The monogenic signal of potential-field data: A Python implementation

Hidalgo-Gato, Marlon C.; Barbosa, Valéria C. F.

doi:10.1190/geo2016-0099.1

Cited by 10 publications

(2 citation statements)

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“…Similarly, the local phase can be converted into the local phase image (Figure b). The local phase image removes the influence of intensity variations with penetration depth and enhances all of the reflections (Hidalgo‐Gato & Barbosa, ), and thus it has the same characteristics as the cosine phase image. The characteristics of the local phase imply that the cosine phase horizons can be replaced with the local phase horizons when the cosine phase data is lacking.…”

Section: Characteristics Of Sbp Imagesmentioning

confidence: 99%

“…The local phase of a monogenic signal in this situation can be obtained and used for forming the local phase image to highlight the reflections, as shown in Figure b. The local phase in monogenic signal analysis is usually used for detecting linear structures and is fit for horizon picking (Felsberg & Sommer, ; Hidalgo‐Gato & Barbosa, ). Felsberg and Sommer () introduced the following filters in frequency domain:

H_{1} ()(, u_{1}, u_{2}) = i \frac{u_{1}}{\sqrt{{u_{1}}^{2} + {u_{2}}^{2}}} and H_{2} ()(, u_{1}, u_{2}) = j \frac{u_{2}}{\sqrt{{u_{1}}^{2} + {u_{2}}^{2}}}

…”

Section: Characteristics Of Sbp Imagesmentioning

confidence: 99%

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A Comprehensive Horizon‐Picking Method on Subbottom Profiles by Combining Envelope, Phase Attributes, and Texture Analysis

Zhao

et al. 2020

Earth and Space Science

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We propose a new method that considers the envelope, phase attributes, and texture analysis of the subbottom profile to automatically obtain continuous and accurate horizon picking. This method overcomes the shortcomings of the traditional methods in envelope and phase automatic horizon picking in terms of accuracy and continuity, and in efficiency when it comes to manual picking. Under the constraint of the envelope threshold from the envelope image and the texture segmentation from the phase image, the accurate envelope horizons are picked from the envelope image. Through mean filtering and template enhancement, the fine phase horizons are picked from the phase image. Finally, by combining the two kinds of picked horizons, more continuous, accurate, and finer horizons are obtained. The proposed method was tested on a real data set.

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Section: Characteristics Of Sbp Imagesmentioning

confidence: 99%