Cooperative Inversion Research Articles

Building 3D Model of Rock Quality Designation Assisted by Co-Operative Inversion of Seismic and Borehole Data

The rock quality designation (RQD) is an important factor for geotechnical work in mining operations. A 3D model of RQD is significant for mine design. In practice, the mine design needs information of the whole mine area, but, borehole data is localised in only a small part of the whole area. Although, surface seismic methods can provide information of the whole survey (mine) area, the resolution of such data is generally smaller than the borehole data. The combination of seismic and borehole data may provide very useful information for geotechnical features like RQD if we can exploit seismic data as a means of interpolating the borehole information to the whole model of that geotechnical feature. In this work, we build a 3D model of RQD from seismic and borehole data acquired in Kevitsa mine site, Northern Finland. We investigate the relationship between Vp and RQD from eight boreholes to build a functional conversion between theses parameters. The seismic model obtained by co-operative inversion of seismic reflection and borehole data is converted into a RQD model using this function. Our model is assessed by comparison between RQD of core measurements and estimation from Vp of borehole data and seismic inversion. The results demonstrate that our process can build a reasonable 3D RQD model for geotechnical purposes.

Structurally coupled cooperative inversion of magnetic resonance with resistivity soundings

Hydrologic parameters, such as porosity, salinity, and hydraulic conductivity are keys for understanding the subsurface. Hydrogeophysical investigations can lead to ambiguous results, particularly in the presence of clay and saltwater. A combination of magnetic resonance sounding and vertical electrical sounding is known to provide insight into these properties. Structural coupling increases the model resolution and reduces the ambiguity for both methods. Inversion schemes using block models exist, but they have trouble resolving smooth or complex parameter distributions. We have developed a structurally coupled cooperative inversion (SCCI) that works with smooth parameter distributions and is able to introduce blocky features through the exchange of structural information. The coupling adapts the smoothness constraint locally in connection to the model roughness to allow for sharper model boundaries. We investigate the performance of the SCCI using blocky and smooth synthetic models that depend on two controlling coupling parameters. A well-known field case is used to verify the results with drilling core and well logs. Varying the coupling parameters results in equivalent models covering the bandwidth from smooth to blocky, while providing a similar data fit. The SCCI results are more consistent with the synthetic models. Structural coupling improves the resolution of the single methods and can be used to describe hydrogeophysical targets in more detail and less ambiguously.

Electric resistivity and seismic refraction tomography: a challenging joint underwater survey at Äspö Hard Rock Laboratory

Abstract. Tunnelling below water passages is a challenging task in terms of planning, pre-investigation and construction. Fracture zones in the underlying bedrock lead to low rock quality and thus reduced stability. For natural reasons, they tend to be more frequent at water passages. Ground investigations that provide information on the subsurface are necessary prior to the construction phase, but these can be logistically difficult. Geophysics can help close the gaps between local point information by producing subsurface images. An approach that combines seismic refraction tomography and electrical resistivity tomography has been tested at the Äspö Hard Rock Laboratory (HRL). The aim was to detect fracture zones in a well-known but logistically challenging area from a measuring perspective. The presented surveys cover a water passage along part of a tunnel that connects surface facilities with an underground test laboratory. The tunnel is approximately 100 m below and 20 m east of the survey line and gives evidence for one major and several minor fracture zones. The geological and general test site conditions, e.g. with strong power line noise from the nearby nuclear power plant, are challenging for geophysical measurements. Co-located positions for seismic and ERT sensors and source positions are used on the 450 m underwater section of the 700 m profile. Because of a large transition zone that appeared in the ERT result and the missing coverage of the seismic data, fracture zones at the southern and northern parts of the underwater passage cannot be detected by separated inversion. Synthetic studies show that significant three-dimensional (3-D) artefacts occur in the ERT model that even exceed the positioning errors of underwater electrodes. The model coverage is closely connected to the resolution and can be used to display the model uncertainty by introducing thresholds to fade-out regions of medium and low resolution. A structural coupling cooperative inversion approach is able to image the northern fracture zone successfully. In addition, previously unknown sedimentary deposits with a significantly large thickness are detected in the otherwise unusually well-documented geological environment. The results significantly improve the imaging of some geologic features, which would have been undetected or misinterpreted otherwise, and combines the images by means of cluster analysis into a conceptual subsurface model.

Magnetotelluric Inversion, Carbonaceous Phyllites and an Ore Zone: Kevitsa, Finland

We invert seven densely sampled magnetotelluric transects at the Kevitsa Ni-Cu-PGE (platinum group elements) deposit in Finland. The geology at the deposit presents at least two high electrical conductivity rock types within a resistive host. We consider the extent to which highly conductive phyllite and an ore zone can be separated through inversion of magnetotelluric data. Multiple inversions were completed with 312 magnetotelluric (MT) stations. It was possible to generate a three-dimensional conductivity volume from the 2D MT derived conductivities sections via volumetric interpolation. We compare the resulting 3D conductivity volume with co-located 2D and 3D seismic data. Comparisons show that an impression of the Ni-Cu-PGE ore-body could be observed in both seismic reflection and conductivity volumes. We discuss methods for constraining MT inversions and the impact they may have in separating proximal but highly conductive units. The inversions were complete as a precursor to full 3D cooperative inversion of the seismic and magnetotelluric data at this site.

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.

Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data

Natural source electromagnetic methods have the potential to recover rock property distributions from the surface to great depths. Unfortunately, results in complex 3D geo-electrical settings can be disappointing, especially where significant near-surface conductivity variations exist. In such settings, unconstrained inversion of magnetotelluric data is inexorably non-unique. We believe that: (1) correctly introduced information from seismic reflection can substantially improve MT inversion, (2) a cooperative inversion approach can be automated, and (3) massively parallel computing can make such a process viable. Nine inversion strategies including baseline unconstrained inversion and new automated/semiautomated cooperative inversion approaches are applied to industry-scale co-located 3D seismic and magnetotelluric data sets. These data sets were acquired in one of the Carlin gold deposit districts in north-central Nevada, USA. In our approach, seismic information feeds directly into the creation of sets of prior conductivity model and covariance coefficient distributions. We demonstrate how statistical analysis of the distribution of selected seismic attributes can be used to automatically extract subvolumes that form the framework for prior model 3D conductivity distribution. Our cooperative inversion strategies result in detailed subsurface conductivity distributions that are consistent with seismic, electrical logs and geochemical analysis of cores. Such 3D conductivity distributions would be expected to provide clues to 3D velocity structures that could feed back into full seismic inversion for an iterative practical and truly cooperative inversion process. We anticipate that, with the aid of parallel computing, cooperative inversion of seismic and magnetotelluric data can be fully automated, and we hold confidence that significant and practical advances in this direction have been accomplished.

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 workflow for cooperative inversion of seismic and magnetotelluric data

SUMMARY We present a cooperative inversion approach for acoustic impedance using seismic and magnetotelluric data. In this approach, the magnetotelluric data, sensitive to the resistivity of rocks are used to get the large scale background spatial trends of the acoustic impedance model, while the seismic data are used to get the smallscale features. The connections between resistivity and elastic properties of rocks are obtained from petrophysical relationships derived from borehole data. Structural constraints derived from seismic are used to improve the magnetotelluric inversion. We present an application of this technique to synthetic data derived from previous interpretation of seismic and magnetotelluric models in a mineral province. The synthetic example shows how an improved result is obtained using our cooperative inversion workflow.

Application of seismic attributes for constraining Magnetotelluric Inversion

SUMMARY Unconstrained inversion of surface magnetotelluric data generates non-uniqueness solutions. Boundaries derived from seismic reflectively images have the potential to substantially improve MT inversion. Seismic should be highly beneficial where significant and strong reflectors can reasonably be associated with contrast in electrical conductivity across well-defined relatively continuous boundaries. We show how seismic reflections can assist in defining such inversion controls as the smoothness penalty across known boundaries. We apply and compare a range of cooperative inversion strategies using large scale co-located magnetotelluric and seismic reflection field data sets from the Carlin style gold district in Nevada USA.

Ant colony optimisation inversion of surface and borehole magnetic data under lithological constraints

The ant colony optimisation algorithm has successfully been used to invert for surface magnetic data. However, the resolution of the distributions of the recovered physical property for deeply buried magnetic sources is not generally very high because of geophysical ambiguities. We use three approaches to deal with this problem. First, the observed surface magnetic data are taken together with the three-component borehole magnetic anomalies to recover the distributions of the physical properties. This cooperative inversion strategy improves the resolution of the inversion results in the vertical direction. Additionally, as the ant colony tours the discrete nodes, we force it to visit the nodes with physical properties that agree with the drilled lithologies. These lithological constraints reduce the non-uniqueness of the inversion problem. Finally, we also implement a K-means cluster analysis for the distributions of the magnetic cells after each iteration, in order to separate the distributions of magnetisation intensity instead of concentrating the distribution in a single area. We tested our method using synthetic data and found that all tests returned favourable results. In the case study of the Mengku iron-ore deposit in northwest China, the recovered distributions of magnetisation are in good agreement with the locations and shapes of the magnetite orebodies as inferred by drillholes. Uncertainty analysis shows that the ant colony algorithm is robust in the presence of noise and that the proposed approaches significantly improve the quality of the inversion results.

Zonal cooperative inversion of partially co‐located data sets constrained by structural a priori information

ABSTRACTIn many near‐surface geophysical studies it is now common practice to collect co‐located disparate geophysical data sets to explore subsurface structures. Reconstruction of physical parameter distributions underlying the available geophysical data sets usually requires the use of tomographic reconstruction techniques. To improve the quality of the obtained models, the information content of all data sets should be considered during the model generation process, e.g., by employing joint or cooperative inversion approaches. Here, we extend the zonal cooperative inversion methodology based on fuzzy c‐means cluster analysis and conventional single‐input data set inversion algorithms for the cooperative inversion of data sets with partially co‐located model areas. This is done by considering recent developments in fuzzy c‐means cluster analysis. Additionally, we show how supplementary a priori information can be incorporated in an automated fashion into the zonal cooperative inversion approach to further constrain the inversion. The only requirement is that this a priori information can be expressed numerically; e.g., by physical parameters or indicator variables. We demonstrate the applicability of the modified zonal cooperative inversion approach using synthetic and field data examples. In these examples, we cooperatively invert S‐ and P‐wave traveltime data sets with partially co‐located model areas using water saturation information expressed by indicator variables as additional a priori information. The approach results in a zoned multi‐parameter model, which is consistent with all available information given to the zonal cooperative inversion and outlines the major subsurface units. In our field example, we further compare the obtained zonal model to sparsely available borehole and direct‐push logs. This comparison provides further confidence in our zonal cooperative inversion model because the borehole and direct‐push logs indicate a similar zonation.

Zonal cooperative inversion of crosshole P-wave, S-wave, and georadar traveltime data sets

In this study, we use and extend a recently developed zonal cooperative inversion approach and apply it to the inversion of three independent geophysical field data sets. We invert crosshole P-wave, S-wave, and georadar data sets acquired in sand and gravel dominated unconsolidated sediments to detect and characterize different sedimentary units relevant for an engineering or hydrological site characterization. The zonal cooperative inversion of the three traveltime data sets results in a single subsurface model, which is a geophysical three-parameter model (P-wave, S-wave, and georadar velocity) outlining the major subsurface zonation while explaining all input data sets. Comparing our zonal model to direct push (DP) logging data (tip resistance, sleeve friction, and dielectric permittivity) shows good agreement; i.e., the zones in our geophysical model largely correspond to major DP parameter changes. Furthermore, we demonstrate how the sparse DP data can be inter- and extrapolated to the entire tomographic plane which allows for further geotechnical and hydrological interpretations. This study illustrates that the zonal cooperative inversion approach is highly flexible and an excellent tool to characterize a variety of environments in terms of multiple physical parameters.

Unexploded ordnance discrimination using magnetic and electromagnetic sensors: Case study from a former military site

In a study at a military range with the objective to discriminate potentially hazardous [Formula: see text] mortars from nonhazardous shrapnel, range, and cultural debris, six different discrimination techniques were tested using data from an array of magnetometers, a time-domain electromagnetic induction (EMI) cart, an array of time-domain sensors, and a time-domain EMI cart with a wider measurement bandwidth. Discrimination was achieved using rule-based or statistical classification of feature vectors extracted from dipole or polarization tensor models fit to detected anomalies. For magnetics, the ranking by moment yielded better discrimination results than that of apparent remanence from relatively large remanent magnetizations of several of the seeded items. The magnetometer results produced very accurate depths and fewer failed fits attributable to noisy data or model insuffi-ciency. The EMI-based methods were more effective than the magnetometer for intrinsic discrimination ability. The higher signal-to-noise ratio, denser coverage, and more precise positioning of the EM-array data resulted in fewer false positives than the EMI cart. When depth constraints from the magnetometer data were used to constrain the EMI fits through cooperative inversion, discrimination performance improved considerably. The wide-band EMI sensor was deployed in a cued-interrogation mode over a subset of anomalies. This produced the highest-quality data because of collecting the densest data around each target and the additional late time-decay information available with the wide-band sensor. When the depth from the magnetometer was used as a constraint in the cooperative inversion process, all [Formula: see text] mortars were recovered before any false positives were encountered.

Effect of co-operative fuzzy c-means clustering on estimates of three parameters AVA inversion

We determine the degree of variation of model fitness, to a true model based on amplitude variation with angle (AVA) methodology for a synthetic gas hydrate model, using co-operative fuzzy c-means clustering, constrained to a rock physics model. When a homogeneous starting model is used, with only traditional least squares optimization scheme for inversion, the variance of the parameters is found to be comparatively high. In this co-operative methodology, the output from the least squares inversion is fed as an input to the fuzzy scheme. Tests with co-operative inversion using fuzzy c-means with damped least squares technique and constraints derived from empirical relationship based on rock properties model show improved stability, model fitness and variance for all the three parameters in comparison with the standard inversion alone.

Integrating geological and geophysical data through advanced constrained inversions

To be reliable, Earth models used for mineral exploration should be consistent with all available geological and geophysical information. During the past several years an important focus of inversion research has been towards advancing the integration of geological data (such as lithology and structure), physical property data (measurements taken on rock samples) and geophysical survey data through appropriate inversion methodologies. We expand the types of geological information that can be incorporated into ‘minimum structure’ type deterministic inversions involving minimisation of an objective function. These include orientation information and physical property trends. We also present an iterative cooperative inversion strategy for combining multiple types of geophysical data and recovering geologically realistic models involving sharp interfaces between rock units. We provide a synthetic example to illustrate our methods.

Cooperative Inversion of Time Domain Electromagnetic and Magnetometer Data for The Discrimination of Unexploded Ordnance

Magnetic and electromagnetic induction (EMI) surveys are the primary techniques used for unexploded ordnance (UXO) remediation projects. Magnetometry is a valuable geophysical tool for UXO detection because of the ease of data acquisition and its ability to detect relatively deep targets. However, magnetic data can have large false alarm rates caused by geological noise, and there is an inherent non-uniqueness when trying to determine the orientation, size and shape of a target. EMI surveys, on the other hand, are relatively immune to geologic noise and are more diagnostic for target shape and size but have a reduced depth of investigation. We aim to improve discrimination ability by developing an interpretation method that takes advantage of the strengths of both techniques. We consider cooperative inversion, where information from the inversion of one type of data is used as a constraint for inverting another. We compare the confidence with which we can discriminate UXO from non-UXO targets when inverting the data sets cooperatively, to results from individual inversions. Examples are given of the application of the methodology to time domain electromagnetic induction (TEM) and magnetic data sets collected at the Yuma Proving Ground UXO Standardized Test Site calibration grid and the Former Camp Sibert.

Detecting voids in masonry by cooperatively inverting P-wave and georadar traveltimes

Collecting different geophysical data sets at the same object and site offers the opportunity to reduce uncertainties and ambiguities in data analysis and interpretation. To be effective, the different available data sets should be linked during the model-generation process, e.g. by cooperative inversion. In this study, we apply a recently developed zonal cooperative inversion approach based on fuzzy c-means cluster analysis to a non-destructive testing experiment. After briefly reviewing the fundamentals of the inversion strategy, we present a synthetic study investigating the potential of the method to detect air-filled voids in masonry by using ultrasonic and georadar traveltime data. Then, we present and discuss laboratory experiments including the results of cooperatively inverted ultrasonic and georadar traveltimes collected at a masonry test specimen. The geometry of the specimen is known and is thus an ideal test object for a first-time real application of the novel zonal cooperative inversion procedure. Compared to the results of separate inversions of ultrasonic and georadar traveltimes, the zonal cooperative inversion allows for an improved delineation of the size and position of the cavities. The P-wave and georadar velocities determined for the model regions corresponding to the cavities are also improved.

Cooperative inversion of 2D geophysical data sets: A zonal approach based on fuzzy c-means cluster analysis

In many near-surface geophysical applications, it is now common practice to use multiple geophysical methods to explore subsurface structures and parameters. Such multimethod-based exploration strategies can significantly reduce uncertainties and ambiguities in geophysical data analysis and interpretation. We propose a novel 2D approach based on fuzzy [Formula: see text]-means cluster analysis for the cooperative inversion of disparate data sets. We show that this approach results in a single zonal model of subsurface structures in which each zone is characterized by a set of different parameters. This finding implies that no further structural interpretation of geophysical parameter fields is needed, which is a major advantage compared with conventional inversions that rely on a single input data set and cooperative inversion approaches.

Integration of seismic methods with reservoir simulation, Pikes Peak heavy-oil field, Saskatchewan

Reservoir characterization is essential for providing optimal recovery from heavy-oil fields. The process of reservoir characterization is demonstrated for a steam injection project at Pikes Peak heavy oil field near Lloydminster, Saskatchewan, Canada. Geologic, geophysical and reservoir engineering data are used to improve the interpretation of reservoir conditions. There is ambiguity in modeling any of these data. However, in modeling all data sets in “cooperative inversion,” ambiguity is decreased, thereby enhancing our knowledge of the reservoir. One of the main benefits of this oil-field modeling is that bypassed oil and steam fronts are effectively mapped.

Joint inversion of transient‐pressure and dc resistivity measurements acquired with in‐situ permanent sensors: A numerical study

We develop a quantitative methodology to interpret jointly in‐situ transient‐pressure and dc resistivity measurements acquired in a hypothetical water injection experiment, with the goal of displacing oil in a hydrocarbon‐bearing formation. The assumed measurement acquisition system consists of enforcing time‐variable flow rates while injecting water into the surrounding rock formations, thereby producing a sequence of repeated transient‐pressure pulses. In‐situ dc resistivity measurements are acquired at the end of every flow‐rate pulsing sequence. The objective of the experiment is to estimate the spatial distributions of absolute fluid permeability and electrical resistivity. Geophysical inverse theory is used to account for the presence of noisy measurements.Synthetic data with noise are inverted to assess the relative benefits of different types of sensor geometries in axisymmetric models of permeability and electrical resistivity. Results strongly suggest that cooperative inversion of in‐situ transient‐pressure and dc resistivity measurements reduces nonuniqueness in the estimation of resistivity and absolute permeability governed by dynamic fluid‐flow phenomena. This leads to a more accurate estimate of permeability and resistivity compared to separate inversion of each data type.