A reappraisal of legacy reflection seismic data from the western margin of the Kaapvaal craton, South Africa, with implications for Mesozoic-Cenozoic regional tectonics

Westgate, M.; Manzi, M.; Malehmir, Alireza; Gibson, Roger L.; Andreoli, M. A. G.; Bumby, Adam John

doi:10.1016/j.tecto.2021.228934

Cited by 6 publications

(1 citation statement)

References 60 publications

(125 reference statements)

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“…However, in hardrock or crystalline-rock geological settings, reflectivity and diffractivity patterns are not correlated as those of in the sedimentary settings because of strong heterogeneity, short reflectivity and poor signal-tonoise ratio (e.g. Adam et al, 1997;Bellefleur et al, 2012;Khoshnavaz et al, 2016;Donoso et al, 2021;Malehmir & Bellefleur., 2009, Manzi et al, 2012Markovic et al, 2020;Westgate et al, 2021). Therefore, it can be challenging to employ the autoencoder, for example, for the wavefield decomposition and noise separation.…”

Section: Introductionmentioning

confidence: 99%

Diffraction denoising using self‐supervised learning

Markovic

Malehmir

2023

Geophysical Prospecting

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Diffraction wavefield contains valuable information on subsurface composition through velocity extraction and sometimes anisotropy estimation. It can also be used for the delineation of geological features, such as faults, fractures and mineral deposits. Diffraction recognition is, therefore, crucial for improved interpretation of seismic data. To date, many workflows for diffraction denoising, including deep‐learning applications, have been provided, however, with a major focus on sedimentary settings or for ground‐penetrating radar data. In this study, we have developed a workflow for a self‐supervised learning technique, an autoencoder, for diffraction denoising on synthetic seismic, ground‐penetrating radar and hardrock seismic datasets. The autoencoder provides promising results especially for the ground‐penetrating radar data. Depending on the target of the studies, diffraction signals can be tackled using the autoencoder both as the signal and/or noise when, for example, a reflection is a target. The real hardrock seismic data required additional pre‐ and post‐autoencoder image processing steps to improve automatic delineation of the diffraction. Here, we also coupled the autoencoder with Hough transform and pixel edge detection filters. Along inlines and crosslines, diffraction signals have sometimes a similar character as the reflection and may spatially be correlated making the denoising workflow unsuccessful. Coupled with additional image processing steps, we successfully isolated diffraction that is generated from a known volcanogenic massive sulphide deposit. These encouraging results suggest that the self‐supervised learning techniques such as the autoencoder can be used also for seismic mineral exploration purposes and are worthy to be implemented as additional tools for data processing and target detections.

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Section: Introductionmentioning

confidence: 99%

Diffraction denoising using self‐supervised learning

Markovic

Malehmir

2023

Geophysical Prospecting

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Granitoid gneisses of the Morokweng impact structure: Implications for Neoarchaean evolution of the western Kaapvaal craton

Gibson

Wela²,

Bybee³

et al. 2022

Lithos

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Shock petrographic and numerical modeling constraints on the morphology and size of the Morokweng impact structure, South Africa

Gibson,

Wela,

Rae

et al. 2024

Meteorit & Planetary Scien

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The 369 m deep M4 drill hole, located ~18 km NNW of the center of the 146 Ma Morokweng impact structure (MIS), intersects shocked Archean granitoid gneisses and subsidiary dolerite intrusions that are cut by faults, cataclasites and mm‐ to m‐wide suevitic and pseudotachylitic breccia dikes. The shock features in quartz in the gneisses and breccia dikes include decorated planar deformation features (PDFs), planar fractures, feather features, and toasting. Other minerals show features that may be shock‐related, such as multiple sets of planar features and alternate twin ladder structures in feldspars, kink bands in biotite, and planar features in titanite, apatite, and zircon; however, these are variably annealed and/or overprinted by hydrothermal alteration effects, and confirmation of their origin awaits further study. Universal Stage measurements of PDF sets in quartz from 12 gneissic target rocks and from lithic and mineral clasts in three suevitic and three pseudotachylitic breccia dikes reveal four dominant sets: (0001), {}, {} and {}. Based on these observations, the average peak shock pressure in these rocks is estimated at ≤16 GPa, which supports the original proximity (within one transient cavity radius) of these rocks to the point of impact. No discernible depth‐dependent shock attenuation was noted in the core. These shock levels and the elevated structural position of the rocks in the M4 core relative to the impact melt sheet intersected in drill holes closer to the center of the MIS suggest that the M4 lithologies represent part of the parautochthonous peak ring volume that subsequently experienced 1.5–2 km of post‐impact erosion before it was buried beneath younger sediments. Numerical modeling using the iSALE‐2D code suggests that the original Morokweng crater had a rim‐to‐rim diameter of between 70 and 80 km, and that the rocks in the M4 core were originally located at a depth of 7–8 km and a radial distance of 8–9 km from the point of impact.

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A reappraisal of legacy reflection seismic data from the western margin of the Kaapvaal craton, South Africa, with implications for Mesozoic-Cenozoic regional tectonics

Cited by 6 publications

References 60 publications

Diffraction denoising using self‐supervised learning

Diffraction denoising using self‐supervised learning

Granitoid gneisses of the Morokweng impact structure: Implications for Neoarchaean evolution of the western Kaapvaal craton

Shock petrographic and numerical modeling constraints on the morphology and size of the Morokweng impact structure, South Africa

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