Inversion Of Electromagnetic Data Research Articles

A multi-task learning network based on transformer network for airborne electromagnetic detection imaging and denoising

Abstract As an emerging geophysical exploration technology in recent years, airborne electromagnetic exploration has the advantages of adapting to diverse terrains, wide coverage, and providing a large amount of electromagnetic data, and can be applied to the rapid collection of large amounts of data. Scenarios are often used in fields such as deep geological structures, mineral resource exploration, and environmental engineering research. However, traditional airborne electromagnetic data inversion technology usually takes a long time to process a large amount of airborne electromagnetic data, and it is difficult to remove the noise in the later signals. Therefore, this paper proposes a multi-task learning network structure based on Transformer. By constraining the two network branches of imaging and denoising, a sub-network with simultaneous denoising and imaging is established to process aeronautical electromagnetic data. The noise test set is introduced for testing. This model achieved a 582.61% signal-to-noise ratio improvement in smooth Gaussian noise denoising, and a 129.69% and 112.74% signal-to-noise ratio improvement in non-smooth Gaussian noise and random impulse noise denoising, respectively. The method proposed in this article overcomes the shortcomings of traditional inversion imaging such as slow speed and low resolution, and at the same time eliminates the influence of noise in airborne electromagnetic data. This is of great significance for the application of deep learning in the field of geophysical exploration.

Large-scale 3D inversion of semi-airborne electromagnetic data - topography and induced polarization effects in a graphite exploration scenario

The transition towards renewable energies demands secure supply with critical raw material and requires efficient non-invasive methods for deep Earth resource exploration. The novel DESMEX (Deep electromagnetic sounding for mineral exploration) semi-airborne electromagnetic (semi-AEM) exploration concept aims at efficient exploration of resources down to 1 km depth. Here we present a large-scale semi-AEM exploration study in a graphite mining district in eastern Bavaria, Germany. The derived 3D semi-AEM model is based on about 70000 complex valued data points recorded utilizing seven transmitters. Strong conductivity contrasts with dominant east-west trending anomalies are visible. Shallow high conductivity structures correlate well with the known occurrence of graphite and match existing helicopter-borne EM results. The main anomaly reaches down to several hundred meters depth. To investigate different interpretation scenarios for large-scale semi-AEM data, we demonstrate the effect of topography and analyze the feasibility of fast 2D inversion applications. To validate the robustness of the 3D semi-AEM model, the data set is inverted with two different 3D inversion algorithms and the results are compared. The presence of graphite leads to significant induced polarization (IP) effects with considerably high chargeabilities superposing electromagnetic induction. We include these effects in a realistic 3D inversion using a synthetic data study to analyze if the IP effect alters the overall conductivity structure and demonstrate that the obtained 3D model is reliable.

A multidimensional AI-trained correction to the 1D approximate model for Airborne TDEM sensing

The computational resources required to solve the full 3D inversion of time-domain electromagnetic data are immense. To overcome the time-consuming 3D simulations, we construct a surrogate model, more precisely, a data-driven statistical model to replace the 3D simulations. It is trained on 3D data and predicts the approximate output much faster. We construct a surrogate model that predicts the discrepancy between a 1D subsurface model and a deviation of the 1D assumption. The latter response is fastly computable with a semi-analytical 1D forward model. We exemplify the approach on a two-layered case. The results are encouraging even with few training samples. Given the computational cost related to the 3D simulations, there are limitations in the number of training samples that can be generated. In addition, certain applications require a wide range of parameters to be sampled, such as the electrical conductivity parameters in a saltwater intrusion case. The challenge of this work is achieving the best possible accuracy with only a few thousand samples. We propose to view the performance in terms of learning gain, representing the gain from the surrogate model whilst still acknowledging a residual discrepancy. Our works open new avenues for effectively simulating 3D TDEM data.

Adaptive laterally constrained inversion of time-domain electromagnetic data using hierarchical Bayes

Laterally constrained inversion (LCI) of time-domain electromagnetic (TEM) data is effective in recovering quasi-layered models, particularly in sedimentary environments. By incorporating lateral constraints, LCI enhances the stability of the inverse problem and improves the resolution of stratified interfaces. However, a limitation of the LCI is the recovery of laterally smooth transitions, even in regions unsupported by the available data sets. Therefore, we have developed an adaptive LCI scheme within a Bayesian framework. Our approach introduces user-defined constraints through a multivariate Gaussian prior, wherein the variances serve as hyperparameters in a hierarchical Bayes algorithm. By simultaneously sampling the model parameters and hyperparameters, our scheme allows for varying constraints throughout the model space, selectively preserving lateral constraints that align with the available data sets. We demonstrate the effectiveness of our adaptive LCI scheme through a synthetic example. The inversion results showcase the self-adaptive nature of the strength of the constraints, yielding models with smooth lateral transitions while accurately retaining sharp lateral interfaces. An application to field TEM data collected in Laizhou, China, supports the findings from the synthetic example. The adaptive LCI scheme successfully images quasi-layered environments and formations with well-defined lateral interfaces. Moreover, the Bayesian inversion provides a measure of certainty, allowing for a comprehensive illustration of the confidence in the inversion results.

Surface geometry inversion of transient electromagnetic data

We investigate an emerging method called surface geometry inversion (SGI) for the inversion of transient electromagnetic (TEM) data. Conventional minimum-structure inversion methods parameterize the Earth model with many mesh cells within which the physical properties are constant and construct a physical property model that is usually smoothly varying as well as fitting the observations. With these smooth models, it is difficult to extract the interface between different geological units, and it can be especially difficult to target drill holes for thin, plate-like targets which are frequently encountered in mineral exploration. Our SGI parameterizes the model in terms of the coordinates of the nodes (vertices) used to connect together the surfaces that define the geological interfaces. The algorithm then inverts for the locations of these nodes, which directly provides geometric information about the target. This can be more useful than a fuzzy image of conductivity, especially for an exploration project. A genetic algorithm (GA) is used to solve the non-linear over-determined optimization problem. We use a finite-element solver to solve the TEM forward modeling problem of each candidate model in the GA population. Because forward modeling is independent for each model, we implement a hybrid MPI + OpenMP parallel method to improve computational efficiency. We investigate a new parameterization method specifically designed for thin, plate-like structures, that is more efficient and can effectively avoid self-intersection. We first illustrate the effectiveness of our SGI algorithm on a synthetic block model before testing the new parameterization method on a synthetic thin plate model. Finally, we apply our SGI to a real dataset collected for the exploration of thin graphitic faults. The constructed model from our SGI corresponds well with the drilling data.

Bayesian inversion and uncertainty analysis of transient electromagnetic data

AbstractQuantification of non‐uniqueness and uncertainty is important for transient electromagnetism (TEM). To address this issue, we develop a trans‐dimensional Bayesian inversion schema for TEM data interpretation. The trans‐dimensional posterior probability density (PPD) offers a solution to model selection and quantifies parameter uncertainty resulting from the model selection from all possible models rather than determining a single model. We use the reversible‐jump Markov chain Monte Carlo sampler to draw ensembles of models to approximate PPD. In addition to providing reasonable model selection, we address the reliability of the inversion results for uncertainty analysis. This strategy offers reasonable guidance when interpreting the inversion results. We make the following improvements in this paper. First, in terms of algorithmic acceleration, we use the nonlinear optimization inversion results as the initial model and implement the multi‐chain parallel method. Second, we develop double factors to control the sampling step size of the proposed distribution, so that the sampling models cover the high‐probability region of the parameter space as much as possible. Finally, we provide the potential scale reduction factor‐η convergence criteria to assess the convergence of the samples and ensure the rationality of the output models. The proposed methodology is first tested on synthetic data and subsequently applied to a field dataset. The TEM inversion results show that probability inversion can provide reliable references for data interpretation through uncertainty analysis.

Bayesian joint inversion of seismic and electromagnetic data for reservoir lithofluid facies, including geophysical and petrophysical rock properties

A Bayesian approach is developed to estimate lithofluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock-physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Because computing this probability for all possible facies configurations may be impractical, a Markov chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geologic scenario and a well from a selected marine oil field. Two other wells from the same reservoir are used to gather prior distributions. Data from the well, calibration of the rock-physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward-modeling adaptations based on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical data sets are then inverted for lithofluid facies models under different prior inputs. Two challenging geoelectric scenarios also are tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating these geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also indicate potential for quantitative reservoir interpretations.

Assessing and Improving the Robustness of Bayesian Evidential Learning in One Dimension for Inverting Time-Domain Electromagnetic Data: Introducing a New Threshold Procedure

Understanding the subsurface is of prime importance for many geological and hydrogeological applications. Geophysical methods offer an economical alternative for investigating the subsurface compared to costly borehole investigations. However, geophysical results are commonly obtained through deterministic inversion of data whose solution is non-unique. Alternatively, stochastic inversions investigate the full uncertainty range of the obtained models, yet are computationally more expensive. In this research, we investigate the robustness of the recently introduced Bayesian evidential learning in one dimension (BEL1D) for the stochastic inversion of time-domain electromagnetic data (TDEM). First, we analyse the impact of the accuracy of the numerical forward solver on the posterior distribution, and derive a compromise between accuracy and computational time. We also introduce a threshold-rejection method based on the data misfit after the first iteration, circumventing the need for further BEL1D iterations. Moreover, we analyse the impact of the prior-model space on the results. We apply the new BEL1D with a threshold approach on field data collected in the Luy River catchment (Vietnam) to delineate saltwater intrusions. Our results show that the proper selection of time and space discretization is essential for limiting the computational cost while maintaining the accuracy of the posterior estimation. The selection of the prior distribution has a direct impact on fitting the observed data and is crucial for a realistic uncertainty quantification. The application of BEL1D for stochastic TDEM inversion is an efficient approach, as it allows us to estimate the uncertainty at a limited cost.

Inversion for 3D Conductivity and Chargeability Models Using EM Data Acquired by the New Airborne TargetEM System in Ontario, Canada

This paper introduces an original approach to the joint inversion of airborne electromagnetic (EM) data for three-dimensional (3D) conductivity and chargeability models using hybrid finite difference (FD) and integral equation (IE) methods. The inversion produces a 3D model of physical parameters, which includes conductivity, chargeability, time constant, and relaxation coefficients. We present the underlying principles of this approach and an example of a high-resolution inversion of the data acquired by a new active time domain airborne EM system, TargetEM, in Ontario, Canada. The new TargetEM system collects high-quality multicomponent data with low noise, high power, and a small transmitter–receiver offset. This airborne system and the developed advanced inversion methodology represent a new effective method for mineral resource exploration.

Quasi-2D inversion of surface large fixed-loop transient electromagnetic sounding data

Abstract In many cases, 1D inversion is still an important step in transient electromagnetic data processing. Potential issues may arise in the calculation of apparent resistivity using induced electromotive force (EMF) due to overshoot and the presence of multi-valued functions. Obtaining reliable and consistent inversion results using a uniform half-space as the initial model is challenging, especially when aiming for efficient inversion. Focusing on these problems, we use the land-based transient electromagnetic (TEM) sounding data, which was acquired by using a large fixed-loop transmitter, and adopt a quasi-2D inversion scheme to generate improved images of the subsurface resistivity structure. First, we have considered directly using magnetic field data or converting induced EMF into magnetic field, and then calculating the apparent resistivity over the whole zone. Next, a resistivity profile that varies with depth is obtained through fast smoke ring imaging. This profile serves as the initial model for the subsequent optimal inversion. The inversion scheme uses a nonlinear least-squares method, incorporating lateral and vertical constraints, to produce a quasi-2D subsurface image. The potentiality of the proposed methodology has been exemplified through the interpretation of synthetic data derived from a 3D intricate resistivity model, as well as field data obtained from a TEM survey conducted in a coalmine field. In both cases, the inversion process yields quasi-2D subsurface images that exhibit a reasonable level of accuracy. These images appear to be less moulded by 3D effects and demonstrate a satisfactory level of agreement with the known target area.

Deep structural controls on the distribution of carbonate reservoirs and overburden heterogeneity in Central Luconia province, offshore Borneo revealed by 3D anisotropic inversion of regional controlled-source electromagnetic and magnetotelluric profile data

Understanding the deep structural controls on reservoir distribution and overburden heterogeneity is important for near-field exploration and green-field development of the Miocene to Holocene carbonate buildups in Central Luconia province in offshore northwest Borneo, Malaysia. Data from 59 stations along an approximately 180 km long controlled-source electromagnetic (CSEM) and magnetotelluric (MT) survey line with three segments recorded using different CSEM transmitter-towing directions were available. We applied three-dimensional (3D) anisotropic resistivity inversion with cross-gradient constraint and verified the resulting models using resistivity logs from nearby wells and the acoustic basement interpreted from seismic data. Our anisotropic resistivity models reveal a fragmented carbonate-rich zone atop a segmented basement comprising electrically resistive horsts coinciding with the northerly Mega-platform, Central and Southern Field Highs, separated by steep conductive zones coinciding with the West, East, and Southeast Troughs previously interpreted from seismic data. The structural highs correlate with the spatial distribution of the known carbonate buildups implying a genetic link. The carbonate bodies are overlain and underlain by persistent layers (C2 and C3) of low resistivity and high anisotropy, which we interpret as indicating compressional deformation or detachment zones. Overburden layer C2 (whose base coincides with the top of a key sedimentary package, Cycle V in seismic data) is thinnest over the East Trough and is discontinuous at the eastern margin of the West Trough, which are locations where drilled wells did not find hydrocarbons implying that the seal rocks are inefficient or breached at those localities. We used these observations to refine the existing seismic-based interpretation of carbonate play-types along our transect indicating how 3D joint CSEM-MT imaging can potentially contribute to derisking or optimizing future exploration and/or development work in this province.

Weighted Laterally Constrained Inversion of Airborne Transient Electromagnetic Data with IP Effects

Due to the induced polarization (IP) effects, abnormal fast decay and sign reversals are often observed in the field airborne transient electromagnetic (ATEM) responses, this phenomenon can well be explained by introducing the four-parameter Cole-Cole resistivity model. However, the introduction of multiple parameters makes the non-uniqueness of inversion more serious. To improve this problem, a weighted 1D laterally constrained inversion is proposed to recover the four IP parameters and thickness via adding different weighted factors chosen by lateral constraint conditions, in this paper, a synthetic polarization model is developed and 1D forward modeling with synthetic natural noise is conducted to imitate the field response. The proposed inversion is employed on the synthetic data and the results demonstrate that the method can improve the inversion stability and obtain more reliable result.

3D inversion of multifrequency controlled-source electromagnetic data and its application to geothermal exploration in the Tianzhen region of the northern Datong Basin, China

Owing to its sensitivity to hydrothermal alteration and geothermal fluids, the distribution of subsurface electrical conductivity provides critical information for characterizing geothermal systems. For geothermal exploration, the controlled-source electromagnetic (CSEM) technique provides a crucial tool to image the subsurface resistivity structures, especially in areas with strong cultural noise. Usually, land-based CSEM surveys are carried out with dozens of operating frequencies to enhance spatial resolution. However, the 3D inversion of multifrequency CSEM data using the commonly used direct solver is challenging with limited computational resources. In this study, we implement a practical inversion strategy for interpreting 3D multifrequency CSEM data. By combining a hybrid direct-iterative solver with the inexact Gauss-Newton optimization, 3D inversion of CSEM data involving multiple frequencies can be performed effectively on typical workstations. First, we test the effectiveness of the developed approach using synthetic multifrequency 3D CSEM data generated for a simplified geothermal model. The inversion strategy is then applied to the multifrequency CSEM field data collected for the geothermal exploration at Tianzhen region in Shanxi Province, China. The comparison between the inversion results using the sparse data set (six frequencies) and the dense data set (12 frequencies) highlights the necessity of using data from more frequencies in the inversion to improve the resolution. The resulting 3D resistivity model clearly delineates the clay alteration layers and shallow thermal reservoirs of the potential high-temperature geothermal system within the study area, while its deep heat source is not revealed owing to the limited investigation depth of the survey.

A method to construct statistical prior models of geology for probabilistic inversion of geophysical data

In probabilistic inversion of geophysical data, one must describe the expected noise in the system, and any prior information. In a geoscience context, prior information can provide a quantitative description of the expected spatial variability and correlations of the geology. But in practical inversion cases, driven by difficulty in quantifying geological information and computational complexity, an analytical mathematical smooth prior model is often chosen to describe the spatial variability. This is one of the primary reasons that realistic geological structures are difficult to resolve in geophysical models. Thus, there is currently a need for investigating and proposing practical ways of capturing complex (and often qualitative) geological information in statistical prior models that can be used in probabilistic inversion, which satisfies both the geologist, geophysicist, engineer and the geostatistician. In this research we show how a 1D statistical prior model can be designed that emulates the spatial distribution found in 188 boreholes with Miocene and Quaternary deposits from a study area (approx. 177,5 km2) near Horsens, Denmark. The prior model is built in two major steps 1) a multidimensional distribution describing the sub-division of major geological elements (here represented by lithologies from defined geological periods) and 2) a truncated pluri-Gaussian distribution describing the internal structure of lithologies within each element. The presented prior model can both be used to generate independent realizations, which can be used as part of the extended rejection sampler, as well as allowing the possibility of doing a “random walk” in the model space, as required by the extended Metropolis algorithm. We demonstrate, as an example, how the developed prior model can be used in a probabilistic inversion of airborne transient electromagnetic (AEM) data and discuss the implications the use of such informed prior models can have.

Three-Dimensional Separate and Joint Inversions of Multi-Component Frequency-Domain Airborne Electromagnetic Data: Synthetic Model Studies.

Airborne electromagnetic (AEM) surveys using airborne mobile platforms enable rapid and efficient exploration of areas where groundwork is difficult. They have been widely used in fields such as shallow resource exploration and environmental engineering. Three-dimensional AEM inversion is the main technique used in fine structural interpretation. However, most current methods focus on separate component data inversions, which limit the kinds of structures that can be recovered in the inversion results. To address this issue, a method for the robust 3D joint inversion of multicomponent frequency-domain AEM data was developed in this study. First, a finite element method based on unstructured tetrahedral grids was used to solve the forward problem of frequency-domain AEM data for both isotropic and anisotropic media. During inversion, a limited-memory quasi-Newton (L-BFGS) method was used to reduce the memory requirements and enable the joint inversion of large-scale multicomponent AEM data. The effectiveness of our algorithm was demonstrated using synthetic models for both isotropic and anisotropic cases, with 5% Gaussian noise added to the modeling data to simulate the measured data for separate and joint inversions. The results of the synthetic models show that joint inversion has advantages over separate inversion in that it enables the recovery of finer underground structures and provides a novel approach for the fine interpretation of frequency-domain AEM data.

Inversion of VLF-R data using a non-linear rank order smoothing constraint and its implementation to image surface rupture of the North Anatolian Fault in Başiskele, Kocaeli, NW Turkey

Inversion of electromagnetic induction data is often realized using smoothing operators for regularizing the process. These operators are applied using derivative matrices, penalizing differences between neighboring mesh cells. The resulting smoothing effect is similar to that of low-pass Gaussian filters and may cause difficulties due to the smoothed boundaries between structures. To prevent such ambiguities, non-linear rank filters are applied widely in image processing applications to filter low frequency variations while keeping boundaries. Correspondingly, we have defined a non-linear rank order smoothing operator for regularization. The effects of the defined parameters are demonstrated on synthetic models and the operator is observed to be able to provide better defined boundaries. The implementation of the operator on field data is realized using single-frequency VLF-R data collected on profiles traversing the surface rupture of the North Anatolian Fault in Kocaeli, Turkey. In the study area, ERT data are also collected on a single profile, and its smooth inversions are realized individually and jointly with VLF-R data for comparisons. Inversions show that the recovered subsurface structure using the rank order smoothing is closer to that indicated by the ERT data and previous studies. According to the recovered models, the clayey fillings in the fault rupture are acting as a barrier affecting the horizontal fluid flow in the near-surface and the resulting water accumulations around the fault rupture may cause susceptibility for earthquake induced damages.

Simultaneous Inversion of Ocean Bottom Pressure and Electromagnetic Tsunami Records for the 2009 Samoa Earthquake

AbstractThis study presents tsunami inversion analyses that simultaneously use ocean‐bottom pressure and ocean‐bottom electromagnetic data along with an accurate tsunami propagation model to estimate the earthquake source model for the 2009 Samoa earthquake. This earthquake was a doublet event that occurred along a pair of normal and thrust faults. To the best of our knowledge, this joint inversion of pressure and electromagnetic data is unprecedented. The incorporation of electromagnetic data in the tsunami inversion analysis was relatively simple, and the inclusion of frequency dispersion, self‐attraction, and loading effects in the tsunami propagation calculations allowed the use of tsunami waveforms recorded at distant locations, which improved the reliability of the solutions obtained. Our best source model indicated that the east‐west extent of the plate‐boundary slip area was approximately 50 km, which is narrower than that of a previous fault model, and explained both electromagnetic and pressure data from the 2009 Samoa tsunami.

Inversion of TEM measurement data via a quantum particle swarm optimization algorithm with the elite opposition-based learning strategy

The fine interpretation and inversion of transient electromagnetic method measurement data have the problems of nonlinearity, multi-solution, and ill condition. However, the conventional particle swarm optimization (PSO) nonlinear inversion methods suffer from prematurity, slow convergence, and low calculation accuracy. To solve these problems, a quantum PSO (QPSO) algorithm based on the elite opposition-based learning (EOL) strategy is proposed. Firstly, three performances tests of the EOL-QPSO algorithm are carried out with Peaks, Schaffer and Rastrigin functions. The results show that the EOL-QPSO algorithm has excellent solution accuracy, efficient calculation speed and balanced exploitation and exploration capability. Secondly, the conventional PSO algorithm and the EOL-QPSO algorithm are used to compare the inversion of the theoretical model and the synthetic data with noise, and combined with Bayesian method, the posterior model probability statistics of the synthetic data are carried out. The research shows that the EOL-QPSO inversion algorithm is improved in terms of calculation accuracy, calculation efficiency, anti-noise performance and exploitation and exploration capability, and it can accurately obtain the posterior estimates of the real model. Finally, the inversion of field-measured data demonstrates that the EOL-PSO inversion method accurately reflects the position of the water-accumulated goaf.

Geometric 2.5D inversion of marine time domain electromagnetic data with application to hydrocarbon deposits prospecting

To solve the problem of detection and delineation of marine hydrocarbon deposits located at different depths in an inhomogeneous medium, we propose an approach to 2.5D inversion of electromagnetic (EM) data measured in the time domain (TD) using a differentially normalized method of electrical prospecting. The approach is based on geometric inversion, in which block structures are used to parameterize the geological medium. The sizes of blocks in the initial model are set depending on the depth. Bathymetry is taken into account as a 3D surface. As a result of inversion, the coordinates of block boundaries and conductivity inside blocks and layers are determined. The minimized functional is represented as a sum of weighted residuals between computed and practical data and regularizing terms. Adaptive regularization is used to satisfy parameter constraints. To solve a 3D forward problem and calculate derivatives with respect to parameters, the finite element method (FEM) is used. We propose to use a priori data on the depth of the target layers and use various options of block structures with an analysis of possible uncertainties in detecting target bodies and determining their boundaries. We present the results of a 2.5D inversion of field data obtained in one of the areas in the North Sea. Target bodies discovered as a result of inversion are confirmed by well data. Coincidences were obtained both for productive wells and for dry ones. Using synthetic data for various geoelectric models and options of block structures, we justify the proposed approach to the inversion of marine TD EM data and make recommendations for its use.

Crosshole radio electromagnetic tomography using apparent source intensity and logarithmic barrier

Crosshole radio-frequency (typically from 1 to 36 MHz) electromagnetic methods are powerful tools for locating conductive ores and distribution of electric properties of rocks between boreholes. In the range of these frequencies, electromagnetic field can be approximated by the wave field. Usually, crosshole radio-frequency electromagnetic inversion requires exact source intensity but it is difficult to determine that.We present a ray-based inversion method of crosshole radio electromagnetic data which does not require exact source intensity. We introduce a concept of apparent source intensity, establish the method of determining that and realize absorption coefficient and conductivity imaging of crosshole radio electromagnetic data using that. And then we improve accuracy and reliability of tomographic inversion of crosshole radio electromagnetic data using positive constraints for absorption and conductivities. Inversion is based on simultaneous iterative reconstruction technique or adaptive control gradient method.Tests of synthetic and field data show that the method we propose has high reliability.