schlumberger using EM data acquired by EMGS over 35 blocks in the Keathley Canyon, in the Gulf of Mexico to reduce risk in exploration decisions and improve seismic deliverables. Seismic and EM data are utilized first in a cooperative workflow through localized seismic imaging reverse time migration (LSI RTM) to validate new salt structures highlighted by the single domain 3D anisotropic CSEM and MT inversions. They are then fed into a simultaneous joint inversion (SJI) to update a multi-property earth model (velocity and resistivity) by jointly minimizing the CSEM data misfit, the seismic residual move-outs and a relationship between the two properties.

Improved estimation of P-wave velocity, S-wave velocity, and attenuation factor by iterative structural joint inversion of crosswell seismic data

We present an iterative joint inversion approach for improving the consistence of estimated P-wave velocity, S-wave velocity and attenuation factor models. This type of inversion scheme links two or more independent inversions using a joint constraint, which is constructed by the cross-gradient function in this paper. The primary advantages of this joint inversion strategy are: avoiding weighting for different datasets in conventional simultaneous joint inversion, flexible for incorporating prior information, and relatively easy to code. We demonstrate the algorithm with two synthetic examples and two field datasets. The inversions for P- and S-wave velocity are based on ray traveltime tomography. The results of the first synthetic example show that the iterative joint inversion take advantages of both P- and S-wave sensitivity to resolve their ambiguities as well as improve structural similarity between P- and S-wave velocity models. In the second synthetic and field examples, joint inversion of P- and S-wave traveltimes results in an improved Vs velocity model that shows better structural correlation with the Vp model. More importantly, the resultant VP/VS ratio map has fewer artifacts and is better correlated for use in geological interpretation than the independent inversions. The second field example illustrates that the flexible joint inversion algorithm using frequency-shift data gives a structurally improved attenuation factor map constrained by a prior VP tomogram.

Simultaneous Structure-Coupled Joint Inversion of Gravity and Magnetic Data Based on a Damped Least-Squares Technique

The structure-coupled joint inversion method of gravity and magnetic data is a powerful tool for developing improved physical property models with high resolution and compatible features; however, the conventional procedure is inefficient due to the truncated singular values decomposition (SVD) process at each iteration. To improve the algorithm, a technique using damped leastsquares is adopted to calculate the structural term of model updates, instead of the truncated SVD. This produces structural coupled density and magnetization images with high efficiency. A so-called coupling factor is introduced to regulate the tuning of the desired final structural similarity level. Synthetic examples show that the joint inversion results are internally consistent and achieve higher resolution than separated. The acceptable runtime performance of the damped least squares technique used in joint inversion indicates that it is more suitable for practical use than the truncated SVD method.

Improvements in land seismic static calculation via simultaneous joint inversion and integrated earth modeling

A geological complex near-surface traditionally yields poor quality first arrival picks in land sesmic surveys making use of vibroseis sources. Uphole surveys can reduce the uncertainty, but are costly to undertake. Simultaneous joint inversion used as a static solver in combination with non-seismic data can reduce the number of uphole shots and improve the computed near- surface static solution.

Simultaneous joint inversion of amplitude-versus-offset and controlled-source electromagnetic data by implicit representation of common parameter structure

I evaluated a simultaneous joint inversion of seismic amplitude-versus-offset (AVO) and controlled-source electromagnetic (CSEM) data for fluid-flow monitoring. A new approach for structure-coupled joint inversion was presented, in which the coupling of the two data types was obtained by allowing for the direct identification of parameter structure that is shared by the different geophysical model parameters. The main idea was to use a composite parameter representation, which enables inversion with respect to the parameter magnitude and parameter structure. In the current application, parameter structure refers to transitions between dominating property values and is represented by the position and shape of the flooding front. Hence, with this approach, the position and shape of the flooding front are inverted for directly, and the coupling between the different data sets is obtained without the inclusion of an additional penalizing term in the objective function. Regularization of the inverse problem is obtained by using a flexible parameterization grid adapted to the resolution power of the available data. This approach is especially suited for problems in which the prior information is limited or highly uncertain. The solution approach is illustrated for two types of coupling: (1) identification of fluid saturation using rock-physics modeling and (2) for structure-coupled joint inversion with respect to P-wave velocity and electric conductivity. Through various synthetic examples in 2D, the proposed approach showed its efficiency for identifying the main features of the fluid distribution within the reservoir. Simultaneously inverting AVO and CSEM data was further seen to give results that were more robust with respect to certain random and more systematic (modeling) errors compared with inverting the data sets separately.

Generalized joint inversion of multimodal geophysical data using Gramian constraints

We introduce a new approach to the joint inversion of multimodal geophysical data using Gramian spaces of model parameters and Gramian constraints, computed as determinants of the corresponding Gram matrices of the multimodal model parameters and/or their attributes. We demonstrate that this new approach is a generalized technique that can be applied to the simultaneous joint inversion of any number and combination of geophysical datasets. Our approach includes as special cases those extant methods based on correlations and/or structural constraints of the multimodal model parameters. As an illustration of this new approach, we present a model study relevant to exploration under cover for iron oxide copper‐gold (IOCG) deposits, and demonstrate how joint inversion of gravity and magnetic data is able to recover alteration associated with IOCG mineralization.

Simultaneous joint inversion of 3D seismic and magnetotelluric data from the Walker Ridge

Elena Medina, Andrea Lovatini, Federico Golfre Andreasi, Simone Re and Fred Snyder present a case history from the Gulf of Mexico of multi-dimensional simultaneous joint inversion of wide-azimuth 3D seismic and marine magnetotelluric data to improve subsalt imaging.

Multi‐measurement integration for near‐surface geological characterization

ABSTRACTGeological characterization of the near‐surface is challenging because often shallow layers are unconsolidated and their properties have rapid and significant variations in vertical and lateral directions. This creates challenges for a majority of geophysical methods, which have limitations in the presence of a complex distribution of physical properties and that give optimal results over limited depth intervals, which are different for the different methods. The shallow near‐surface depth region is often problematic for standard seismic exploration methods. As a result, geological characterization through individual measurements is often compromised. The solution proposed here is the integration of multiple measurements of surface and deep subsurface properties, with the goal of generating a geologically meaningful lithostructural model of the near‐surface.The underlying idea is based on the observation that deep subsurface structural features often leave an impression on the surface. This provides an opportunity for structural mapping at the surface and below the bottom of the near‐surface zone to guide the processing and interpretation of data from the near‐surface.We show how the integration of multiple data sets from the measurement of different physical properties can be used to generate and enhance specific near‐surface products such as drilling risk maps or P‐wave velocity models for static corrections or for velocity modelling. The analysis proves that integration of multiple data sets can provide a solution when individual measurements, such as refraction traveltimes, do not provide sufficient information to characterize the lithostructure of the near‐surface. Satellite surface imagery and deep seismic data can provide the structural framework for the processing and interpretation of shallow seismic data. Simultaneous joint inversion of shallow P‐ and S‐wave data reveals the vertical and lateral velocity structures in the near‐surface, which are usually concealed when only refraction data are used. Finally, the correlation of multiple data sets in a correct spatial reference provides a quality control for the data set and allows for the lithostructural interpretation of data in the near‐surface.

Integrated seismic-electromagnetic workflow for sub-basalt exploration in northwest Saudi Arabia

A project involving magnetotellurics, shallow EM and gravity acquisition was carried out in northwest Saudi Arabia to improve the imaging of a basalt-covered section of the Al Jawf graben where seismic is particularly noisy. The Al Jawf graben extends from Jordan to Saudi Arabia. Mesozoic and Paleozoic sections contained within the structure have the potential to host multiple oil reservoirs. The lack of reliable seismic data in this area motivated the use of a multi-geophysics exploration approach. Electromagnetic (EM) techniques were identified as the optimal complementary geophysical data set. In fact, the EM signal propagates through the resistive basalt cover with minimal attenuation, providing information about the underlying conductive sedimentary section. A combination of active-source time-domain EM (TDEM) and broadband magnetotelluric (MT) techniques imaged the base of the shallow basalt cover and its internal variations as well as larger-scale faults that control the graben structure. Integration of the broadband EM and gravity data with seismic was achieved through simultaneous joint inversion (SJI) to derive an integrated velocity model that was subsequently used to reprocess the seismic data in the time and depth domains. Seismic reprocessing resulted in a substantial improvement in the seismic imaging of deep structures predicted by the structural model but not imaged by the previous seismic processing.

Salt detection and interactive interpretation by seismic-gravity simultaneous joint inversion

Salt detection and interactive interpretation by seismic-gravity simultaneous joint inversion