V good listen

That's nice method to improve imaging for structural interpretation. I've just read about reconvolution improving frequency which can be a great method for better resolution. What do you think about reconvolution?

Vielen Dank

Thanks for the citations! I am Happy to contribute !

thanks so much from Germany

Thanks for uploading this video. cheers

Thanks Heather. I enjoy your clips Very informative and to the point

Amazing!

Very interesting mam

Excellent, thanks for sharing Heather. Reminded the course from Marfurt I took few years ago.

looks like a normal fault to me.

cool, thanks for sharing

Very helpful thank you. I tried it on my data and loved it

Fantastic and very concise / cogent presentation on this very important topic. It is certainly a great teaser for me to dive deep into more intricacies of the practical and analytical aspects of these quick and dirty analytical tools. Thanks so much for sharing this presentation and putting it together!

Great Job ! nice overall work

This video is about a master thesis presentation on assessing spectral attributes in identifying gas hydrates in seismic data from Pegasus Basin Offshore New Zealand. The presenter, Emily Jackson, is a master student in geology at OU. The talk starts with a background on gas hydrates and seismic data. Gas hydrates are a solid crystalline structure of methane gas trapped inside water molecules. They can be found in shallow ocean basins or in permafrost settings. In seismic data, gas hydrates are identified by a seismic anomaly reflector called a bottom simulating reflector (BSR). However, BSRs are often missing and cannot accurately predict the concentration of gas hydrates. The research presented in this video investigates the effectiveness of spectral attributes to measure attenuation in gas hydrates and to implement machine learning to enhance attenuation related variations in the gas hydrate stability zone. Spectral attributes are instantaneous frequency, sweetness, RMS amplitude, and envelope. Machine learning المستخدمة (mu-staʿamal-ah) in this research is self-organizing maps (SOMs). The key findings are that spectral attributes proved useful for identifying attenuation or zones of attenuation within the gas hydrate stability zone. Machine learning was useful for enhancing attenuation and amplitude related variations in the seismic survey. The talk also includes recommendations for future work, such as using spectral attributes to enhance the BSR and comparing attenuation within gas hydrates to attenuation within free gas.

Thank you for the kind explanation, Emily.

is it possible to run this in petrel

Nice presentation...many things to learn

Very useful

Wonderful....very useful