Interpretation of airborne electromagnetic data using the modified image method

Geophysics ◽  
1989 ◽  
Vol 54 (8) ◽  
pp. 1023-1030 ◽  
Author(s):  
Clyde J. Bergeron ◽  
Juliette W. Ioup ◽  
Gus A. Michel
Keyword(s):  
Synthetic Data ◽  
Low Frequency ◽  
Ground Truth ◽  
Skin Depth ◽  
Cape Cod ◽  
Image Method ◽  
Radar Altimeter ◽  
Cape Cod Bay ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Data

The modified image method is used to invert active electromagnetic (AEM) data from a 1984 U. S. Navy survey of Cape Cod Bay. The high‐frequency data (7200 Hz) give a robust value for the altitude of the helicopter‐towed AEM bird and for the first‐layer skin depth and, hence, for the first‐layer conductivity. The inversion of low‐frequency (385 Hz) bottom‐probing signals produces more noise‐sensitive estimates for the water depth and for the conductivity contrast K, the ratio of the bottom to water conductivities. The results show good agreement with “ground‐truth” radar altimeter, sea conductivity, and sonar depth data. To demonstrate the accuracy of the modified image method of inversion, we incorporate ground‐truth measurements along a flight line and the experimental frequencies in a forward Sommerfeld calculation to generate synthetic data which then are inverted using this technique.

A holistic approach to inversion of frequency-domain airborne EM data

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. G301-G312 ◽  
Author(s):  
Ross Brodie ◽  
Malcolm Sambridge
Keyword(s):  
Frequency Domain ◽  
Synthetic Data ◽  
Holistic Approach ◽  
Data Sets ◽  
Data Set ◽  
Zero Level ◽  
Airborne Electromagnetic ◽  
Available Information ◽  
Airborne Electromagnetic Data ◽  
Parameter Values

We have developed a holistic method for simultaneously calibrating, processing, and inverting frequency-domain airborne electromagnetic data. A spline-based, 3D, layered conductivity model covering the complete survey area was recovered through inversion of the entire raw airborne data set and available independent conductivity and interface-depth data. The holistic inversion formulation includes a mathematical model to account for systematic calibration errors such as incorrect gain and zero-level drift. By taking these elements into account in the inversion, the need to preprocess the airborne data prior to inversion is eliminated. Conventional processing schemes involve the sequential application of a number of calibration corrections, with data from each frequency treated separately. This is followed by inversion of each multifrequency sample in isolation from other samples.By simultaneously considering all of the available information in a holistic inversion, we are able to exploit interfrequency and spatial-coherency characteristics of the data. The formulation ensures that the conductivity and calibration models are optimal with respect to the airborne data and prior information. Introduction of interfrequency inconsistency and multistage error propagation stemming from the sequential nature of conventional processing schemes is also avoided. We confirm that accurate conductivity and calibration parameter values are recovered from holistic inversion of synthetic data sets. We demonstrate that the results from holistic inversion of raw survey data are superior to the output of conventional 1D inversion of final processed data. In addition to the technical benefits, we expect that holistic inversion will reduce costs by avoiding the expensive calibration-processing-recalibration paradigm. Furthermore, savings may also be made because specific high-altitude zero-level observations, needed for conventional processing, may not be required.

A new vertical continuation procedure for airborne electromagnetic field data from the modified image method

Geophysics ◽  
1999 ◽  
Vol 64 (5) ◽  
pp. 1364-1368 ◽  
Author(s):  
Clyde J. Bergeron ◽  
John R. Brusstar ◽  
Ningke Yi ◽  
Yan Wu ◽  
Juliette W. Ioup
Keyword(s):  
Accurate Method ◽  
Skin Depth ◽  
Flight Path ◽  
Integral Representations ◽  
Algebraic Expression ◽  
Image Method ◽  
Sommerfeld Integral ◽  
Horizontal Variation ◽  
Conductivity Anomalies ◽  
Airborne Electromagnetic

Airborne electromagnetic (AEM) data measured by equipment in a bird tethered to a helicopter have large variations caused by the unavoidable vertical excursions of the helicopter as it traverses its flight path. Such large changes tend to mask the smaller changes in field strength caused by lateral variations in the earth’s electrical conductivity along the flight path, which is the information that is the goal of AEM surveys. Signals produced by conductivity anomalies such as sea‐ice keels and pipelines in marshes or in the shallow ocean are enhanced and may be apparent directly in the continued fields. Furthermore, electronic or environmental noise is more easily detected in the continued fields and reduced by various methods of filtering and signal processing. In the modified image method (MIM) formalism for AEM fields, the algebraic expression for the secondary to primary field ratio [Formula: see text] is given in terms of R, where R is the total complex vertical separation of the primary and image dipoles [Formula: see text] scaled to the coil spacing ρ, [Formula: see text] is the complex effective skin depth, and h is the altitude of the bird. An inverse algebraic relation gives R as a function of [Formula: see text]. In this paper we present a simple and accurate method of continuing the field by way of continuing R. Because R is linear in h, the vertical continuation of R from h to h0 is accomplished by a simple linear translation. This method is applied to a flight line of an AEM survey of Barataria Bay, Louisiana, which includes both the marsh and a near‐shore region of the Gulf of Mexico. The smoothnesss of the continued data over the Gulf implies that the variability of the continued data over the marsh is attributable to horizontal variation in salinity, soil porosity, and water depth rather than noise. To produce more accurate values for R, we have also included details of an extended half‐space renormalization function which, in effect, removes residual differences between the fields calculated from the MIM algebraic and the numerical evaluation of the exact Sommerfeld integral representations of the [Formula: see text] field.

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

scholarly journals Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

2021 ◽  
Vol 13 (7) ◽  
pp. 1238
Author(s):  
Jere Kaivosoja ◽  
Juho Hautsalo ◽  
Jaakko Heikkinen ◽  
Lea Hiltunen ◽  
Pentti Ruuttunen ◽  
...  
Keyword(s):  
Reference Data ◽  
Measurement Techniques ◽  
Synthetic Data ◽  
Ground Truth ◽  
Precision Farming ◽  
Aerial Imaging ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

Application of complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V79-V86 ◽  
Author(s):  
Hakan Karsli ◽  
Derman Dondurur ◽  
Günay Çifçi
Keyword(s):  
Seismic Data ◽  
Trace Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Random Noise ◽  
Synthetic Data ◽  
Low Frequency ◽  
Bright Spot ◽  
Phase Information ◽  
Resolution Improvement

Time-dependent amplitude and phase information of stacked seismic data are processed independently using complex trace analysis in order to facilitate interpretation by improving resolution and decreasing random noise. We represent seismic traces using their envelopes and instantaneous phases obtained by the Hilbert transform. The proposed method reduces the amplitudes of the low-frequency components of the envelope, while preserving the phase information. Several tests are performed in order to investigate the behavior of the present method for resolution improvement and noise suppression. Applications on both 1D and 2D synthetic data show that the method is capable of reducing the amplitudes and temporal widths of the side lobes of the input wavelets, and hence, the spectral bandwidth of the input seismic data is enhanced, resulting in an improvement in the signal-to-noise ratio. The bright-spot anomalies observed on the stacked sections become clearer because the output seismic traces have a simplified appearance allowing an easier data interpretation. We recommend applying this simple signal processing for signal enhancement prior to interpretation, especially for single channel and low-fold seismic data.

Growth and Movement of Tagged Lobsters Released in Western Cape Cod Bay, 1970–1977

1984 ◽  
pp. 119-129
Author(s):  
Robert P. Lawton ◽  
Phillips Brady ◽  
Christine Sheehan ◽  
Wendell Sides ◽  
Elizabeth Kouloheras ◽  
...  
Keyword(s):  
Cape Cod ◽  
Western Cape ◽  
Cape Cod Bay
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