Effects of electrical anisotropy on long-offset transient electromagnetic data

Liu, Yajun; Yogeshwar, Pritam; Hu, Xiangyun; Peng, Ronghua; Tezkan, B.; Mörbe, Wiebke; Li, Jianhui

doi:10.1093/gji/ggaa213

Cited by 13 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The time-domain FV method is used for modeling the 3D TEM response. The basic theoretical background of the modeling algorithm can be found in Liu [4]. For a given computational domain Ω and time interval [0, tf], the time-varying electric field E and magnetic flux B excited by an loop source are governed by the time dependent Maxwell's equations: (2) where E and B are the electric field and magnetic flux respectively, μ is the magnetic permeability with μ=μ0, and ε is the permittivity with ε=ε0, σ is the conductivity in S/m and 𝑱 𝑟 (𝑡) is the current density from the impressed source.…”

Section: D Tem Modeling Using Fv Methodsmentioning

confidence: 99%

3D Inversion of Transient Electromagnetic Data for Concealed Karst Cavern Detection

Li,

Liu,

Peng

et al. 2023

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

The karst cavern is common undesirable geological body in limestone areas, which can bring great safety hazards to the construction and operation of tunnels, bridges and highways. Transient electromagnetic method (TEM) can effectively detect hidden karst structures based on the difference in electrical property between the karst cavern and the surroundings. Traditionally, 1D inversion is commonly used for the interpretation of TEM data due to its high efficiency. However, this may lead to artifacts due to 2D or 3D effects in practical surveys. In this study, we present a Gaussian-Newton approach to 3D inversion of TEM data for karst cavern detection. The numerical test on synthetic TEM data demonstrates that 3D inversion can effectively delineate the location and geometry of concealed karst cavern compared to the 1D inversion results.

show abstract

Section: D Tem Modeling Using Fv Methodsmentioning

confidence: 99%

3D Inversion of Transient Electromagnetic Data for Concealed Karst Cavern Detection

Li,

Liu,

Peng

et al. 2023

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

show abstract

“…e methods used to conduct prediction mainly include the empirical formula method, numerical simulation method, borehole observation method, and electric detection method. e accuracy of the empirical formula method and numerical simulation method is lower than that of the field measurement results, while the borehole observation and electrical method are mainly in the form of point observations, and the observation results have certain limitations [17].…”

Section: Analysis Of the Load Transfer Coefficient Of The Loose Layermentioning

confidence: 99%

Roof Failure Mechanism of Thin Bedrock Working Faces under Loading and Analysis Based on Microseismic Monitoring Technology

et al. 2022

View full text Add to dashboard Cite

The resistance transfer coefficient of supports plays an important role in support selection in coal mines, which is the main factor in support crushing accidents. Based on the key layer theory, the formula for calculating the resistance transfer coefficient of supports under the load of a loose layer was deduced. The analysis of four working faces with thick loose layers and the corresponding mining pressure data were used to deduce the load transfer coefficients of the thick loose layers and ultimately illustrate the relationship between thick loose layers and different influencing factors. By using microseismic technology to monitor the process of roof failure in a thin bedrock working face with a thick loose layer during mining, the roof failure characteristics of a large-mining-height working face under the load of a thick loose layer were further verified. The results show that a thicker loose layer and thinner bedrock caused more of the load to transfer to the working face, the roof microseismic events were mainly concentrated in the range of 60 m to 75 m above the coal seam, and the most active events occurred during the square stage (the length of the working face’s goaf is equal to its width). The height of the water-conducting fracture zone was analyzed by microseismic data and then verified with theoretical calculations.

show abstract

“…Due to the strong 3-D, IP, and possible anisotropic effects (cf. Liu et al, 2020) in the survey area, the error floor of LOTEM field data had to be increased to 5% (Mörbe, 2020). Since the errors of semi-AEM field data are usually larger than 5%…”

Section: -D Joint Inversion Of Field Datamentioning

confidence: 99%

“…Based on extracting the different resolution characteristics of semi-AEM and LOTEM data, we intend to optimize mineral exploration surveys via the joint inversion of these two EM methods. In a large-scale mineral exploration survey conducted in the DESMEX frame in eastern Thuringia, Germany (Liu et al, 2020;Mörbe et al, 2020;Steuer et al, 2020;Smirnova et al, 2019), one 8.5 km long LOTEM profile was measured along one of the semi-AEM flight lines. Many other geophysical experiments were also conducted during the DESMEX survey.…”

mentioning

confidence: 99%

2-D joint inversion of semi-airborne CSEM and LOTEM data in eastern Thuringia, Germany

Cai

Yogeshwar

Mörbe

et al. 2022

Geophysical Journal International

Self Cite

View full text Add to dashboard Cite

Summary Various electromagnetic (EM) techniques have been developed for exploring natural resources. The novel frequency-domain semi-airborne controlled source electromagnetic (semi-AEM) method takes advantages of both ground and airborne techniques. It combines ground-based high-power electrical dipole sources with large scale and spatially densely covered magnetic fields measured via airborne receivers. The method can survey the subsurface down to approximately 1000 m and is particularly sensitive towards conductive bodies (e.g. mineralized bodies) in a more resistive host environment. However, the signal-to-noise ratio of semi-AEM is lower than that of ground-based methods such as long-offset transient electromagnetics (LOTEM), mainly due to the limited stacking time and motion induced noise. As a result, the semi-AEM often has reduced depth of investigation in comparison to LOTEM. One solution to overcome these flaws is to analyse and interpret semi-AEM data together with information from other EM methods using a joint inversion. Since our study shows that LOTEM and semi-AEM data have complementary subsurface resolution capabilities, we present a 2-D joint inversion algorithm to simultaneously interpret frequency-domain semi-AEM data and transient electric fields using extended dipole sources. The algorithm has been applied to the field data acquired in a former mining area in eastern Thuringia, Germany. The 2-D joint inversion combines the complementary information and provides a meaningful 2-D resistivity model. Nevertheless, obvious discrepancies appear between the individual and joint inversion results. Consequent synthetic modelling studies illustrate that the discrepancies occur because of i) differences in lateral and depth resolution between the semi-AEM and LOTEM data caused by different measuring configurations, ii) different measured EM components, and iii) differences in the error weighting of the individual datasets. Additionally, our synthetic study suggests that more flexible land-based configurations with sparse receiver locations are possible in combination with semi-AEM without a significant loss of target resolution, which is promising for accelerating data acquisition and for survey planning and logistics, particularly when measuring in inaccessible areas.

show abstract

Effects of electrical anisotropy on long-offset transient electromagnetic data

Cited by 13 publications

References 36 publications

3D Inversion of Transient Electromagnetic Data for Concealed Karst Cavern Detection

3D Inversion of Transient Electromagnetic Data for Concealed Karst Cavern Detection

Roof Failure Mechanism of Thin Bedrock Working Faces under Loading and Analysis Based on Microseismic Monitoring Technology

2-D joint inversion of semi-airborne CSEM and LOTEM data in eastern Thuringia, Germany

Contact Info

Product

Resources

About