Forschungszentrum Landschaftsentwicklung und Bergbaulandschaften (FZLB)
Edge detection is one of the most important steps in the map interpretation of potential field data. In such a dataset, it is difficult to distinguish adjacent anomalous sources due to their field superposition. In particular, the presence of overlain shallow and
deep magnetic/gravity sources leads to strong and weak anomalies. In this paper, we present an improved filter, STDR, which utilises the ratio of the second-order vertical derivative to the second-order total horizontal derivative at the tilt angle equation. The maximum
and minimum values of this filter delineate the positive and negative anomalies, respectively. This novel filtering approach normalises the intensity of strong and weak anomalies, as well as anomalies with different depths and properties. Moreover, to better
illustrate the edges, its total horizontal derivative (THD_STDR) is also used. For positive and negative anomalies, the maximum value of the THD_STDR filter shows the edges of the anomalies. The potentiality of the proposed method is examined through both
synthetic and real case scenarios and the results are compared with a number of existing edge detector filters, namely TDR, THD_TDR, Theta and TDX. Due to substantial improvements in the filtering, STDR and its total horizontal derivative allow for
more accurate estimation of anomaly edges in comparison with the other filtering techniques. As a consequence, the interpretation of the potential field data is more feasible using the STDR filtering method.
Potential field methods produce anomaly maps with different magnitudes and depths that are typically contaminated by noise, making them hard to interpret. In order to highlight edges of the anomalies with different depths and magnitudes, data filtering techniques have received a great attention, in particular for mineral explorations. Filtering approaches render to explore more details from potential field data maps. In this respect, high pass filters are commonly used for enhancing the anomaly edges all of which utilize gradients of the potential field. In order to apply different filters on the potential field data, major attempts have been made to make a balance between noise and the signal obtained from a filtered image (Cooper & Cowan, 2006).
High-Resolution Soil Electrical Conductivity Imaging from EMI D Based Probabilistic Inversion
(2018)
Electromagnetic induction (EMI) sensors allow for non-invasive soil characterizations. Proximal soil sensing using EMI hindered due to the problems related to the inversion of apparent electrical conductivity (ECa) data. In this study, I used Bayesian inference to obtain the electrical conductivity layering of the subsurface from multi-configuration EMI data. In this respect, generalized formal likelihood function was used to more accurately describe the sensitivity of the posterior parameter distribution to residual assumptions. Discrete Cosine Transform (DCT) was employed as a model compression technique to reduce the number of unknown parameters in the inversion. I considered apparent electrical conductivity pseudosection as a training image (TI) in multiple-point statistical simulations. Information from TI realizations were utilized to determine dominant DCT coefficients, as well as prior probability density functions for the subsequent probabilistic inversions. The potentiality of the proposed approach was examined through an experimental scenario. The results demonstrated that this methodology allows for soil electrical conductivity imaging with high resolution. This strategy permits to incorporate summary metrics from ensemble of ECa pseudosection realizations in the inversion without resorting to any complimentary source of information. The proposed approach ensures accurate and high resolution characterization of soil conductivity layering from measured ECa values.
Low frequency loop-loop electromagnetic induction (EMI) is widely used for monitoring soil electrical conductivity and water content. As a non-invasive geophysical technique, EMI allows for rapid and real-time electrical conductivity measurements. However, EMI has not yet been used much to back out the vertical (depth profile) conductivity structure due to problems with the inversion of measured apparent electrical conductivity (ECa) data. In this study, we used Bayesian inference with the MT-DREAM(ZS) algorithm to infer the electrical conductivity layering of the subsurface from EMI data.We test and evaluate our methodology using apparent electrical conductivity data measured along two transects in the Hühnerwasser catchment in Lusatia, Germany. These measurements were made using CMD-Explorer, a multi-configuration sensor with three inter-coil spacings and two antenna orientations. Three offsets and two antenna modes lead to six measurement depths. Electrical Resistivity Tomography (ERT) measurements were also carried out to provide reference conductivity values and to calibrate the EMI data. Such calibration is necessary for quantitative interpretation of the ECa values and to enable multi-layered inversion. The Discrete Cosine Transform (DCT) was used to reduce the number of unknown parameters, and different likelihood functions were used to evaluate the sensitivity of the posterior parameter distribution to residual assumptions. DCT-based inversion equates to a quasi-two-dimensional framework which incorporates all data along the profile and results in a low-dimensional over-determined optimization problem. Results demonstrated that although appropriate selection of the low frequency DCT coefficients is important, the definition of the likelihood function plays a crucial role in the estimation of parameter and predictive uncertainty. The use of a Gaussian likelihood function introduces artifacts in DCT-based inversion of EMI data. The use of a more flexible likelihood function results in more accurate results of the DCT-inversion. Integration of the DCT with the MT-DREAM(ZS) algorithm and a flexible generalized likelihood function appears promising for the inversion of low frequency loop-loop EMI data. The proposed approach promises accurate and high resolution estimation of subsurface hydrogeophysical properties from EMI data.