Contrast source inversion (CSI) for cross-hole radio imaging (RIM) data – Part 1: Theory and synthetic studies

Li, Yongxing; Smith, Richard S.

doi:10.1016/j.jappgeo.2018.12.002

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…It will also fail to effectively identify signals and will be unable to accurately reflect the underground geological structure. It seriously interferes with image segmentation, coding, feature extraction and target detection, which greatly increases the difficulty of mapping [4]. In the application of image clutter processing, reference [5] proposed to perform non-negative matrix factorization on the gray values of pixels in the GPR image to remove the first principal component for clutter suppression.…”

Section: Introductionmentioning

confidence: 99%

Power borehole image recognition technology based on convolution feature fine-grained optimization

Zhang

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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With the development of power system, the technology of power well is facing many problems. How to not only detect the power well information, but also characterize the measured data in an easy-to-explain way, so that the staff can understand and analyze the power well casing, is of great significance to the detection of pipeline damage and the development of the power industry. Nowadays, the development of informatioa n processing and microelectronics technology, especially the rapid development of image processing technology, provides a more theoretical basis for the realization of power well detection technology, and expands more possibilities for the study of detection data. A clutter suppression method based on fine-grained optimization decomposition of the convolution feature is proposed. A noise constraint is added into the non-negative matrix factorization, and a base matrix and a coefficient matrix to be factorized are assumed to be non-negative exponential distributions in probability. The non-negative matrix factorization is carried out by using a convolutional neural network to obtain the approximate inference of the base matrix and the coefficient matrix. The selection of parameter values and the input of horizontal gradients are determined. The low-rank matrix representation of clutter components is obtained so as to separate the clutter from the image. The experimental process is compared with the simulation data, which verifies that the model has good robustness for the image recognition in the power well.

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Section: Introductionmentioning

confidence: 99%

Power borehole image recognition technology based on convolution feature fine-grained optimization

Zhang

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Full-waveform inversion of cross-hole radio frequency electromagnetic data

Zheglova,

Farquharson,

Malcolm

2024

Geophysical Journal International

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Summary We consider application of full waveform inversion (FWI) to radio-frequency electromagnetic (EM) data. Radio-frequency imaging (RIM) is a cross-borehole technique to image electromagnetic subsurface properties from measurements of transmitted radio-frequency waves. It is used in coal seam imaging, ore exploration and various engineering and civil engineering applications. RIM operates at frequencies from 50 kHz to several tens of MHz. It differs from other geophysical EM methods, because the frequency band includes the transition between the wave propagation and diffusion regimes. RIM data are acquired in two-dimensional cross-hole sections in a reciprocal manner. Traditionally, radio-frequency data are inverted by straight ray tomography because it is inexpensive and easy to implement. It is argued that due to attenuation, the sensitivity of the transmitted electric field is the strongest within the first Fresnel zone of the ray connecting the transmitter and receiver. While straight ray tomography is a simple to implement and fast method, the non-linearity in the relationship between model parameters and data is often strong enough to warrant non-linear inversion techniques. FWI is an iterative high resolution technique, in which the physical properties are updated to minimize the misfit between the measured and modelled wavefields. Full waveform techniques have been used and extensively studied for the inversion of seismic data, and more recently, they have been applied to the inversion of GPR data. Non-linear inversion methods for RIM data are less advanced. Their use has been hindered by the high cost of full wave modelling and the high conductivity contrasts of many RIM targets, and, to some extent, by the limitations of the measuring instruments. We present the first application of this methodology to simultaneous conductivity and permittivity inversion of RIM data. We implement the inversion in the frequency domain in two dimensions using L-BFGS optimization. We analyze the sensitivity of the data to the model parameters and the parameter trade-off and validate the proposed methodology on a synthetic example with moderate conductivity variations and localized highly conductive targets. We then apply the FWI methodology to a field data set from Sudbury, Canada. For the field data set, we determine the most appropriate preprocessing steps that take into account specific peculiarities of RIM: the insufficient prior information about the subsurface and the limitations of the measuring equipment. We show that FWI is applicable under the conditions of RIM and is robust to imperfect prior knowledge: we obtain satisfactory model recoveries starting from homogeneous initial models in all of our examples. Just as other methods, FWI underestimates large conductivity contrasts due to the loss of sensitivity of the transmitted electric field to the conductivity variations as the conductivity increases above a certain level. The permittivity inside high conductors can not be recovered, however, recovering permittivity variations in the resistive zones helps obtain better focused conductivity images with fewer artifacts. Overall, FWI produces cleaner, less noisy and higher resolution reconstructions than the methods currently used in practice.

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Integrating Forward Modeling and Deep Learning for Precise Detection of Geological Anomalies in Coal Seams Using the Radio Imaging Method

Yang,

Wu,

Zhao

et al. 2024

IEEE Access

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Contrast source inversion (CSI) for cross-hole radio imaging (RIM) data – Part 1: Theory and synthetic studies

Cited by 7 publications

References 18 publications

Power borehole image recognition technology based on convolution feature fine-grained optimization

Power borehole image recognition technology based on convolution feature fine-grained optimization

Full-waveform inversion of cross-hole radio frequency electromagnetic data

Integrating Forward Modeling and Deep Learning for Precise Detection of Geological Anomalies in Coal Seams Using the Radio Imaging Method

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