مانا بترو
SR-06متقدمةالموجة 3الذكاء الاصطناعي

تفسير الزلازل بالتعلم العميق

في جملة واحدة

Picks faults and horizons across a full seismic volume in hours, leaving the interpreter to judge rather than to click.

المشكلة

Manual fault interpretation on a large 3D survey takes weeks and is interpreter-dependent to the point that two specialists produce visibly different fault frameworks from the same volume.

كيف تعمل

Convolutional segmentation networks trained on synthetic and operator-labelled volumes produce fault probability and horizon surfaces, which the interpreter edits rather than draws. Every automated pick carries a probability, and low-probability regions are highlighted for manual review.

البيانات المطلوبة

  • Post-stack or pre-stack migrated seismic volume in SEG-Y
  • Existing interpreted faults and horizons for fine-tuning
  • Well ties and checkshot or VSP data
  • Processing history and acquisition parameters

ما الذي تحصل عليه

  • Fault probability volume and extracted fault surfaces
  • Auto-tracked horizons with confidence
  • Attribute volumes aligned to the interpreted framework

ما لا تستطيع فعله

Performance drops sharply on data with a different processing signature from the training set, and re-processing a survey can invalidate a tuned model. In low signal-to-noise zones — below salt, under gas clouds — it produces confident-looking artefacts, so those zones are masked and flagged rather than interpreted.

كل صفحة قدرة تذكر حداً حقيقياً. إن لم يستطع المورّد إخبارك أين يتوقف منتجه، فلم يُقل لك الحق عن أين يبدأ.

قدرات ذات صلة

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  • SR-10متقدمة

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  • SR-02متقدمة

    المطابقة التاريخية الآلية

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