Summary Geophysical investigations of geology beneath lakes, rivers, and shallow seawater can provide detailed information critical for hydrologic modelling, geologic studies, contaminant mapping, and more. However, significant technological and interpretational challenges have limited the applications in aquatic environments. We have developed and improved existing towed transient electromagnetic (tTEM) system to a new, easily configurable Floating, transient electromagnetic instrument (FloaTEM) capable of imaging the subsurface beneath both fresh and salt water. The FloaTEM system can be configured for the specific fresh or saltwater application as necessary. We have also carried out synthetic analysis which shows that depth of investigation of the FloaTEM system greatly depends on the resistivity and thickness of the water column. The system has been successfully deployed in Denmark for a variety of hydrologic investigations, improving the ability to understand and model subaqueous processes. We present two fresh water and one salt-water application of the FloaTEM system. Inversion results show the great heterogeneities in the sediment types below the freshwater lakes. Surveying on saline water shows that the system is capable to identify the clay and sand layers below the saline water column.
Abstract. Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We show the effectiveness of the presented database by surrogating the forward modelling process, and urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database. The dataset is publically available at https://doi.org/10.5281/zenodo.7260886 (Asif et al., 2022a).
In the last decades, Airborne Electromagnetic (AEM) surveys have been used more and more in relation to groundwater mapping campaigns worldwide. AEM surveys provide information that is highly valuable when understanding the 3-D architecture of the structures in the subsurface. The airborne surveys provide huge datasets and thus, a degree of detail that is very time-consuming and sometimes even impossible to interpret manually in three dimensions. Recently, semi-automatic modelling approaches have therefore been developed and investigated, just as AEM data has been incorporated into stochastic modelling with the objective to utilize data in a more time-efficient way. In this presentation, we will compare and evaluate the results of three different modelling approaches of AEM and borehole data in a study area in Denmark. This area consists of Quaternary clay tills and meltwater sands deposited on top of largely horizontal Pre-Quaternary sand and clay deposits. The modelling approaches are the
A major part of the drinking water in the Netherlands is derived from groundwater stored in coastal and inland aquifers. The water occurs through natural processes, but in some areas artificial infiltration of lake and river water in the coastal sand dunes is necessary to support the demand for clean drinking water in the densely populated country. In fact, artificial infiltration along the Dutch coast has been common practice for decades and is an essential part of the drinking water supply. The sustainability of this approach demands detailed knowledge of the fresh–saltwater balance to avoid salinization of the aquifers. The availability and quality of the groundwater are typically estimated using groundwater models, which can be used to forecast the behaviour of groundwater systems to external stresses, such as climate change. The reliability of groundwater models hinges on measurements such as hydraulic heads, stream flow rates, permeability of the subsurface and in coastal regions the boundary conditions for the coastal edge of the model. Here, a limiting factor is often the lack of offshore data since it is logistically difficult and costly to drill in the seabed. Airborne electromagnetics are increasingly being used to support groundwater management through high-resolution largescale mapping of aquifer properties. The method provides strong sensitivity to important hydrogeological units such as the fresh-saltwater interface and clay layers (which often constitute the base of the aquifers). Hence, AEM has been widely used to address issues such as mapping saltwater intrusion (Gunnink etal., 2012; Jørgensen et al., 2012) and aquifer delineation (Chandra et al., 2016; Schamper et al., 2013). One of the key advantages of the method is that it is airborne, allowing areas, which would otherwise be difficult to access, to be mapped in a cost-effective manner. As demonstrated in the present study, the fresh-saltwater interface and coastal boundary conditions of the groundwater model can be determined by combining onshore and offshore airborne electromagnetics.
Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database.
Chalk behaves with time dependent deformation when subjected to a load. We review a previously published data set from high pressure oedometer tests and apply a friction factor corresponding to the friction between solid and pore fluid. The friction factor is the Biot critical frequency used in acoustic theory to discriminate between the high and low frequency domains. By doing this we show how test results from dry, oil, and water saturated chalk can be combined in one simple expression.
Summary A SkyTEM airborne survey has been flown over 1820 km2 of an area of intense agriculture in Hawkes Bay region of New Zealand. The survey is designed to map the characteristics of a coastal fluvial aquifer and support the management of groundwater resources. Processing the data from a 590 km2 subset of the survey, adjacent three urban centres, has been helped by using ground-based geophysical data. These data include ground-TEM, DC resistivity soundings, and borehole geophysical logs. Seismic reflection data across the region provide constraints on the deeper sections of the aquifer system (200 – 500 m). One of the key aims of the study is to map the variability of the surface geological strata that in places are a recharge zone and in other places a confining layer for the aquifer. The SkyTEM data have a spatial coverage (170 m line spacing and 20–30 m station spacing) that enables these units to be mapped in more detail than is possible with the current boreholes and ground geophysical data. In places the ground-based data provide valuable support for constraining the shallow SkyTEM models where data can be missing and deep parts of the SkyTEM model where the resolution is low.
Summary Access to clean and reliable water sources is limited in many developing countries, with serious health, environmental, and economic consequences. Climate change exacerbates the issue, with more frequent and severe droughts and floods. Groundwater is a potential source of water, but unsustainable usage leads to depletion. Geophysical methods, such as transient electromagnetic (TEM) surveys, have been used to locate and map groundwater resources. However, traditional ground-based TEM instruments are complex and expensive, requiring trained operators and cumbersome data processing. We introduce a new ground-based TEM instrument called sTEM, which is designed to be easy-to-use for non-experts. This cost-effective and modern alternative can be particularly beneficial in the developing world for well-siting and mapping hydrogeology. The sTEM instrument also has applications in the developed world for infill in large regional scale surveys and mapping saline groundwater. The abstract provides an overview of the technical specifications of the sTEM instrument, with field examples and comparisons with other data types to be presented in the conference.
A033 INTEGRATED INVERSION OF CVES AND TEM DATA USING LATERAL CONSTRAINTS Summary 1 We present a mutually and laterally constrained inversion between transient electromagnetic (TEM) and geoelectric (DC) data sets. Although both methods measure in some sense the electrical resistivity or conductivity of the subsurface they sample different volumes and have different sensitivities. The different sampling volumes and different sensitivities are exploited by the mutually and laterally constrained inversion algorithm combining the coinciding profile data sets. The output model incorporates the information from both profile data sets to obtain the optimum layered 1D models fitting both data types. All data
