Nikolaj Foged

An integrated processing scheme for high-resolution airborne electromagnetic surveys, the SkyTEM system

The SkyTEM helicopter-borne transient electromagnetic system was developed in 2004. The system yields unbiased data from 10 to 12 μs after transmitter current turn-off. The system is equipped with several devices enabling a complete modelling of the movement of the system in the air, facilitating excellent high-resolution images of the subsurface. An integrated processing and inversion system for SkyTEM data is discussed. While the authors apply this system with SkyTEM data, most of the techniques are applicable for airborne electromagnetic data in general. Altitude data are processed using a simple recursive filtering technique that efficiently removes reflections from trees. The technique is completely general and can be used to filter altitude data from any airborne system. Raw voltage data that are influenced by electromagnetic coupling to man-made structures are culled from the dataset to avoid uncoupled data being distorted by coupled data, and geometrical corrections are applied to correct for pitch and roll of the transmitter frame. Data are de-spiked and averaged using trapezoid-shaped filter kernels. A Laterally Constrained Inversion using smooth models is actively used to evaluate the processing, and the final inversion is tightly connected to the processing procedures.

An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data

We present an overview of a mature, robust and general algorithm providing a single framework for the inversion of most electromagnetic and electrical data types and instrument geometries. The implementation mainly uses a 1D earth formulation for electromagnetics and magnetic resonance sounding (MRS) responses, while the geoelectric responses are both 1D and 2D and the sheet’s response models a 3D conductive sheet in a conductive host with an overburden of varying thickness and resistivity. In all cases, the focus is placed on delivering full system forward modelling across all supported types of data. Our implementation is modular, meaning that the bulk of the algorithm is independent of data type, making it easy to add support for new types. Having implemented forward response routines and file I/O for a given data type provides access to a robust and general inversion engine. This engine includes support for mixed data types, arbitrary model parameter constraints, integration of prior information and calculation of both model parameter sensitivity analysis and depth of investigation. We present a review of our implementation and methodology and show four different examples illustrating the versatility of the algorithm. The first example is a laterally constrained joint inversion (LCI) of surface time domain induced polarisation (TDIP) data and borehole TDIP data. The second example shows a spatially constrained inversion (SCI) of airborne transient electromagnetic (AEM) data. The third example is an inversion and sensitivity analysis of MRS data, where the electrical structure is constrained with AEM data. The fourth example is an inversion of AEM data, where the model is described by a 3D sheet in a layered conductive host. We present an overview of a mature and general algorithm for inversion of most electromagnetic and geoelectrical data, ground-based and airborne. The implementation uses a 1D formulation for electromagnetics and MRS responses, geoelectric responses are 1D and 2D, and the 3D sheet’s response implements an overburden of varying thickness and resistivity.

Combining 3D geological modelling techniques to address variations in geology, data type and density - An example from Southern Denmark

The very complex near-surface geology in Denmark is a big challenge when constructing 3D geological models. Borehole data alone are normally insufficient for proper 3D modelling because data are too widespread. Therefore, Airborne ElectroMagnetic (AEM) techniques are often used to obtain supplementary information on the spatial distribution and composition of the geology.A large-scale AEM survey and high-resolution seismic data along with both new and existing borehole data and seismic data from hydrocarbon exploration were available for the construction of a detailed 3D geological model in our study area. The data are unevenly distributed, and only part of the study area was covered by the AEM survey. Cross-cutting tunnel valleys, erosional unconformities, delta units and a large glaciotectonic complex are among the geological features identified in the area. The geological complexity varies significantly across the model area.A broad geological overview and understanding of the area was obtained by joint cognitive interpretation of the geophysical and the geological data. To address the geological complexity and the very high level of detail gained from the AEM data, the model was constructed as a voxel model with lithofacies attributes supplemented by a number of bounding surfaces. In areas where the geology is not too detailed and complex, the model was constructed manually, whereas automated methods were used to populate voxels in areas with a high complexity. The automated methods comprised clay fraction modelling, which was used where AEM data are available, and stochastic modelling, which was used outside the area covered by AEM data.Our study shows that it is advantageous to combine several modelling methods in areas with varying geological complexity and data density. The choice of modelling methods should depend on the character and coverage of available data and on variations in geology throughout the model area. We present a 3D geological model combined by three different techniques.The choice of modelling technique is guided by geology and data type/density.An interactive manual approach is preferable where the geology is simple.Automated approaches are preferable where the geology is complex.Results from the three models were merged into a final voxel model.

Airborne EM mapping of rockslides and tunneling hazards

We have investigated potential rockslides in Western Norway using a time- and cost-efficient airborne electromagnetic (AEM) survey approach. The study area comprises phyllite, a low-grade metamorphic rock type that tends to be reworked to clay in disturbed zones. Mapping these electrically conductive clay zones was the aim of the AEM survey. Based on indications that precipitation drives the reported rockslide movements, the local municipality and regional hydroelectricity company are evaluating the option of draining the unstable area to a nearby hydropower reservoir using a drainage tunnel of more than 10 km. We conducted the AEM mapping survey to locate the sliding planes and to investigate the tunnel corridor for areas with potential tunneling hazards. Spatially constrained inversion of the data set (250 km) reveals extended conductive zones interpreted as sliding planes and/or gneiss/phyllite interface. Detailed follow-up of these initial results is planned with targeted percussion drilling and groun...

Environmental and Process Monitoring

For enhanced gas recovery (EGR) using CO2 as well as for CO2 storage in depleted gas fields it needs to be shown that injection and storage is save and neither population nor environment is exposed to risks during operation or afterwards. This requires the development and application of methods to monitor groundwater, vadose zone and atmosphere. Therefore, extensive investigations of the near-surface aquifers were performed to characterize the geological structure and the geochemical and hydraulic conditions as part of a baseline-monitoring and to specify input parameters for model simulations. If CO2 leakage should occur and CO2 migrates upwards from the storage complex, shallow freshwater aquifers are the first protected good that might be affected. Based on the model simulations, parameters that would be affected by leakages were specified and parameter changes as well as spatial extension of the expected changes quantified. A comparison of the model results with measured natural variabilities show that especially pH and TIC (total inorganic carbon), but under certain conditions also electric conductivity and aqueous calcium concentration (Ca) are most suited parameters for the detection of CO2 leakages based on observation wells in shallow aquifers. It was an important result that the temporal fluctuations of groundwater composition are generally small but spatial variations are large.

Permeability Estimation Directly From Logging‐While‐Drilling Induced Polarization Data

In this study, we present the prediction of permeability from time domain spectral induced polarization (IP) data, measured in boreholes on undisturbed formations using the El-log logging-while-drilling technique. We collected El-log data and hydraulic properties on unconsolidated Quaternary and Miocene deposits in boreholes at three locations at a field site in Denmark, characterized by different electrical water conductivity and chemistry. The high vertical resolution of the El-log technique matches the lithological variability at the site, minimizing ambiguity in the interpretation originating from resolution issues. The permeability values were computed from IP data using a laboratory-derived empirical relationship presented in a recent study for saturated unconsolidated sediments, without any further calibration. A very good correlation, within 1 order of magnitude, was found between the IP-derived permeability estimates and those derived using grain size analyses and slug tests, with similar depth trends and permeability contrasts. Furthermore, the effect of water conductivity on the IP-derived permeability estimations was found negligible in comparison to the permeability uncertainties estimated from the inversion and the laboratoryderived empirical relationship.

Artificial neural networks for removal of couplings in airborne transient electromagnetic data

Modern airborne transient electromagnetic surveys typically produce datasets of thousands of line kilometres, requiring careful data processing in order to extract as much and as reliable information as possible. When surveys are flown in populated areas, data processing becomes particularly time consuming since the acquired data are contaminated by couplings to man-made conductors (power lines, fences, pipes, etc.). Coupled soundings must be removed from the dataset prior to inversion, and this is a process that is difficult to automate. The signature of couplings can be both subtle and difficult to describe in mathematical terms, rendering removal of couplings mostly an expensive manual task for an experienced geophysicist. Here, we try to automate the process of removing couplings by means of an artificial neural network. We train an artificial neural network to recognize coupled soundings in manually processed reference data, and we use this network to identify couplings in other data. The approach provides a significant reduction in the time required for data processing since one can directly apply the network to the raw data. We describe the neural network put to use and present the inputs and normalizations required for maximizing its effectiveness. We further demonstrate and assess the training state and performance of the network before finally comparing inversions based on unprocessed data, manually processed data, and artificial neural network automatically processed data. The results show that a well-trained network can produce high-quality processing of airborne transient electromagnetic data, which is either ready for inversion or in need of minimal manual processing. We conclude that the use of artificial neural network scan significantly reduce the processing time and its costs by as much as 50%.

Sharp Spatially-decoupled Inversion of Airborne Electromagnetic Data for Improved Model Integration

One of the main limiting factors to the accuracy of large scale groundwater models is the scarcity of hydraulic data. High-resolution Airborne Electromagnetic Methods (AEM) are capable of mapping the electrical resistivity structure of the subsurface in great detail and covering large areas in short time and on a limited budget. As such, there is great potential in integrating AEM data in groundwater modeling as a supplementing source of an extensive amount of information. We have developed several novel techniques that in combination allows for bringing groundwater and AEM models much closer together, i.e.: (1) a novel, scalable inversion engine that allows the AEM inversion to handle arbitrarily large areas at a time; (2) the spatially-decoupled inversion approach, which decouples the inversion model from the acquisition points and can operate on the same grid/voxel cells as the groundwater model; (3) a custom regularization scheme that allows for producing geophysical models with sharp vertical/horizontal resistivity transitions. In this study we present the very first application of the sharp spatially-decoupled inversion on an AEM survey flown for improving the groundwater model in the Kasted area, in the north of Aarhus (Denmark).

Inverting for Lithology Using Resistivity Models and Boreholes

We present an automatic method for parameterization of a 3D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of further detailing for geological models or as direct input to groundwater models. The parameter of interest is the clay fraction, expressed as the relative length of clay-units in a depth interval. The clay fraction is obtained from lithological logs and the clay fraction from the resistivity is obtained by establishing a simple petrophysical relationship, a translator function, between resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3D clay fraction model, which holds information from the resistivity dataset and the borehole dataset in one variable. We apply the concept to the Norsminde survey in Denmark integrating approximately 700 boreholes and more than 100,000 resistivity models from an airborne survey in the parameterization of the 3D model covering 156 km2. The final 3D model differentiates between clay materials and different high resistive materials from information held in resistivity model and borehole observations respectively

Automatic Generation of Groundwater Model Hydrostratigraphy from AEM Resistivity and Boreholes

Regional hydrological models are important tools in water resources management. Model prediction uncertainty is primarily due to structural (geological) non-uniqueness which makes sampling of the structural model space necessary to estimate prediction uncertainties. Geological structures and heterogeneity, which spatially scarce borehole lithology data may overlook, are well resolved in AEM surveys. This study presents a semi-automatic sequential hydrogeophysical inversion method for the integration of AEM and borehole data into regional groundwater models in sedimentary areas, where sand/clay distribution govern groundwater flow. The coupling between hydrological and geophysical parameters is managed using a translator function with spatially variable parameters followed by a 3D zonation. The translator function translates geophysical resistivities into clay fractions and is calibrated with observed lithological data. Principal components are computed for the translated clay fractions and geophysical resistivities. Zonation is carried out by k-means clustering on the principal components. The hydraulic parameters of the zones are determined in a hydrological model calibration using head and discharge observations. The method was applied to field data collected at a Danish field site. Our results show that a competitive hydrological model can be constructed from the AEM dataset using the automatic procedure outlined above.