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In the present study, we have used 3D ERT for detailed mapping of a polluted site at a challenging river site in the southern part of the Denmark. The survey layout involves several crossings of the meander of a river where a leachate plume from an old chemical factory site is expected to reach the river. Both resistivity and time domain induced polarization data were acquired, and full 3D inversion was performed for the resistivity data and 2D inversion of the IP data. Two sets of 3D data were acquired; one at a larger scale covering total area 410x90m2 and one at small scale covering 126x42m2, which enabled both details in the shallow part while covering a large area. 3D DC data were inverted using the inversion package BERT and 2D DC-IP data were inverted using AarhusInv. 3D DC inversion models show the presence of low resistivity anomaly at the northern bank of the stream interpreted as a contaminant plume. The contaminant plume is also seen in a deeper aquifer, which is separated by a low permeable clay layer to the upper aquifer. We could not discriminate the clay-lithology from the contamination using only resistivity model however, including the 2D inversions of the IP data we clearly separate the clay layer from the contaminated aquifer.

Modern airborne transient electromagnetic surveys typically span thousands of line kilometres requiring careful data processing. When surveys are flown in populated areas, data processing becomes particularly time consuming, since the acquired data is contaminated by couplings to man made conductors (power lines, fences, pipes, etc.). Coupled soundings must be removed from the dataset prior to inversion, but since the signature of couplings can be subtle and difficult to describe in general terms it has so far remained mostly a manual task. We train an artificial neural network to recognize coupled soundings in previously processed data and use this network to identify couplings in other data. The approach provides a dramatic reduction in the time required for data processing, since one can directly apply the network to the raw data. We present the training state and performance of the network and compare inversions based on manually processed data and ANN processed data. The results show that a well trained network can produce a high quality processing of ATEM data, which are either ready for inversion or in need of minimal manual processing. The results are very promising and can significantly reduce the processing time and cost of large ATEM surveys.

While full 2D and 3D inversion schemes are emerging for airborne TEM datasets, the workhorse of large scale surveying still remains the well established 1D forward formulation. Modern airborne TEM surveys typically span several thousand line kilometers of data, which can be very time consuming to invert even within the framework of 1D forward modeling. Here, we present how we have modified our existing inversion code to offload its 1D forward computations to massively parallel Intel Xeon Phi co-processors. A prerequisite for good performance on this type of next generation technology is for a code to provide not only good parallel scaling, but also make efficient use of vector instructions. The latter is not possible without modification to the established framework of 1D forward modeling and we demonstrate how this problem can be overcome in a straight forward manner. We show how the modified algorithm provides virtually ideal parallel scaling and almost ideal use of vector instructions on both multi-core processors and massively parallel Intel Xeon Phi co-processors. The use of vector instructions alone provide a speed-up of almost 8x on the co-processor, allowing for full inversion of several thousand line kilometers of data per hour.

Airborne electromagnetic methods (AEM) have in the last decade undergone immense hardware and software developments. 10 years ago high resolution shallow surveys for geotechnical engineering, aquifer mapping, landslides etc, could only be done by frequency domain methods whereas time domain methods were more suited for deep measurements. Today, the development of the time domain methods has reached a level where the same or even better data from an airborne platform is obtained compared to the best groundbased system. Also, data inversion has changed from being dominated by approximate algorithms to full solutions modelling the complete system characteristics with inversion of thousands of line kilometers of data in one run. These inversions are possible because the algorithms use multithreaded architecture, which efficiently runs on small workstations or severs. In this paper we discuss the newest hardware developments of the SkyTEM system and also how these developments are accompanied by inversion algorithms closely adapted to the hardware. The SkyTEM system, as shown in Figure 1, originally developed at Aarhus University, is now operated and developed by an independent spin-off company, SkyTEM Surveys, while inversion and processing algorithms are developed at Aarhus University. However, there are fruitful collaborations and SkyTEM Surveys identify themselves as a high-tech company with an ambitious research and development (R/D) program.

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.

We present a comparison between two different regularisation schemes for multi-layer inversion of the MRS data. The two regularizations consist of the norm (Occam’s inversion) and the support (focused inversion) of the variation of the water content and relaxation time. The focused regularization provides a sharp inversion as it minimizes the area (the support) where the variations of the model parameters occur, and not the (L2) norm of the variations. Clearly, when sharp vertical transitions of the inversion parameters are present, the focused inversion retrieves the interfaces with higher precision and reconstructs more effectively the physical property values as well. This is a quite common situation in hydrogeophysics, e.g. in correspondence with the water table and/or at the boundary between aquifers and aquitards.

We present a geophysical inversion algorithm working directly in a voxel grid disconnected from the actual measuring points, which allows for straightforward integration of different data types in joint inversion, for informing geological/hydrogeological models directly and for easier incorporation of prior information. Inversion of geophysical data usually refers to a model space being linked to the actual observation points. For airborne surveys the spatial discretization of the model space reflects the flight lines. Often airborne surveys are carried out in areas where other ground-based geophysical data are available. The model space of geophysical inversions is usually referred to the positions of the measurements, and ground-based model positions do not generally coincide with the airborne model positions. Consequently, a model space based on the measuring points is not well suited for jointly inverting airborne and ground-based geophysical data. Furthermore, geological and groundwater models most often refer to a regular voxel grid not correlated to the geophysical model space, and incorporating the geophysical data into the geological/hydrological modelling grids is problematic. We present a voxel grid inversion routine that overcomes these problems and we discuss in detail the algorithm implementation.

In the computation of the forward response for Time Domain Induced Polarization the incomplete description of the transmitter waveform causes dramatic errors in the estimation of the magnitude and time characteristic of the IP phenomenon. In fact both the duration of the current pulse and the sequence of pulses used for the stacking procedure have a strong effect in the magnitude and shape of the IP decays. Furthermore, it is important to model, if present, the low-pass filters of the receivers, in order to use all the information contained in the acquired data. For these reasons, a new 1D forward and inversion algorithms have been developed using the full time decay of the IP response and the receiver transfer function to reconstruct the distribution of the four Cole-Cole parameters of the earth. The waveform implementation in the forward response for TDIP is a significant improvement that allows moving from a qualitative interpretation of TDIP data for recognition of anomaly patterns towards a quantitative analysis, able to discriminate soil lithotypes and, if present, some contamination patterns.

Airborne electromagnetics (AEM) is an efficient tool for mapping the subsurface. AEM delivers a very high data coverage compared to the costs, and the direct outputs are high-resolution resistivity images of the subsurface. In particular the time domain methods (TEM) are well suited for mapping of the salt-fresh water boundary in coastal zones, aquifers, paleo-channels, mineralisations and general structural geological mapping. The development of AEM systems and data processing systems has been intensive during the past 10– 15 years because of better electronics and faster and more flexible computers. While most AEM systems in the 1980s were limited to detection of mineralisations, modern system generations measures full spectra, yields absolutely calibrated data and accurate descriptions of the system transfer function. This, in combination with much enhanced inversion and forward algorithms, makes AEM a suitable candidate for mapping targets which so far have not be possible.