Transboundary geophysical mapping of geological elements and salinity distribution critical for the assessment of future sea water intrusion in response to sea level rise

Jørgensen, Flemming; Scheer, W.; Thomsen, S.; Sonnenborg, Torben O.; Hinsby, Klaus; Wiederhold, H.; Schamper, Cyril; Burschil, Thomas; Roth, B.; Kirsch, R.; Auken, Esben

doi:10.5194/hessd-9-2629-2012

Cited by 27 publications

(45 citation statements)

References 50 publications

(41 reference statements)

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“…Buried valleys are complex structures filled with glaciolacustric clay, till, meltwater sand, and gravel and provide classical targets for hydrogeophysical surveys. The presented models mimic the geology studied in Jørgensen et al (2012), whereas the details of the generation of the synthetic 3D TEM data by using the TEMDDD code (Árnason 2008) are the same as discussed by . Both synthetic models are characterized by a 3D resistivity distribution; in fact, the top layer of the model represents a soil of randomly generated resistivity varying both along and perpendicularly to the profile.…”

Section: S Y N T H E T I C D a T Amentioning

confidence: 99%

“…The blue cross mark near the "A" survey portion is the location of the borehole used for comparison in Fig. 6. setting of the area, particularly by mapping buried valleys and salt-fresh water interfaces (Jørgensen et al 2012). The data set was acquired using the SkyTEM system (Sørensen and Auken 2004) operating at an average speed of 43 km/h for a resulting averaged sounding every 25-30 m. Processing of the data was done using the methodology described in (Auken et al 2009) and implemented in the Aarhus Workbench software package (www.aarhusgeophysics.com).…”

Section: F I E L D D a T Amentioning

confidence: 99%

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Sharp spatially constrained inversion with applications to transient electromagnetic data

Vignoli

Fiandaca

Christiansen

et al. 2014

Geophysical Prospecting

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Time-domain electromagnetic data are conveniently inverted by using smoothly varying 1D models with fixed vertical discretization. The vertical smoothness of the obtained models stems from the application of Occam-type regularization constraints, which are meant to address the ill-posedness of the problem. An important side effect of such regularization, however, is that horizontal layer boundaries can no longer be accurately reproduced as the model is required to be smooth. This issue can be overcome by inverting for fewer layers with variable thicknesses; nevertheless, to decide on a particular and constant number of layers for the parameterization of a large survey inversion can be equally problematic. Here, we present a focusing regularization technique to obtain the best of both methodologies. The new focusing approach allows for accurate reconstruction of resistivity distributions using a fixed vertical discretization while preserving the capability to reproduce horizontal boundaries. The formulation is flexible and can be coupled with traditional lateral/spatial smoothness constraints in order to resolve interfaces in stratified soils with no additional hypothesis about the number of layers. The method relies on minimizing the number of layers of non-vanishing resistivity gradient, instead of minimizing the norm of the model variation itself. This approach ensures that the results are consistent with the measured data while favouring, at the same time, the retrieval of horizontal abrupt changes. In addition, the focusing regularization can also be applied in the horizontal direction in order to promote the reconstruction of lateral boundaries such as faults. We present the theoretical framework of our regularization methodology and illustrate its capabilities by means of both synthetic and field data sets. We further demonstrate how the concept has been integrated in our existing spatially constrained inversion formalism and show its application to large-scale time-domain electromagnetic data inversions

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Section: S Y N T H E T I C D a T Amentioning

confidence: 99%

Section: F I E L D D a T Amentioning

confidence: 99%

Sharp spatially constrained inversion with applications to transient electromagnetic data

Vignoli

Fiandaca

Christiansen

et al. 2014

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…The survey covers an area of 730 km 2 with 3327 km of flight lines. Full survey details are provided by Jørgensen et al (2012). The dataset consists of multiple sub-surveys that were originally processed by different parties, thus containing small subjective differences in the processing patterns.…”

Section: F I E L D E X a M P L Ementioning

confidence: 99%

“…10, we compare mean resistivity maps at 30 m-40 m of depth for the area indicated in Fig. 4 based on data with no processing a), an ANN processing b), and a manual processing c) for both high-and low-moment data (for geological interpretations, we refer to Jørgensen et al (2012)). The main feature of the map is a large highresistivity region in the southern part and a low-resistivity region in the north.…”

Section: Figurementioning

confidence: 99%

Artificial neural networks for removal of couplings in airborne transient electromagnetic data

Andersen

Kirkegaard

Foged

et al. 2015

Geophysical Prospecting

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Modern airborne transient electromagnetic surveys typically produce datasets of thousands of line kilometres, requiring careful data processing in order to extract as much and as reliable information as possible. When surveys are flown in populated areas, data processing becomes particularly time consuming since the acquired data are contaminated by couplings to man‐made conductors (power lines, fences, pipes, etc.). Coupled soundings must be removed from the dataset prior to inversion, and this is a process that is difficult to automate. The signature of couplings can be both subtle and difficult to describe in mathematical terms, rendering removal of couplings mostly an expensive manual task for an experienced geophysicist. Here, we try to automate the process of removing couplings by means of an artificial neural network. We train an artificial neural network to recognize coupled soundings in manually processed reference data, and we use this network to identify couplings in other data. The approach provides a significant reduction in the time required for data processing since one can directly apply the network to the raw data. We describe the neural network put to use and present the inputs and normalizations required for maximizing its effectiveness. We further demonstrate and assess the training state and performance of the network before finally comparing inversions based on unprocessed data, manually processed data, and artificial neural network automatically processed data. The results show that a well‐trained network can produce high‐quality processing of airborne transient electromagnetic data, which is either ready for inversion or in need of minimal manual processing. We conclude that the use of artificial neural network scan significantly reduce the processing time and its costs by as much as 50%.

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“…The reflection seismic method using compressional waves (P waves) is an established method and often produces accurate results for near-surface tasks (e.g. Steeples and Miller, 1990;Rumpel et al, 2009;Jørgensen et al, 2012). In many cases, however, a higher spatial resolution is desired than P waves offer.…”

Section: Introductionmentioning

confidence: 99%

Finite-difference modelling to evaluate seismic P-wave and shear-wave field data

2015

Self Cite

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Abstract. High-resolution reflection seismic methods are an established non-destructive tool for engineering tasks. In the near surface, shear-wave reflection seismic measurements usually offer a higher spatial resolution in the same effective signal frequency spectrum than P-wave data, but data quality varies more strongly.To discuss the causes of these differences, we investigated a P-wave and a SH-wave seismic reflection profile measured at the same location on the island of Föhr, Germany and applied seismic reflection processing to the field data as well as finite-difference modelling of the seismic wave field. The simulations calculated were adapted to the acquisition field geometry, comprising 2 m receiver distance (1 m for SH wave) and 4 m shot distance along the 1.5 km long P-wave and 800 m long SH-wave profiles. A Ricker wavelet and the use of absorbing frames were first-order model parameters. The petrophysical parameters to populate the structural models down to 400 m depth were taken from borehole data, VSP (vertical seismic profile) measurements and cross-plot relations.The simulation of the P-wave wave-field was based on interpretation of the P-wave depth section that included a priori information from boreholes and airborne electromagnetics. Velocities for 14 layers in the model were derived from the analysis of five nearby VSPs (v P = 1600-2300 m s −1 ). Synthetic shot data were compared with the field data and seismic sections were created. Major features like direct wave and reflections are imaged. We reproduce the mayor reflectors in the depth section of the field data, e.g. a prominent till layer and several deep reflectors. The SH-wave model was adapted accordingly but only led to minor correlation with the field data and produced a higher signal-to-noise ratio. Therefore, we suggest to consider for future simulations additional features like intrinsic damping, thin layering, or a near-surface weathering layer. These may lead to a better understanding of key parameters determining the data quality of near-surface shear-wave seismic measurements.

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Transboundary geophysical mapping of geological elements and salinity distribution critical for the assessment of future sea water intrusion in response to sea level rise

Cited by 27 publications

References 50 publications

Sharp spatially constrained inversion with applications to transient electromagnetic data

Sharp spatially constrained inversion with applications to transient electromagnetic data

Artificial neural networks for removal of couplings in airborne transient electromagnetic data

Finite-difference modelling to evaluate seismic P-wave and shear-wave field data

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