Comparison of novel semi-airborne electromagnetic data with multi-scale geophysical, petrophysical and geological data from Schleiz, Germany

Steuer, A.; Smirnova, Maria; Becken, Michael; Schiffler, Markus; Günther, Thomas; Rochlitz, Raphael; Yogeshwar, Pritam; Mörbe, Wiebke; Siemon, B.; Costabel, Stephan; Preugschat, Benedikt; Seht, M. Ibs-von; Zampa, Luigi; Müller, Franz

doi:10.1016/j.jappgeo.2020.104172

Cited by 33 publications

(16 citation statements)

References 42 publications

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“…Recent advances in the method have extended penetration all the way to 1000 m (e.g. Steuer et al 2020).…”

Section: The Perception Of Declining Exploration Successesmentioning

confidence: 99%

What we have learned from the past and how we should look forward

Wellmer

2022

Miner Econ

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From the vantage point of more than 50 years' work in the raw material field, as well as working in the private sector, in the German federal ministry of economics, at a geological survey, and engaged in teaching and supervising research at a university, I draw a number of conclusions about the following aspects of the fields: development of long-term prices, the long-term supply situation, especially the expectation of an imminent peaking of supply, the frequent and mistaken prediction of shortfalls in supply, our understanding of reserves and resources, and the cyclic nature of success in exploration. I am solely dealing with geological aspects, not taking into account political inferences and supply disruptions. This is followed by an attempt to look into the future of raw materials demand within the framework of the accelerating green energy transition. These conclusions are:

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“…Recent advances in the method have extended penetration all the way to 1000 m (e.g. Steuer et al 2020).…”

Section: The Perception Of Declining Exploration Successesmentioning

confidence: 99%

What we have learned from the past and how we should look forward

Wellmer

2022

Miner Econ

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“…They are highlighted by black dashed lines and marked as C1 -C4. The shallow conductive bodies C1, C2 and C3 were also revealed by helicopter EM (HEM) measurements (Steuer et al, 2020). However, the depth extension of C2 could not be validated by HEM due…”

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.…”

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confidence: 99%

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

Cai

Yogeshwar

Mörbe

et al. 2022

Geophysical Journal International

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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.

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“…To fully understand the multidimensional variability of bedrock properties throughout a mountainous watershed, new approaches are required that can assess the spatial variability of hydraulic parameters ( 26 , 27 ) and ideally can take advantage of increasingly available, spatially distributed remote sensing datasets.…”

Section: Introductionmentioning

confidence: 99%

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

et al. 2022

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Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

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Comparison of novel semi-airborne electromagnetic data with multi-scale geophysical, petrophysical and geological data from Schleiz, Germany

Cited by 33 publications

References 42 publications

What we have learned from the past and how we should look forward

What we have learned from the past and how we should look forward

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

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

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