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

The removal of native vegetation for the development of land has caused the salinisation of surface soil and water resources in Australia. It is important to determine the existence of potential salinity hazards so that preventative measures may be taken at an early stage. The current hydrogeological conditions and the topography of a given catchment influence the movement of saline groundwater, and it is of particular importance to identify areas of high- and low-recharge to groundwater. On a catchment scale, the delineation of high-recharge areas by drilling can be prohibitively expensive, and more efficient mapping methods are required. Broadband electromagnetic (EM) sounding methods offer the possibility of obtaining the vertical conductivity distribution in the regolith, to produce three-dimensional maps of the resistivity structure. These maps could be used to interpret the distribution of features related to hydrology, such as recharge and stratification of stored salt in catchments. For salinity-related problems, a regolith depth of about 50 m is relevant. It is therefore necessary to measure the high-frequency content of the EM response to differentiate shallow conductive layers. For both ground-based and airborne EM methods, the response has been modelled in a frequency range of a few hundred Hz to 50 kHz. Salinity profile types have been identified to characterise different types of recharge. These profile types have been simulated with models consisting of horizontal layers of different resistivity. The theoretical modelling has determined that EM methods can be used to distinguish different types of salinity profiles. These results are supported by inversion of EM data collected in the field in a number of catchments. The transient electromagnetic (TEM) method is used for ground-based broadband EM measurements, and modelling shows that both the depth and thickness of the salt accumulation can be resolved when its depth is as little as 6 m and its thickness 10 m. Additionally, a relatively resistive basement below the salt accumulation may be distinguished from a conductive one. Since the former case implies the presence of a high-recharge salinity profile and the latter case a low-recharge profile, this method may be used to differentiate the two types of profiles. Down-hole conductivity logs in catchments near Collie and at East Yornaning, WA, confirm that different salinity profiles can be distinguished with EM methods. At Collie, the TEM method has detected the presence of salt accumulations and has determined the depth of their lower boundary. Inversion of these results shows the presence of a resistive electrical basement below the salt accumulations, and therefore indicates that relatively high recharge conditions are associated with these salinity profiles. Similarly, measurements along traverse lines in the East Yornaning catchment show a correlation between the inversions results and known geological features.

The use of ground-based electromagnetic methods (transient electromagnetics (TEM) and radiomagnetotellurics (RMT)) is increased in last decades for hydrogeological purposes. However, airborne electromagnetic methods were also proposed as a possible tool for near surface investigations due to the capability to cover large areas in short time. By jointly inverting different kinds of geophysical measurements at a site, the ambiguity inherent in different geophysical methods can be avoided. In order to couple spatial data from helicopter-borne electromagnetics (HEM) with ground-based electromagnetics, a common 1D joint inversion algorithm for HEM, TEM and RMT data is developed. The depth of investigation of HEM data is rather limited compared to time-domain EM sounding methods. In order to improve the accuracy of model parameters of shallow as well as of the deeper subsurface, the HEM, TEM, and RMT measurements are combined using joint inversion methodology. The 1D joint inversion algorithm is verified for the synthetic HEM, TEM and RMT data. The proposed concept of joint inversion takes advantage of each single method, which provides the capability to resolve near surface (RMT and HEM) and deeper electrical structures (TEM). Furthermore, the joint inversion is realized for the field data (HEM and TEM) from Cuxhaven area, Germany.

An increase in demand for commodities coupled with a decrease in world-class ore deposit discoveries in the last three decades is the driving force for exploring for a new generation of world-class ore deposits at depth. Exploration through cover is becoming one of the critical challenges for the mineral exploration industry. This paper explores methods of integrating geophysical, geological and landscape data so as to reduce uncertainty in landscape evolution models interpreted from inversion of electromagnetic (EM) data. Inversion of EM data is, in general, non-unique: many different models will be able to fit the EM data equally well, resulting in large uncertainty. However, EM model uncertainty can be significantly constrained when geological and landscape context are taken into account. This study aims to characterise the regolith (weathered and transported cover), and understand how its conductivity varies with the landscape and with the regolith architecture. This is assisted by logging information from 104 boreholes penetrating through the regolith and into the basement rocks. The study area is associated with the DeGrussa Cu-Au deposit located in the Capricorn Orogen of Western Australia, a regolith-dominated terrain where the regolith varies in thickness between <5 and ~150m. We first select EM decay curves, extracted from an airborne EM survey, whose locations are close to those of the boreholes. We then use a layered-earth (1D) forward model, and invert those data for the electrical resistivities of each of the lithologies identified on the geological borehole logs. Layer boundary depths are fixed to the borehole depths. We show how the non-uniqueness associated with EM inversion can be reduced by the inclusion of decay curves from geology with different layer thickness ratios. Lithological models derived from the integration of electromagnetic, geological and landscape data show less uncertainty and are therefore more reliable for mineral exploration targeting.

PreviousNext You have accessSymposium on the Application of Geophysics to Engineering and Environmental Problems 2011Practical 3D Inversion of AEM Data for Environmental Applications in Complex Regolith SettingsAuthors: Glenn WilsonTimothy MundayAndrew FitzpatrickLeif CoxMichael ZhdanovGlenn WilsonSearch for more papers by this author, Timothy MundaySearch for more papers by this author, Andrew FitzpatrickSearch for more papers by this author, Leif CoxSearch for more papers by this author, and Michael ZhdanovSearch for more papers by this authorhttps://doi.org/10.4133/1.3614252 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract This paper presents the application of an innovative approach to 3D inversion of airborne electromagnetic (AEM) data which renders the routine inversion of entire surveys realistic from the computational perspective. The method uses a moving footprint, and is based on the 3D integral equation method for computing data and sensitivities, and the re‐weighted regularized conjugate gradient method for minimizing the objective functional. Specifically, we examine results from several well constrained AEM surveys that target the geometry, distribution and water quality associated with palaeochannel aquifers which are developed in contrasting, variably complex regolith settings. The TEMPEST and SkyTEM surveys targeted anastomozing coarse sands and gravels beds in a sandy clay background. The surveys also targeted the abruptly changing and varying basement (due to changes in lithology and structure) which defines the base of the alluvial aquifer and/or the weathered regolith. Results from the 3D inversions are compared against drill hole geological and other geophysical data. The integrated interpretations are able to define the location and groundwater salinity within the palaeochannels. As a result of the surveys and drilling, local farm water and irrigation systems are being developed.Permalink: https://doi.org/10.4133/1.3614252FiguresReferencesRelatedDetailsCited By3D inversion of airborne electromagnetic dataGEOPHYSICS, Vol. 77, No. 4 Symposium on the Application of Geophysics to Engineering and Environmental Problems 2011ISSN (online):1554-8015Copyright: 2011 Pages: 642 publication data© 2011 Copyright © 2011 The Environmental and Engineering Geophysical SocietyPublisher:Environmental & Engineering Geophysical Society HistoryPublished: 06 Jul 2011 CITATION INFORMATION Glenn Wilson, Timothy Munday, Andrew Fitzpatrick, Leif Cox, and Michael Zhdanov, (2011), "Practical 3D Inversion of AEM Data for Environmental Applications in Complex Regolith Settings," Symposium on the Application of Geophysics to Engineering and Environmental Problems Proceedings : 101-101. https://doi.org/10.4133/1.3614252 Plain-Language Summary PDF DownloadLoading ...

Deterministic inversion of electromagnetic (EM) data yields a single best fitting resistivity model of the subsurface, which can be used to interpret the geological subsurface. Such a model fails to capture the uncertainty in both resistivity and geology. This limitation is critical, as multiple, geologically dissimilar subsurface configurations can yield equivalent EM responses, meaning a single model representation can be inaccurate or even misleading. Probabilistic inversion of the EM data provides a principled solution by characterizing the range of subsurface models consistent with data, and thereby explicitly quantifying uncertainty in both the geophysical and geological models.Here we invert by rejection sampling of pre-computed geophysical and geological 1D prior models. This allows for fast and efficient probabilistic inversion of large-scale EM surveys containing thousands of soundings. An added benefit of using pre-computed geological models is the possibility to encode geological expert knowledge into the models as direct information. In this context expert knowledge can be many things, for example the resistivity-lithology relationship, the chronological sequence of geological units, or the relative occurrence of various lithologies to name a few.In the presentation, we demonstrate how such a probabilistic inversion workflow can be set up and applied on towed transient EM data from geophysical surveys in varying geological settings. The required inputs are (i) a geophysical dataset consisting of EM soundings, and (ii) an expert-based assessment of the plausible geological subsurface architectures in the survey area. Optionally, geophysical and lithological well logging can be used to further constrain the inversion. We will highlight the tool/software (GeoPrior1D) we have developed to construct prior ensembles with encoded geological knowledge, especially suited for such a workflow. GeoPrior1D is an open-source tool for generating ensembles of one-dimensional geological and geophysical models that explicitly represent prior models for probabilistic inversion problems.Finally, we present key outcomes of the probabilistic modelling. This includes resistivity models with uncertainty, lithological models with uncertainty (entropy), class probabilities, and various themed maps. The produced models and maps, always accompanied by rigorously quantified uncertainties, enable better and more reliable decision-making across applications such as geohazard risk assessment, resource volume estimates, groundwater modelling, and much more.

&amp;lt;p&amp;gt;Electromagnetic conductivity imaging (EMCI) is a state-of-the-art methodology for soil salinity assessment over large areas. It involves the following rationale: (1) use of the electromagnetic induction (EMI) geophysical technique to measure the soil apparent electrical conductivity (EC&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt;, mS&amp;amp;#160;m&amp;lt;sup&amp;gt;&amp;amp;#8722;1&amp;lt;/sup&amp;gt;) over an area; (2) inversion of EC&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt; to obtain EMCI, which provides the spatial distribution of the soil electrical conductivity (&amp;amp;#963;, mS&amp;amp;#160;m&amp;lt;sup&amp;gt;&amp;amp;#8722;1&amp;lt;/sup&amp;gt;); (3) calibration process consisting of a regression between &amp;amp;#963; and the electrical conductivity of the saturated soil paste extract (EC&amp;lt;sub&amp;gt;e&amp;lt;/sub&amp;gt;, dS&amp;amp;#160;m&amp;lt;sup&amp;gt;&amp;amp;#8722;1&amp;lt;/sup&amp;gt;), used as a proxy for soil salinity; and (4) conversion of EMCI into salinity maps using the obtained calibration equation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we applied EMCI and a regional calibration in Lez&amp;amp;#237;ria Grande de Vila Franca de Xira, located in Portugal. The study area is an important agricultural system where soil faces the risk of salinization due to climate change, as the level and salinity of groundwater are likely to increase as a result of the rise of the sea water level and consequently of the estuary. These changes can also affect the salinity of the irrigation water which is collected upstream of the estuary.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;EMI surveys and soil sampling were carried out between May 2017 and October 2018 at four locations with different salinity levels across the study area. A regional calibration was developed and its ability to predict EC&amp;lt;sub&amp;gt;e&amp;lt;/sub&amp;gt; from EMCI was evaluated. The validation analysis showed that EC&amp;lt;sub&amp;gt;e&amp;lt;/sub&amp;gt; was predicted with a root mean square error of 3.14 dS&amp;amp;#160;m&amp;lt;sup&amp;gt;&amp;amp;#8722;1&amp;lt;/sup&amp;gt; in a range of 52.35 dS&amp;amp;#160;m&amp;lt;sup&amp;gt;&amp;amp;#8722;1&amp;lt;/sup&amp;gt;, slightly overestimated (&amp;amp;#8722;1.23 dS&amp;amp;#160;m&amp;lt;sup&amp;gt;&amp;amp;#8722;1&amp;lt;/sup&amp;gt;), with a strong Lin&amp;amp;#8217;s concordance correlation coefficient of 0.94 and high linearity between measured and predicted data (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; = 0.88). It was also observed that the prediction ability of the regional calibration is more influenced by spatial variability of data than temporal variability of data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Because of the transient nature of data, it was also possible to perform a preliminary qualitative analysis of soil salinity dynamics in the study area, revealing salinity fluctuations related to the input of salts and water either through irrigation, precipitation, or level and salinity of groundwater.&amp;lt;/p&amp;gt;

Joint inversions of coincident geophysical data are usually constrained to produce more reliable subsurface models. Structural, petrophysical, model parameter correlation, empirical, and transforms are some of the published constraints. The Gramian constraint provides a broad mathematical framework for implementing the aforementioned constraints. The Gramian constraint is formed from the determinant of the inner products of the model parameters involved. Previous works have used the Gramian constraint to invert multimodal parameters of different geophysical methods. But there has not been any extension of Gramian‐constrained joint inversion to mono‐model parameter from similar geophysical methods, for example, a similar conductivity or resistivity model from time‐ and frequency‐domain airborne electromagnetic methods. I implement the Gramian‐constrained joint inversion of time‐ and frequency‐domain airborne EM (AEM) data. This implementation allows the Gramian constraint to enhance the linear correlation of the model parameter between the two methods as the number of iterations increases. Improvement of the final joint inversion results over the standalone models is noticeable for both 3% noise‐contaminated synthetic and field data experiments. The field data jointly inverted are the high moment time‐domain SkyTEM data and frequency‐domain RESOLVE helicopter EM data acquired over the salinized Bookpurnong Irrigation District in South Australia in 2006 and 2008, respectively.

Probabilistic predictions using a groundwater model informed with airborne EM data

In August 2008, a marine Controlled Source ElectroMagnetic (CSEM) survey was acquired by WesternGeco EM for the previous operator (Encana) of Lady Franklin and Atammik blocks offshore west Greenland. The objective of the project was to rank possible hydrocarbon prospects in the upper Cretaceous section. Therefore, the survey was designed to optimize CSEM responses based on the previous operator's interpretation within a limited budget, resulting in a pseudo-3D survey, sub-optimal to assess actual interpretation updated according to the results of the three exploration wells drilled by Cairn in the block during 2011. Independent 3D inversions of the CSEM data run before the drilling campaign suggest a consistent background resistivity over the different prospects. Drilling results confirmed the resistivity distribution as recovered by 3D CSEM inversions within the data resolution limit. After the drilling campaign, a sensitivity study was carried out in order to assess the limit at which the geological problem can be tuned by the electromagnetic data response. This showed that the resistive anomalies visible on some of the prospects in the block cannot be assessed for hydrocarbon saturation on the basis of electromagnetic data alone as they are below the resolution of the method given the non-uniqueness of the results. The present paper highlights how non-seismic data, such as electromagnetic, can provide additional information which, integrated with other geophysical and geological data, can help exploration during the different phases. The pre-drilling analysis of the resistivity distribution as recovered by 3D inversion of the data identified resistive anomalies that were interpreted as hydrocarbon indicators. The drilling results identified the geological meaning of these anomalies, which were further analyzed to include and extrapolate the new post-drilling interpretation in order to evaluate the non-drilled prospects. The sensitivity study was critical to understand the EM data limitations in this geological context before investing in a new project. Introduction Electromagnetic data are widely applied in the oil industry in order to de-risk exploration, reducing the uncertainty related to reservoir saturation (Hesthammer et al., 2012). Many success stories increased the confidence in electromagnetic data as hydrocarbon detectors: in fact, electromagnetic data provide information exclusively regarding the resistivity distribution of the underlying earth. Resistive anomalies may be related to fluid saturation, but they may also be produced by different geological features not necessarily related to fluid saturation: volcanics, carbonates, and evaporites are only some examples of lithologies characterized by high resistivity values. In addition, inversion of electromagnetic data is a non-unique problem and therefore constraining the inversion with the available geological and geophysical information is critical in order to produce a geologically meaningful model and subsequent interpretations. The limited data and frequency range (~ 0.1 to 10 Hz), the diffusive physics of CSEM, and the necessity (usually) of using a smooth stabilizer for inversion result in low resolution.

For more than half a century airborne electromagnetic (AEM) methods have been used worldwide for cost-effective resistivity mapping of areas larger than several hundred km2. The technical developments and intensive use of these systems, principally in mining exploration during the first decades, led to the development of helicopter transient EM (HTEM) systems. Since the 2000s these systems provide the best lateral and vertical resolution for environmental exploration, and they still keep a good depth of investigation allowing the exploration from the first meters to depths of several hundred meters. This chapter focuses on helicopter borne transient electromagnetic (HTEM) systems, which are well suited for the detection of low resistive targets such as salt water intrusion in coastal zones.AEM methods are based on the diffusive induction phenomenon. It is a key tool for building realistic hydrogeological models; however it requires an understanding of its limits, and some insight into data processing modeling is necessary. They require careful processing, and removal of cultural EM noise, present in most survey areas, is mandatory in order to get high quality results. Accurate modeling of the data and of the system is also critical. The modeling is most often based on least-square optimization algorithms giving smooth or layered model descriptions of ground.In this chapter we describe the AEM method in detail and we discuss processing and inversion of data. To demonstrate the results from an investigation, we end the chapter with a case study of a SkyTEM survey made in the volcanic island of Mayotte where key geological structures and salt water intrusion were successfully mapped.KeywordsSeawater IntrusionSaline AquiferSecondary FieldGround ResistivityHigh Resistive LayerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

On proper orthogonal decomposition (POD) based reduced-order modeling of groundwater flow through heterogeneous porous media with point source singularity

Finding large volume of saline water to support oil field developments in an arid region of the Barmer Basin, Rajasthan, India, was a challenge. Prior to this study, very limited information was available about aquifers deeper below 150 m below ground level (bgl). The present paper describes the characterization of a deep aquifer system using oil field data acquired during hydrocarbon exploration and development. The objective of the study was aimed to identify saline water that has no practical use for domestic or irrigation purposes at economically viable cost without any adverse effect on freshwater system. Detailed regional hydrogeological study has been carried out using oil exploration data, i.e. seismic, geophysical logs, etc. followed by aquifer well drilling and testing to understand the hydrogeology of the southern part of the Barmer Basin. It has been found that extensive confined multiaquifer systems exist between 150 and 1000 mbgl. Aquifers were identified by using oil field well logs and cores of exploration and development wells. Four distinct groups of permeable granular zones separated by impermeable zones have been identified. The spatial extent of the aquifer has also been mapped with the help of seismic reflection data. Finally, geometry and thickness of the aquifer have been defined of each layer with reasonable accuracy. The salinity data of drilled wells indicate that groundwater salinity increases with depth. Brackish (2500 mg/L) groundwater occurs at the top and becomes highly saline (~17,500 mg/L) at the bottom. An extensive deep confined saline aquifer has been identified in Jagadia (Miocene) formation. Aquifer test has been conducted to understand the hydraulic characteristics and water quality of this aquifer. The results indicate that hydraulic conductivity is in range of 20–25 m/day in the Jagadia formation. Salinity of groundwater is ~17,500 mg/L. The aquifer is shallower in the north and gradually deepens southwards where aquifer top is ~1500 mbgl. The gross thickness of sand in the Jagadia formation varies from 25 m to a maximum of 600 m (south-east). The total gross calculated volume of the aquifer is ~2.6 × 109 m3. The aquifer contains unconsolidated to poorly consolidated, medium- to coarse-grained, moderate to well-sorted, subrounded sands. Quartzose with carbonaceous claystone and clay interbeds and minor lignite are also present. Grain size analyses indicate a mostly uniform formation with the majority of samples having a uniformity coefficient of 4 or less. The effective porosity has also been derived from petrophysical logs, and it is ~20% in the northeast and increases to ~30% in the southeastern boundary. This large static groundwater resource within the Jagadia formation below ~250 m depth has been identified as suitable aquifer for saline water abstraction to meet the long-term saline water requirement of oil fields in the southern area of the Barmer Basin. This aquifer is not in use for domestic and irrigation purposes due to salinity and its depth of occurrence. Saline water abstraction is not likely to deplete the limited dynamic shallow groundwater resources.

Electric and electromagnetic methods were conducted on the North Sea island of Langeoog to investigate its freshwater lens that is managed by the local water works to supply the island with drinking water. Helicopter-borne electromagnetic (HEM), as well as surface geophysical methods were applied, i.e., electrical resistivity tomography (ERT), transient electromagnetic (TEM), and magnetic resonance sounding (MRS). We found that, beside the depth of the fresh-saltwater interface, the combined interpretation of TEM and MRS allows a reliable identification of lithological layering, whereas ERT or TEM alone exhibit a high degree of ambiguity regarding subsurface lithology and groundwater salinity. Future research is focused on the joint inversion of the surface methods, as well as on the combined inversion of surface and HEM data. In this way, 3-D multi-parameter models will be generated as additional input for numerical modeling of groundwater dynamics inside the freshwater lens.

Data-driven models are necessary for addressing groundwater challenges. In many regions around the world scarcity of traditional hydrogeological data can limit the understanding of groundwater resources. For instance, absence of water level, salinity, and geological data can lead to uncertainty in groundwater models.&amp;#160;Such data scarcity problems are present in the coastal Sine Saloum Delta in Senegal where saltwater intrusion processes pose a significant threat to freshwater resources. In this work transient electromagnetic (TEM) surveys are conducted to obtain spatially dense information about the subsurface. TEM data provides resistivity profiles that help to identify key geological units, and delineate freshwater and saltwater zones. Additionally, nuclear magnetic resonance (NMR) data, which are sensitive to subsurface water content, can be used to aid in the parameterization of hydrogeological units. &amp;#160;A hybrid modeling framework was developed that includes establishing a geological model, assigning hydrogeological parameters, and simulating hydraulic heads and saltwater intrusion using MODFLOW. This framework demonstrates how geophysical methods can supplement limited traditional datasets and reduce model uncertainty. Such approaches have real-world transferability for similar data-scarce areas.

Land subsidence is a phenomenon that involves the lowering or settling of the earth's surface due to various factors. The land subsidence due to groundwater withdrawal over the world has been seen in many areas. A decrease in ground water level would cause an increase in effective stresses at clay layers which results consolidation of lower layers. Since about 1980, it has been proven that Kerman Province subsidence in Iran is due to extensive ground water withdrawal. Overdraft of groundwater, an increasing of about 6 times since 1969 to 1999, has caused a decline of about 28 m in groundwater level. The rate of subsidence recently is about 5-15 cm. for decline of about one meter in groundwater level. In Rafsanjan area, many problems such as increase in the salinity of groundwater, land subsidence and consequently earth fissures and cracks in buildings are caused by groundwater withdrawal.