Airborne 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.

A speculative ridge within the Kidson Sub-basin – integrated interpretation from geophysical data

This paper presents an analysis of the basement geometry and its implications on the Kidson Sub-basin in the southern Canning Basin. Integration of seismic data primarily near the rim of the sub-basin with airborne electromagnetic data across its central portion reveals an east-northeast oriented ridge at Permian level, suggesting a possible elevated structure in the Ordovician and basement. The proposed ridge effectively separates the Kidson Sub-basin into two distinct parts, providing an explanation for facies and thickness variations observed in the Nambeet and Goldwyer formations, as well as the absence of the Willara Formation in the southeastern portion of the sub-basin. The presence of shallowing trends observed in seismic profiles along the southern flank of the ridge suggests the proximity of the basement high. The ridge is speculative and requires further work to verify its existence, potentially including passive and reflection seismic surveys across the structure. If confirmed, it might be a significant feature with far-reaching implications for the prospectivity of resources in the Kidson Sub-basin. The hydrocarbon sourced from the Nambeet, Goldwyer and Bongabinni formations in the NNW depocentre may have migrated via the extension of the Parallel Range Fault and be trapped in the footwall block over the ridge. Compared to the structures in the Fitzroy Trough, the traps within the Kidson Sub-basin are expected to maintain reasonable integrity and have good potential for petroleum accumulation and carbon sequestration in the thick Paleozoic succession.

Successive bootstrapping deep learning approach and airborne EM-borehole data fusion to understand salt water in the Mississippi River Valley Alluvial Aquifer

Increasing demands from agriculture and urbanization have decreased groundwater level and increased salinity worldwide. Better aquifer characterization and soil salinity mapping are important for proactive groundwater management. Airborne electromagnetic (AEM) is a powerful tool for aquifer characterization and salinity delineation. However, AEM needs to be interpreted with caution before being used for groundwater quality analysis. This study introduces a framework that utilizes the AEM data for both lithologic modeling and salinity delineation. A resistivity-to-lithology (R2L) model is developed to interpret AEM resistivity to lithology based a depth-dependent multi-resistivity thresholds. Then, a cokriging method is used to integrate AEM data from two different EM systems to predict resistivity at the aquifer. Finally, a resistivity-to-chloride concentration (R2C) model utilizes the resistivity model to estimate chloride concentrations at sand facies. A deep learning artificial neural network (DL-ANN) model is introduced with a successive bootstrapping approach to estimate total dissolved solids first and then use it together with resistivity data to estimate chloride concentration. The methodology was applied to delineating salinity plumes in the Mississippi River Valley alluvial aquifer (MRVA). This study found that the salinity distribution in MRVA is highly correlated with the Jurassic salt basin, salt domes, faulting, seismicity, and river water quality. The result indicates salinity upconing due to excessive pumping.

Deep resistivity geophysics of the San Juan–Silverton caldera complex, San Juan County, Colorado (USA)

Magnetotelluric (MT) and audiomagnetotelluric (AMT) data are used to better understand the subsurface geology and mineral resources in the San Juan–Silverton caldera complex located near Silverton, Colorado, western United States, as part of the extensive southern Rocky Mountains volcanic field that covers much of southwestern Colorado and northern New Mexico. Seven MT and AMT profiles of varying lengths image resistivity structure to depths of ~5 km. The AMT inversion models characterize geophysical responses of near-surface lithologies, structures, and mineralized systems and also help corroborate airborne electromagnetic data at shallow levels. The MT inversion models extend our depth of investigation from near the surface to great depths (~5 km) and help to form hypotheses about roots of the hydrothermal plumbing that fed shallower mineralized systems. Subsurface high resistivities occur beneath intermediate-composition lava flows and Proterozoic units. Subsurface moderate- to low-resistivity values may reflect hydrothermal plumbing that served as flow paths for mineralizing fluids and metallic ore formation. The model interpreta­tions presented in this study could be utilized in remediation planning or mineral resource applications. The methods used could be applied to other watersheds with similar volcanic environments containing acid-generating historical mines or hydrothermally altered and mineralized source rocks.

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.

Bayesian Inversion of Frequency-Domain Airborne EM Data With Spatial Correlation Prior Information

Bayesian Inversion of Frequency-Domain Airborne EM Data With Spatial Correlation Prior Information

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.

Three-Dimensional Inversion of Induced Polarization Effects in Airborne Time Domain Electromagnetic Data Using the GEMTIP Model

This paper discusses the physical and mathematical principles of the airborne induced polarization (IP) method. The possibility of extracting information about the IP properties of rocks from airborne survey data has become a subject of active research recently. We introduce a method for the joint inversion of the airborne EM data into the electrical conductivity and IP parameters based on the generalized effective-medium theory of induced polarization (GEMTIP). We also present the results of the inversion of the airborne EM data collected over the Echum Project Area, in Northwestern Ontario, Canada, into 3D conductivity and chargeability models. Obtaining IP physical property models from an airborne geophysical survey may result in a paradigm change in mineral exploration by pulling more information and value from airborne EM surveys.

A comparison of airborne geophysical data over two magmatic nickel deposits

Historical exploration for economic nickel (Ni) mineralization has often targeted magmatic sulfide deposits in extensional settings. However, convergent-margin-hosted Alaskan-type complexes represent a potentially underexplored source of Ni. Case studies of the geophysical responses associated with two magmatic Ni deposits (one is typical, and one is associated with an Alaskan-type complex) are presented, and the results are compared. Data were assessed from historical and newly acquired airborne geophysical surveys that were collected over the Mayville property in southeast Manitoba and the Turnagain property in northern British Columbia. The properties were explored by Mustang Minerals Corporation and Giga Metals Corporation, respectively. Airborne electromagnetic (EM) and magnetic data were utilized to compare the two properties and the mineralized zones. The review showed that the Mayville magmatic sulfide deposit was directly detectible with EM methods, and the passive and active-source methods were complementary to one another. The EM data did not directly detect the Turnagain Alaskan-type deposit, but the magnetics data proved to be successful in defining the geologic framework. Implications for future targeting and exploration for economic Ni mineralization are considered.

Bayesian detectability of induced polarization in airborne electromagnetic data

SUMMARY Detection of induced polarization (IP) effects in airborne electromagnetic measurements does not yet have an established methodology. This work contributes a Bayesian approach to the IP detectability problem using decoupled transdimensional layered models and applies an approach novel to geophysics whereby transdimensional proposals are used within the embarrassingly parallelizable and robust sequential Monte Carlo class of algorithms for the simultaneous inference of parameters and models. This algorithm allows for adaptivity considerations for multiple models and proposal types. Methodological contributions to solid Earth geophysics include the decoupled layered model approach and proposal of a statistic that uses posterior model odds for IP detectability. A case study is included investigating the detectability of IP effects in airborne electromagnetic data at a broad scale.

A fast 3-D inversion for airborne EM data using pre-conditioned stochastic gradient descent

SUMMARY Airborne electromagnetic (AEM) exploration produces large amounts of data due to its high sampling rate, so that the 3-D inversions take extremely big computation and time consumption. We present a fast 3-D inversion framework for large-scale AEM explorations using a pre-conditioned stochastic gradient descent combined with Gauss–Newton (PSG-GN) method. We adopt a compressed sensing (CS) in the 3-D forward modelling, in which a random undersampling is used to reduce the calculation, while the responses for all survey stations are obtained via a reconstruction technique. For our 3-D AEM inversions, a method of combining the stochastic gradient descent with Gauss–Newton (SG-GN) that requires only a small data set in each iteration instead of the conventional full-batch data (complete original data) inversion have been investigated. To further speed up the 3-D inversion, we develop a pre-conditioner considering the random sampling rate and gradient noise to achieve a fast convergence. We use two synthetic models to test the accuracy, convergence and efficiency of our algorithm. The results show that the conventional inversion with full-batch data and the PSG-GN method can both converge quickly, but our method can enhance the inversion efficiency up to 78 per cent. Finally, we invert a field data set acquired from a massive sulfide deposit in Ireland and obtain the results that agree well with the known geologies.

Flexible quasi-2D inversion of time-domain AEM data, using a wavelet-based complexity measure

SUMMARYRegularization methods improve the stability of ill-posed inverse problems by introducing some a priori characteristics for the solution such as smoothness or sharpness. In this contribution, we propose a multidimensional scale-dependent wavelet-based ℓ1-regularization term to cure the ill-posedness of the airborne (time-domain) electromagnetic induction inverse problem. The regularization term is flexible, as it can recover blocky, smooth and tunable in-between inversion models, based on a suitable wavelet basis function. For each orientation, a different wavelet basis function can be used, introducing an additional relative regularization parameter. We propose a calibration method to determine (an educated initial guess for) this relative regularization parameter, which reduces the need to optimize for this parameter and, consequently, the overall computation time is under control. We apply our novel scheme to a time-domain airborne electromagnetic data set in Belgian saltwater intrusion context, but the scheme could equally apply to any other 2D or 3D geophysical inverse problem.

Two-dimensional fast imaging of airborne EM data based on U-net

As an efficient geophysical exploration tool, the airborne electromagnetic (AEM) method has been widely used in mineral exploration, geological mapping, environmental and engineering investigation, etc. Currently, the imaging and 1D inversions are the mainstream means for AEM interpretation as the amount of AEM data is huge and 2D and 3D inversions are not efficient. In this paper, we propose a 2D fast imaging method for frequency-domain AEM data based on U-net network. The U-net is a symmetric full-convolution neural network, in which the partial pooling operation between the convolution layers is replaced by the up-sampling operation, while the target location is achieved by skipping connection. This method does not need to consider the complex coupling between the EM responses and underground structures, but instead it establishes a mapping relationship between EM responses and the resistivity model and can quickly achieve accurate imaging of AEM data. We use this network to image both synthetic and field survey data and compare the results with the traditional inversion algorithms. The results show that the U-net imaging have high resolution at high speed that provides a new way for interpreting large amounts of AEM data.

Fast Bayesian Inversion of Airborne Electromagnetic Data Based on the Invertible Neural Network

The inversion of airborne electromagnetic (AEM) data suffers from severe non-uniqueness of the solution. Bayesian inference provides the means to estimate structural uncertainty with a rich suite of statistical information. However, conventional Bayesian methods are computationally demanding in nonlinear inversions, especially considering the huge volumes of observational data, and thus are not feasible in practice. In this study, we develop a fast Bayesian inversion operator based on the invertible neural network (INN) to fully explore the posterior distribution and quantitatively evaluate the model uncertainty. The INN uses a latent variable to capture the information loss during measurement and constructs bijective mappings between AEM data and the resistivity model. We also introduce another noise variable into the INN to account for data uncertainties. Synthetic tests demonstrate that the INN can effectively recover the posterior distribution by a relatively small ensemble of predicted resistivity models whose AEM responses show a significant agreement with the true signal. We also apply the INN inversion operator to a field data set and obtain results consistent with previous studies. The INN shows considerable adaptability to field observations and strong noise robustness. Meanwhile, the INN delivers the inversion result with posterior model distribution for 23366 AEM time series in 20 seconds on a common PC. The inversion efficiency can be further improved for large data set due to its natural parallelizability. The proposed INN method can support fast Bayesian inversion of AEM data and offer tremendous potential for near real-time uncertainty evaluation of underground structures.

Interpretation of Frequency-Domain Airborne Electromagnetic Data Based on the Deep Neural Network Incorporating Topographic Information

Deep neural networks (DNNs) have recently been used to interpret frequency-domain electromagnetic (EM) data, therefore vast amounts of information can be rapidly interpreted. However, in airborne surveys that include mountainous regions, severe topographic changes distort the EM data and they can lead to unreasonable interpretations. DNN-based inversions that reduce the effects of topographic changes have not yet been proposed whereas some conventional inversion techniques can correct topographic distortions. Since DNN-based interpretation needs various training dataset and its performance depends on the characteristics of training dataset, it is important to generate various AEM data and resistivity model pairs. However, it is almost impossible to derive the EM responses of two-dimensional (2D) or 3D resistivity models incorporating diverse topographic patterns because such models require many variables and computation costs are limited. Therefore, we suggested the pseudo 1D interpretation of EM data which can consider the topographic changes using EM responses and topographic information at three neighboring data points. We include topographic information in DNN training as slope angle between the data points. Compared to conventional inversion, our trained DNN model recovers the synthetic resistivity models more reasonably. In addition, we used the trained DNN model to evaluate a real AEM dataset from south-central Alaska. The trained DNN provided reasonable interpretation results despite that the data were acquired at mountainous area with rough topography. Since the trained DNN model can provide predictions rapidly, the interpretation time was drastically reduced. We believe that this research can increase the practical field applicability of AEM survey.

Three-Dimensional Airborne Electromagnetic Data Inversion With Flight Altitude Correction

The flight altitude has a large effect on the airborne electromagnetic (AEM) responses. Due to the dynamic environment of the aircraft, the recorded sensor altitudes may contain errors. Research demonstrates that the AEM responses caused by a several meters altitude errors can be larger than caused by some anomalous body. Ignoring these errors will create erroneous results in AEM data interpretation. Considering that there is not yet a published 3D AEM inversion method that takes into account the flight altitude, we develop in this paper a 3D inversion algorithm for AEM with the flight height treated as an inversion parameter. For the forward modeling we use the finite element method, while for the inversion we use the Gauss-Newton optimization method. To make our inversion works for variable flight altitudes, we propose a scheme of 3D Jacobean matrix calculation for both the resistivities and flight altitudes without much increasing the computational cost. The numerical simulation result confirms that the flight altitude really has a large effect on the AEM responses. The inversions of synthetic data show that our 3D inversion method can both recover the resistivity distribution in the underground and decrease the altitude errors recorded, while the field data inversion demonstrates that our method can deliver a better inversion model with a smaller data misfit.

Airborne geophysical method images fast paths for managed recharge of California’s groundwater

Given the substantial groundwater level declines in the Central Valley of California, there is an urgent need to supplement the recharge of the groundwater systems by implementing managed aquifer recharge. With approximately 170 km3 (140 million acre-feet) of available groundwater storage space, water deemed to be excess during wet years could be spread on the ground surface at selected locations allowing it to move downward to recharge the underlying aquifer system. Along the eastern edge of the Central Valley there are large paleovalleys that can act as fast paths expediting the downward movement of water. These paleovalleys, incised and then filled with coarse-grained materials—sand, gravel, cobbles—at the end of the last glacial period, are referred to as incised valley fill (IVF) deposits. An IVF deposit has been mapped at one location in the Kings River alluvial fan, with others proposed to exist in the fans of major rivers. If located, these deposits would be optimal sites for managed recharge. In this study, we assessed the use of a helicopter-deployed geophysical method to efficiently locate IVF deposits throughout the Central Valley. We acquired 542 line-kilometers of airborne electromagnetic (AEM) data in the Kings River alluvial fan, with dense line-spacing over the Kings River IVF deposit which had been mapped as ∼2 km wide, extending over 20 km into the Central Valley, from the ground surface to a depth of 30 m. The IVF deposit was unambiguously imaged in the AEM data as an extensive linear feature that was more electrically resistive than the surrounding materials due to the high percentage of coarse-grained sediments. This study provides the evidence to support the rapid adoption of the AEM method to locate IVF deposits along the eastern edge of the Central Valley. These deposits provide valuable natural infrastructure for recharging California’s groundwater.

Geophysical data provide three dimensional insights into porphyry copper systems in the Silverton caldera, Colorado, USA

The Silverton caldera in southwest Colorado, USA hosts polymetallic veins and pervasively altered rocks indicative of porphyry copper systems. Nearly a kilometer of erosion has exposed multiple levels of the hydrothermal systems from shallow lithocaps down to quartz-sericite-pyrite veins. New airborne electromagnetic and magnetic survey data are integrated with previous alteration mapping and porphyry models to show the subsurface geophysical response of shallow to deep levels of the porphyry system. Qualitative map views show lateral changes in the magnetization and resistivity of the hydrothermally altered rocks. The volcanic terrain exhibits high magnetization and high amplitude anomalies map near-surface plutonic rocks associated with porphyry systems. Magnetic susceptibility measurements on outcrops of hydrothermally altered rocks indicate magnetite content decreases upward and outward from the source intrusions where magnetic anomaly lows are observed over the lithocaps. The resistivity maps highlight hydrothermal alteration as resistivity lows with exception being rocks having propylitic alteration. Quantitative resistivity models show low resistivity zones with an apparent thickness around 50–150 m beneath quartz-sericite-pyrite veins interpreted to be the result of supergene processes that may continue today, and the calculated magnetic source depths occur near the top of this zone. The resistivity models also show rocks having propylitic, silicic, and quartz-alunite-pyrophyllite assemblages exhibit high resistivity with depth, and argillic alteration assemblages had high resistivity due to high quartz content. This integrated approach presented in a three-dimensional environment provides guidance when exploring for porphyry copper systems in less exposed terrains.

Novel Airborne EM Image Appraisal Tool for Imperfect Forward Modeling

Full 3D inversion of time-domain Airborne ElectroMagnetic (AEM) data requires specialists’ expertise and a tremendous amount of computational resources, not readily available to everyone. Consequently, quasi-2D/3D inversion methods are prevailing, using a much faster but approximate (1D) forward model. We propose an appraisal tool that indicates zones in the inversion model that are not in agreement with the multidimensional data and therefore, should not be interpreted quantitatively. The image appraisal relies on multidimensional forward modeling to compute a so-called normalized gradient. Large values in that gradient indicate model parameters that do not fit the true multidimensionality of the observed data well and should not be interpreted quantitatively. An alternative approach is proposed to account for imperfect forward modeling, such that the appraisal tool is computationally inexpensive. The method is demonstrated on an AEM survey in a salinization context, revealing possible problematic zones in the estimated fresh–saltwater interface.

Use of Machine Learning to Estimate Statistics of the Posterior Distribution in Probabilistic Inverse Problems—An Application to Airborne EM Data

AbstractThe solution to a probabilistic inverse problem is the posterior probability distribution for which a full analytic expression is rarely possible. Sampling methods are therefore often used to generate a sample from the posterior. Decision‐makers may be interested in the probability of features related to model parameters (e.g., existence of pollution or the cumulative clay thickness) rather than the individual realizations themselves. Such features and their associated uncertainty, are simple to compute once a sample from the posterior distribution has been generated. However, sampling methods are often associated with high computational costs, especially when the prior and posterior distribution is non‐trivial (non‐Gaussian), and when the inverse problem is non‐linear. Here we demonstrate how to use a neural network to directly estimate posterior statistics of continuous or discrete features of the posterior distribution. The method is illustrated on a probabilistic inversion of airborne EM data from Morrill Nebraska, where the forward problem is nonlinear and the prior information is non‐Gaussian. Once trained the application of the network is fast, with results similar to those obtained using much slower sampling methods.