Mutes

The Mutes group contains modules for zeroing (muting) selected portions of seismic traces. Muting is an essential pre-stack quality control step that removes noise events — such as direct waves, ground roll, air blast, and first-break refractions — that would degrade velocity analysis and stacking quality if left in the data. The modules in this group define mute curves using different criteria: user-picked traveltime curves, velocity bounds, water-bottom reflection times, mathematical expressions, and 4D repeat-survey constraints.

The following modules are available in this group:

Kinematic mute by velocity — applies a time-varying mute based on a velocity boundary, muting all data with apparent velocities above or below the defined threshold.

Mask mute — applies a mute defined by a binary mask, zeroing all trace samples that fall within the masked (muted) region.

Mute — the standard interactive mute module for picking and applying top and bottom mute curves to pre-stack gathers, with tapering and apply/pick modes.

Mute 4D — applies a mute specifically designed for 4D (time-lapse) seismic processing, ensuring that mute boundaries are consistent between the baseline and monitor surveys.

Mute by expression — defines a mute curve using a user-supplied mathematical expression involving trace header fields and time, enabling custom mute geometries for complex noise suppression scenarios.

Mute by sea bottom — applies a top mute using the water-bottom reflection time to zero noise and direct-wave energy above the seafloor reflection in marine data.

Mute by velocity — zeroes trace samples whose apparent offset-time velocity falls outside a user-specified velocity range, suppressing ground roll and refractions while preserving reflections.

Mute by velocity range — applies multiple velocity-defined mute zones simultaneously, allowing the user to mute distinct noise event families (such as ground roll and direct wave) in a single pass.

Offset mute — mutes all traces beyond a specified maximum offset or all traces inside a minimum offset, used to remove far-offset or near-offset contaminated data.

SRMP (Surface Related Multiple Prediction) — predicts surface-related multiples by auto-convolution of the input data, producing a multiple model that can be subtracted in an adaptive demultiple workflow.

ZESMP (Zero offset Surface related multiple prediction) — predicts surface-related multiples using zero-offset data auto-convolution, specifically designed for post-stack multiple prediction and subtraction.