Offset errors in probabilistic inversion of small-loop frequency-domain electromagnetic data: a synthetic study on their influence on magnetic susceptibility estimation

Small-loop frequency-domain electromagnetic (FDEM) devices measure a secondary magnetic field caused by the application of a stronger primary magnetic field. Both the in-phase and quadrature component of the secondary field commonly suffer from systematic measurement errors, which would result in a non-zero response in free space. The in-phase response is typically strongly correlated to subsurface magnetic susceptibility. Considering common applications on weakly to moderately susceptible grounds, the in-phase component of the secondary field is usually weaker than the quadrature component, making it relatively more prone to systematic errors. Incorporating coil-specific offset parameters in a probabilistic inversion framework, we show how systematic errors in FDEM measurements can be estimated jointly with electrical conductivity and magnetic susceptibility. Including FDEM measurements from more than one height, the offset estimate becomes closer to the true offset, allowing an improved inversion result for the subsurface magnetic susceptibility.Comment: Conference: Workshop on Gravity, Electrical and Magnetic Methods and Their Application

Mesonephric-like adenocarcinoma of the endometrium : diagnostic advances to spot this wolf in sheep's clothing : a review of the literature

Mesonephric-like adenocarcinoma is a recently described rare neoplasm occurring in the uterine corpus and ovary. This under-recognized subtype of carcinoma can be very challenging to diagnose. In mesonephric adenocarcinoma a variety of growth patterns can be present within the same tumor, as a result of which they can be misinterpreted and diagnosed as low-grade endometrioid adenocarcinoma, clear cell carcinoma, or even serous carcinoma and carcinosarcoma. We report a case of mesonephric-like adenocarcinoma misdiagnosed as a low-grade endometrioid endometrial adenocarcinoma that had an early local recurrence and metastasized to the liver and the lungs. Histopathological, immunohistochemical and molecular analysis were performed and compared to published literature, providing a comprehensive overview of the current knowledge. Databases (Pubmed, Web of Science, Google Scholar) were searched with a combination of the following search terms: mesonephric-like, mesonephric, adenocarcinoma, carcinoma, uterine body, uterine corpus, endometrium. Mesonephric-like adenocarcinoma is a difficult-to-diagnose entity. Advanced diagnostics, including improved morphologic, immunohistochemical and molecular knowledge can help develop new therapeutic strategies against this specific subtype of endometrial cancer with an aggressive clinical behavior

Geostatistical inversion of electromagnetic induction data for landfill modelling

The characterization and monitoring of landfills has become a major concern, not only for assessing the associated environmental impact (e.g., groundwater contamination) but also for evaluating the potential for recovery of secondary resources, in particular for the production of raw materials and energy. For both objectives, it is crucial to have knowledge of the waste composition and the current landfill conditions (e.g. water saturation level). Near-surface geophysical surveys have been proven effective for the non-invasive investigation of landfills, in which different methods have been used depending on the specific survey targets.  Because of its sensitivity to two subsurface physical properties, electrical conductivity (EC) and magnetic susceptibility (MS), frequency-domain electromagnetic (FDEM) induction has been successfully applied to the qualitative characterization of urban and industrial landfills, including mine tailings. Yet, due to the generally complex composition and strongly heterogeneous spatial distribution of waste deposits, reconstructing a reliable landfill model from surface geophysical measurements remains challenging. Geostatistical inversion emerges as powerful tool to improve the landfill modelling from geophysical data, allowing for a more detailed description of the spatial distribution of the properties of interest and the associated uncertainty. Additionally, it provides a flexible framework for integrating data from geophysical surveys and conventional sampling from drilling or trenching.</p><p>In this work, we present a new geostatistical inversion technique able for the simultaneous inversion of FDEM data for EC and MS, which optimize the landfill modelling procedure and is sensitive towards change on the physical properties of interest. This method is based on an iterative procedure where ensembles of subsurface models of EC and MS are generated with stochastic sequential simulation and co-simulation. These simulated models are conditioned locally by existing borehole data for these properties and by a spatial continuity pattern imposed by a variogram model. Synthetic instrument response data, including both the in-phase and quadrature-phase components of the FDEM response, are generated from each model using a forward model connecting the data domain (FDEM data) with the model domain (subsurface physical properties). The misfit between the observed and forward-modelled FDEM data, weighted according to the depth sensitivity of the FDEM response toward changes in EC and MS, is used to drive the generation of a new set of models in the next iteration. We illustrate the inversion procedure with synthetic landfill example data sets which were created based on real data collected at a mine tailing in Portugal and a municipal solid waste landfill in Belgium

GNSS interferometric reflectometry for station location suitability analysis

National geodetic reference systems can be continuously monitored using applications of Global Navigation Satellite Systems (GNSS). Within these reference systems, Continuously Operating GNSS Reference Stations (CORSs) are often employed to provide 24/7 satellite tracking data. Understanding the influence of the surroundings of a CORS on the recorded satellite tracking data is indispensable for quality analysis of both acquired data and station location suitability. One of the main sources of inaccurate tracking data is the result of the combined reception of direct as well as indirect, environment-reflected satellite signals by the CORS, in which the latter can be considered interference compromising the signal’s accuracy. The magnitude of this interference is usually evaluated by the Signal-to-Noise Ratio (SNR), a parameter stored by default in the RINEX interchange format for raw GNSS data. The technique of GNSS Interferometric Reflectometry (GNSS-IR) exploits the availability of the SNR data and has been frequently used for applications such as soil moisture monitoring, detection of vegetation water content, measuring snowfall or determining water levels. In this research, we propose to employ GNSS-IR to investigate the effect of the surrounding on a CORS in order to evaluate station location suitability. More specifically, this will be done by using the signal to estimate the Reflector Height (RH), which depends on the reflector roughness (i.e. the roughness of the surface surrounding the CORS). The quality of this estimation will be validated by comparing with the actual measurement of the RH of the CORS on site. In our approach, a statistically sound method is developed quantifying the stability of the RH determination. The proposed methodology consists of using Lomb-Scargle periodograms to select the dominant oscillation frequency of each satellite track SNR data, followed by an analysis and filtering of the peak amplitudes. This leads to the analysis product: number of significant peak amplitudes for an individual CORS over (sub-)daily timeframes. With historical data covering long time periods, statistical analysis of the (sub-)daily timeseries allows for reviewing the station location suitability. In Belgium, CORS are located on two typical positions: in Flanders, the 32 antennas are mainly installed on rooftops of buildings; in Wallonia, the 23 antennas are installed on a concrete pole next to highways. There is no evidence of one choice of station position being more suitable than the other. However, cars are known to be an important factor in signal reflections. In our analysis of station suitability, the effect of cars passing by on the highway near a Walloon CORS, but also movements on, e.g., parking lots next to buildings with a rooftop CORS, will be investigated. With the developed methodology, guidelines for station location selection could be further developed, together with a system to continuously monitor CORS position suitability using GNSS-IR, triggering a warning when significant changes in the environment changes the local reflectometry fingerprint

Efficient probabilistic joint inversion of direct current resistivity and small-loop electromagnetic data

Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This reduction can be quantified using Bayesian inversions. However, standard Markov chain Monte Carlo (MCMC) approaches are computationally expensive for most geophysical inverse problems. We present the Kalman ensemble generator (KEG) method as an efficient alternative to the standard MCMC inversion approaches. As proof of concept, we provide two synthetic studies of joint inversion of frequency domain electromagnetic (FDEM) and direct current (DC) resistivity data for a parameter model with vertical variation in electrical conductivity. For both studies, joint results show a considerable improvement for the joint framework over the separate inversions. This improvement consists of (1) an uncertainty reduction in the posterior probability density function and (2) an ensemble mean that is closer to the synthetic true electrical conductivities. Finally, we apply the KEG joint inversion to FDEM and DC resistivity field data. Joint field data inversions improve in the same way seen for the synthetic studies

Bayesian inversion of loop-loop electromagnetic data using an Ensemble Kalman method

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