TY - GEN A1 - Moghadas, Davood A1 - Taghizadeh-Mehrjardi, Ruhollah A1 - Triantafilis, John T1 - Probabilistic inversion of EM38 data for 3D soil mapping in central Iran T2 - Geoderma Regional N2 - Accurate determination of near surface soil electrical properties is important for agricultural and environmental management. In this respect, low frequency electromagnetic induction (EMI) has been widely used to measure soil apparent electrical conductivity. However, the potential to model soil subsurface layering has not been fully realized using EMI data. In this paper, we applied a probabilistic optimization approach, namely DREAM (ZS), on Geonics EM38 data to explore the robustness of this approach for soil subsurface conductivity mapping. The EM38 data was measured from the Ardakan region in the province of Yazd located in central Iran. Several soil sampleswere taken andwere further analyzed in a laboratory to derive soil textural and physical parameters. The probabilistic inversion was performed in a joint multi-configuration framework considering a five layered model. The estimated values are mainly in agreementwith the clay map (as the most influential factor); nevertheless, soil salinity data and inversely estimated conductivity values are poorly correlated for deeper layers due to the aridic condition and high clay content in the study area. DREAM (ZS) optimization approach appears to be promising for accurate retrieval of soil conductivity depth profile from EM38 data. Y1 - 2016 U6 - https://doi.org/10.1016/j.geodrs.2016.04.006 VL - 7 IS - 2 SP - 230 EP - 238 ER - TY - GEN A1 - Jadoon, Khan Zaib A1 - McCabe, Matthew F. A1 - Altaf, Muhammad Umer A1 - Hoteit, Ibrahim A1 - Muhammad, Nisar A1 - Moghadas, Davood A1 - Weihermüller, Lutz T1 - Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo T2 - Hydrology and Earth System Sciences N2 - A substantial interpretation of electromagnetic induction (EMI) measurements requires quantifying optimal model parameters and uncertainty of a nonlinear inverse problem. For this purpose, an adaptive Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to assess multi-orientation and multi-offset EMI measurements in an agriculture field with non-saline and saline soil. In MCMC the posterior distribution is computed using Bayes’ rule. The electromagnetic forward model based on the full solution of Maxwell’s equations was used to simulate the apparent electrical conductivity measured with the configurations of EMI instrument, the CMD Mini-Explorer. Uncertainty in the parameters for the three-layered earth model are investigated by using synthetic data. Our results show that in the scenario of non-saline soil, the parameters of layer thickness as compared to layers electrical conductivity are not very informative and are therefore difficult to resolve. Application of the proposed MCMC-based inversion to field measurements in a drip irrigation system demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil, and provides useful insight about parameter uncertainty for the assessment of the model outputs. Y1 - 2017 U6 - https://doi.org/10.5194/hess-21-5375-2017 SN - 1607-7938 SN - 1027-5606 VL - 21 IS - 10 SP - 5375 EP - 5383 ER - TY - GEN A1 - Moghadas, Davood A1 - Jadoon, Khan Zaib A1 - McCabe, Matthew F. T1 - Spatiotemporal monitoring of soil water content profiles in an irrigated field using probabilistic inversion of time-lapse EMI data T2 - Advances in Water Resources N2 - Monitoring spatiotemporal variations of soil water content (θ) is important across a range of research fields, including agricultural engineering, hydrology, meteorology and climatology. Low frequency electromagnetic induction (EMI) systems have proven to be useful tools in mapping soil apparent electrical conductivity (σa) and soil moisture. However, obtaining depth profile water content is an area that has not been fully explored using EMI. To examine this, we performed time-lapse EMI measurements using a CMD mini-Explorer sensor along a 10m transect of a maize field over a 6 day period. Reference data were measured at the end of the profile via an excavated pit using 5TE capacitance sensors. In order to derive a time-lapse, depth-specific subsurface image of electrical conductivity (σ), we applied a probabilistic sampling approach, DREAM(ZS), on the measured EMI data. The inversely estimated σ values were subsequently converted to θ using the Rhoades et al. (1976) petrophysical relationship. The uncertainties in measured σa, as well as inaccuracies in the inverted data, introduced some discrepancies between estimated σ and reference values in time and space. Moreover, the disparity between the measurement footprints of the 5TE and CMD Mini-Explorer sensors also led to differences. The obtained θ permitted an accurate monitoring of the spatiotemporal distribution and variation of soil water content due to root water uptake and evaporation. The proposed EMI measurement and modeling technique also allowed for detecting temporal root zone soil moisture variations. The time-lapse θ monitoring approach developed using DREAM(ZS) thus appears to be a useful technique to understand spatiotemporal patterns of soil water content and provide insights into linked soil moisture vegetation processes and the dynamics of soil moisture/infiltration processes. Y1 - 2017 U6 - https://doi.org/10.1016/j.advwatres.2017.10.019 SN - 1872-9657 SN - 0309-1708 VL - 110 SP - 238 EP - 248 ER - TY - CHAP A1 - Moghadas, Davood A1 - Vrugt, Jasper A. T1 - Non-invasive characterization of soil conductivity structure using probabilistic inversion and dimensionality reduction approach T2 - European Geosciences Union, General Assembly 2018, Vienna, Austria N2 - 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. Y1 - 2018 UR - https://meetingorganizer.copernicus.org/EGU2018/EGU2018-1445.pdf N1 - EGU2018-1445 PB - European Geophysical Society CY - Katlenburg-Lindau ER - TY - GEN A1 - Nasuti, Yasin A1 - Nasuti, Aziz A1 - Moghadas, Davood T1 - STDR: A Novel Approach for Enhancing and Edge Detection of Potential Field Data T2 - Pure and Applied Geophysics N2 - 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. Y1 - 2018 U6 - https://doi.org/10.1007/s00024-018-2016-5 SN - 0033-4553 SN - 1420-9136 IS - 24 ER - TY - GEN A1 - Moghadas, Davood A1 - Vrugt, Jasper A. T1 - The influence of geostatistical prior modeling on the solution of DCT-based Bayesian inversion: A case study from Chicken Creek catchment T2 - Remote Sensing N2 - Low frequency loop-loop electromagnetic induction (EMI) is a widely-used geophysical measurement method to rapidly measure in situ the apparent electrical conductivity (ECa) of variably-saturated soils. Here, we couple Bayesian inversion of a quasi-two-dimensional electromagnetic (EM) model with image compression via the discrete cosine transform (DCT) for subsurface electrical conductivity (EC) imaging. The subsurface EC distributions are obtained from multi-configuration EMI data measured with a CMD-Explorer sensor along two transects in the Chicken Creek catchment (Brandenburg, Germany). Dipole-dipole electrical resistivity tomography (ERT) data are used to benchmark the inferred EC fields of both transects. We are especially concerned with the impact of the DCT truncation method on the accuracy and reliability of the inversely-estimated EC images. We contrast the results of two different truncation approaches for model parametrization. The first scenario considers an arbitrary selection of the dominant DCT coefficients and their prior distributions (a commonly-used approach), while the second methodology benefits from geostatistical simulation of the EMI data pseudosection. This study demonstrates that DCT truncation based on geostatistical simulations facilitates a robust selection of the dominant DCT coefficients and their prior ranges, resulting in more accurate subsurface EC imaging from multi-configuration EMI data. Results based on geostatistical prior modeling present an excellent agreement between the EMI- and ERT-derived EC fields of the Chicken Creek catchment. Y1 - 2019 U6 - https://doi.org/10.3390/rs11131549 SN - 2072-4292 VL - 11 IS - 13 ER - TY - GEN A1 - Moghadas, Davood A1 - Jadoon, Khan Zaib A1 - McCabe, Matthew F. T1 - Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network T2 - Journal of Applied Geophysics N2 - Loop-loop electromagnetic induction (EMI) has proven to be efficient for fast and real-time soil apparent electrical conductivity (ECa) measurements. It is important to develop robust and accurate inversion strategies to obtain soil electromagnetic conductivity image (EMCI) from ECa data. Moreover, obtaining an accurate nonlinear relationship between subsurface electrical conductivity (σ) and water content (θ) plays a key role for soil moisture monitoring using EMI. Here, we incorporated probabilistic inversion of multi-configuration ECa data with dimensionality reduction technique through the discrete cosine transform (DCT) using training image (TI)-based parametrization to retrieve soil EMCI. The ECa data were measured repeatedly along a 10 m transect using a CMD mini-Explorer sensor. Time-lapse reference data were collected as well to benchmark the inversion results and to find the in-situ relationship between σ and θ. To convert the inversely estimated timelapse EMCI to the soil moisture,we examined two approaches, namely, Rhoades et al. (1976) model and artificial neural network (ANN). The proposed inversion strategy estimated the soil EMCI with an excellent agreement with the reference counterpart. Moreover, the ANN approach demonstrated superiorities than the commonly used petrophysical model of Rhoades et al. (1976) to obtain spatiotemporal images of θ from time-lapse EMCI. The results demonstrated that incorporation of the DCT-based probabilistic inversion of ECa data with the ANN approach offers a great promise for accurate characterization of the temporal wetting front and root zone soil moisture. Y1 - 2019 U6 - https://doi.org/10.1016/j.jappgeo.2019.07.004 SN - 0926-9851 VL - 169 SP - 226 EP - 238 ER - TY - GEN A1 - Moghadas, Davood A1 - Badorreck, Annika T1 - Characterization of soil electrical conductivity from Chicken Creek Catchment using deep learning inversion of geophysical data T2 - EGU General Assembly 2020, Online, 4–8 May 2020 Y1 - 2020 U6 - https://doi.org/10.5194/egusphere-egu2020-2664 ER - TY - CHAP A1 - Nasuti, Aziz A1 - Nasuti, Yasin A1 - Moghadas, Davood T1 - Enhancing Potential Field Data Using TDY Filter T2 - Near Surface Geoscience Conference & Exhibition, 9-12 September 2018, Porto, Portugal N2 - 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). Y1 - 2018 UR - https://www.researchgate.net/publication/327646691_ ER - TY - CHAP A1 - Moghadas, Davood T1 - High-Resolution Soil Electrical Conductivity Imaging from EMI D Based Probabilistic Inversion T2 - 24th European Meeting of Environmental and Engineering Geophysics, 9-13 September Porto, Portugal N2 - 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. Y1 - 2018 UR - http://www.earthdoc.org/publication/publicationdetails/?publication=94372 ER -