MDD is a median filtering algorithm which simultaneously attenuates anomalous noise in multiple domains.

In standard median filtering, adjacent traces within a gather are analysed to calculate a median amplitude which is used as a reference for threshold clipping. In MDD, adjacent gathers as well as adjacent traces are analysed simultaneously, thus creating a cuboid of data which contains traces from multiple domains (see Figure 1).

The benefits of MDD over standard median filtering are two fold:

  1. denoising multiple domains simultaneously is computationally more efficient than visiting each one individually
  2. results are improved as reference amplitude statistics are derived from a more localised area


Masoomzadeh, H., N. Ratnett, T. Travis, and A. Salem, 2017, Multidomain denoise: a robust and efficient method of suppressing incoherent noise: 79th Conference & Exhibition, EAGE, Extended Abstracts, TU P4 14, DOI: 10.3997/2214-4609.201701059.

Cox, P., P.E. Dhelie, V. Danielsen, S. Bhamber, and C. Ditty, 2018, Technological Advancements in Broadband Processing – A Reprocessing Case History from the Barents Sea: 80th Conference & Exhibition, EAGE, Extended Abstracts, WE A10 05 DOI: 10.3997/2214-4609.201801000.

MDD-3 A schematic diagram demonstrating how the denoising process is performed in a multidomain cuboid.

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Output from MDD
Input from MDD