AEGC 2019: From Data to Discovery – Perth, Australia 1

Handling noise in AEM inversion – implications for subsurface characterisation Shane Mulè Camilla Soerensen Tim Munday CSIRO CSIRO CSIRO Perth, Australia Perth, Australia Perth, Australia shane.mule@csiro.au camilla.soerensen@csiro.au tim.munday@csiro.au

INTRODUCTION

The comparison of algorithms for the inversion of airborne electromagnetic (AEM) data are frequently undertaken to assess the performance, effectiveness or validity of new methods, for example, when comparing higher order inversions 2.5D/3D verses 1D approaches (Cox et al., 2010; Lawrie et al., 2018; Munday et al., 2018). However, comparative studies often neglect to note that the inversion of AEM data is not an objective process as it requires decisions about the inversion model, regularisation, filtering, and not least how noise present in AEM data is handled during inversion. Additionally, the impact of noise handling is often overlooked when assessing AEM inversion results, however these decisions can significantly influence what is interpretable from data. Noise present in AEM data fundamentally limits what can be extracted through inversion. Traditionally, the noise of an electronic measurement system would require an understanding of all noise sources and how they contribute to the total noise present in measured data. Due to the complexity of AEM systems this approach is difficult to undertake and therefore various methods are used to estimate or quantify noise present in AEM data. For AEM inversion these methods can include survey wide estimates such as; using a data noise floor (Sattel, 2005), allocating a percentage error to data (Raiche, 1998), estimating a systems’ additive and multiplicative noise (Brodie, 2016; Green & Lane, 2003), and manually or automatically extracting noise from each data measurement (Auken et al., 2009). Of these methods two main techniques for noise quantification are routinely used, namely system noise estimation (Green & Lane, 2003) and noisy data removal (Auken et al., 2009).

The method by Green & Lane (2003), requires the estimation of a system noise level by analysis of the standard deviation of data measured at high altitude (additive noise) and the repeatability of data measured on multiple occasions over the same ground (multiplicative noise). Given the relative simplicity and robustness of this method, it is a regularly used approach for estimating the noise present in AEM data sets (see, for example, Annetts & Hauser, 2015; Brodie & Fisher, 2008; Brodie & Sambridge, 2009; Christensen & Lawrie, 2013; Ley-Cooper et al., 2015). The method developed by Auken et al. (2009), requires the allocation of noise levels or removal of noisy data points in a dataset which is typically done via a manual process. This method is more complex and time consuming, however it provides an ability to better define the noise of AEM data as it varies across a dataset. The advent of software in recent years with an ability to quantify noise per data measurement (‘Aarhus Workbench’, 2019; Golodoniuc et al., 2018), has seen this approach become more widely used to quantify noise present in AEM data (see, for example, Bedrosian et al., 2016; Munday et al., 2013; Reninger et al., 2018; Viezzoli et al., 2012). This study aims to compare results from two different ways of handling noise using two 1D inversion algorithms. We examine results from GALEISBSTDEM (GALEI) (Brodie, 2016) which is traditionally used with the method defined by Green & Lane (2003), and from AarhusInv (Auken et al., 2015) which uses the method by Auken et al. (2009). More specifically the paper describes how noise handling may influence what could be interpreted from the resulting inversions. This comparison is undertaken using a SkyTEM AEM dataset from the McArthur Basin, in the Northern Territory (NT), Australia.

METHODS Firstly, we outline how noise can be accounted for in the two inversion schemes, then we show an example of modelled results of the same data using these two methods, and finally we apply the approach employed in Aarhus Workbench to GALEI and discuss the outcomes. AARHUSINV AarhusInv (formerly known as EM1DINV) was developed by the Hydrogeophysics Group at Aarhus University (Auken et al., 2005). The inversion algorithm allows allocation of standard deviation and the masking of noise to any part of the data which is noisy or erroneous. These data may cross multiple fiducials or may only impact a few individual time gates. Data sets which have gone through this process are deemed “processed”. In this study Aarhus Workbench

SUMMARY Noise is a fundamentally limiting factor for what aspects of the subsurface can be imaged using an AEM system. This paper compares the use of two inversion techniques and two different methods of estimating data noise, to determine how the choice of noise handling impacts inversion results. The use of processing to remove noisy data from an AEM dataset is shown to provide an improved result over using more traditional survey wide system noise level estimates. This result highlights the importance of critically assessing noise present within AEM data and provides a foundation for further development of techniques which can identify and remove noisy data. Key words: AEM inversion, noise, processing.

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(‘Aarhus Workbench’, 2019) was used to manually mask data as appropriate. During inversion masked fiducials and time gates are not inverted and have no impact on the inverted solution. GALEI Geoscience Australia Layered Earth Inversion (GALEI) is a 1D layered earth inversion algorithm developed by Geoscience Australia (Brodie, 2016; Brodie & Sambridge, 2009). This inversion algorithm typically uses system noise estimates for a given system, based on derived additive and multiplicative noise (Green & Lane, 2003), to indicate the level of noise present at each time gate. In this study, the additive noise has been estimated from the relevant high altitude data acquired during survey and the multiplicative noise has been set at a nominal 3%. These values specify the noise level and multiplier which is applied to each time gate and all measurement points across the survey to determine the level of noise used during inversion. Individual time gates or fiducials can be allocated externally specified noise levels, although it is not routinely done. It is not possible however to mask data and exclude it from the inversion. Due to this limitation, when externally specified noise levels are used in GALEI, it is common practise to imitate data masking by allocating noisy or erroneous data with large noise levels. In this study this large noise level method has been used, however the validity of this method, which is routinely used in the industry, is not known. One possible consequence of this approach may be that noise in the measured data still has some influence on the inverted response even though it has been deemed as noise.

RESULTS The unprocessed Low and High moment SkyTEM AEM data used for this study are presented in Figure 1. In an effort to reinforce the importance of accounting for noise, the unprocessed data was inverted without any allocated noise levels (Figure 2). The inversion results of the unprocessed data, without any allocated noise level, is clearly erroneous at most locations along the section, notably at depths below -100 metres AHD. As previously stated, calculated additive and multiplicative noise is typically used in GALEI to quantify the noise levels present in data. Inverting the SkyTEM data in GALEI using this method of noise estimation is presented in Figure 3. This method provides an improvement over the inverted result in which no noise level was used (Figure 2).

Figure 1. Unprocessed SkyTEM responses for the low (top) and high (bottom) moment for a line from the McArthur Basin, NT.

Figure 2. Unprocessed SkyTEM data inverted using AarhusInv (top), GALEI (bottom). For this inversion no noise level was specified for any time gate or measurement location. Shaded areas in the upper section indicate the Depth of Investigation (DOI) for the AarhusInv inversion, and the black line in the lower section indicates the DOI for the GALEI inversion.

Figure 3. SkyTEM data inverted using GALEI in which additive and multiplicative noise estimates were used during inversion. The black line indicates the DOI for the GALEI inversion. The unprocessed data was processed using the methodology outlined in Auken et al. (2009). This produced a dataset where data points deemed to be noise were removed (Figure 4).

Figure 4. Processed SkyTEM responses for the low (top) and high (bottom) moment for a line from the McArthur Basin, NT, greyed out areas are masked data points which are not used in the inversion. The processed data inverted using both AarhusInv and GALEI is presented in Figure 5. Since GALEI does not have the

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ability to ignore data points removed during processing, these data points are allocated a large noise level during inversion to imitate removal. The results presented in Figure 5 show that both inversion methodologies produce a similar result when a processing method is employed which removes noisy data. A comparison between the unprocessed (Figure 2) and processed inversion results (Figure 5) highlights similarities between the two in the top 100 metres but an increasing degree of difference with depth. Of specific note are the deeper conductive features in the unprocessed inversion results at x-axis distances of 500 and 5000 metres, which are significantly improved with processing. Most evident is the improvement processing provides to the inversion results at depths greater than -200 metres AHD. Comparison of the inversion result from processed data (Figure 5) and the system noise method for noise estimation (Figure 3), shows significant improvement as a result of processing, particularly in the definition of conductivity structures at depths below 0 metres AHD. Although it is common practise to process data for noise removal when inverting data using AarhusInv, it is rarely applied to data inverted using GALEI. The improved, albeit more time-consuming result, achieved in this instance highlights the importance of undertaking a robust approach to noise handling, especially in resolving subsurface conductivity structure at depth.

Figure 5. Processed SkyTEM data inverted using AarhusInv (top) and GALEI (bottom). In AarhusInv individual time data points which were removed through processing are disregarded by the inversion algorithm. In GALEI these data points have been allocated a large noise level to imitate removal. Shaded areas in the upper section indicate the DOI for the AarhusInv inversion, and the black line in the lower section indicates the DOI for the GALEI inversion.

CONCLUSIONS The handling of noise during AEM inversion is an often overlooked, but critical aspect, of the inversion process. Traditionally different methods of estimating system noise levels have been used to apply survey wide limits on what can be resolved from AEM data through inversion. Although this method may be appropriate for some areas of a survey, it may not adequately reflect the changing nature and amplitude of noise typically encountered throughout a dataset. A method of accounting for the changing nature of noise is to use an

approach where noisy data is removed and not used during inversion. In this study, we have demonstrated that removing noisy data from an AEM dataset prior to inversion, can provide an improved inversion result and may help to better define the conductivity structures imaged in the subsurface. The use of alternative methods of quantifying and removing noisy data, including full waveform data analysis, automated algorithms and machine learning based approaches, have been not explored here but will form the basis of future research.

ACKNOWLEDGEMENTS The authors would like to thank Todd River Resources and the Innovation Connections grant which allowed this work to be carried out.

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