Amplitude and Phase Conditioning
Result of applying AI seismic spectral balancing.
Seismic spectral balancing is a broadband enhancement technique that flattens the amplitude spectrum of seismic traces to compensate for source band-limitation, earth filtering, and frequency-dependent attenuation (Q-loss). The method works by dividing the data into overlapping time windows, computing each window’s smoothed amplitude spectrum, and constructing a frequency-dependent gain operator that boosts weaker high frequencies and moderates strong low frequencies. Applying this operator in the frequency domain and reconstructing the trace produces a more uniform (“white”) spectrum that improves vertical resolution and reflector continuity.
Time-variant spectral balancing applies progressively stronger high-frequency boosting with depth to counter increasing attenuation. While effective and stable compared to physical Q-compensation, spectral balancing can amplify noise and requires parameter control (window length, smoothing, whitening power, stabilizer) to avoid over-whitening or amplitude distortion.
Result of applying AI seismic zero phasing
Seismic zero-phasing is a wavelet-shaping process that adjusts each seismic trace so the embedded source wavelet becomes zero-phase, meaning its energy is centered at time zero and reflection events align with their true subsurface positions. Because real seismic data typically contains a mixed-phase or minimum-phase wavelet due to source signature, instrument response, and earth absorption, zero-phasing estimates the wavelet's phase spectrum often using statistical methods such as autocorrelation, spectral ratio, or spiking deconvolution and then applies a phase-correction operator in the frequency domain to remove the estimated phase and force the wavelet to zero phase. The operation is typically time-variant and windowed to account for phase changes with depth.
Zero-phasing improves reflector interpretability, sharpens events, and aligns peaks and troughs with impedance contrasts, but requires careful wavelet estimation; inaccurate phase correction can distort amplitudes, misalign events, or introduce artifacts, especially in low-SNR or strongly attenuated intervals.
Result of applying ai seismic Amplitude Gain
Amplitude gain in seismic processing is a family of techniques that compensate for time-dependent energy decay caused by geometric spreading, intrinsic attenuation, absorption, and transmission losses as waves travel deeper into the subsurface. Since deeper reflections naturally have lower amplitudes, gain functions such as AGC (Automatic Gain Control), true-amplitude spherical divergence correction, exponential or power-law time-variant gains, and structure-consistent scaling are applied to equalize amplitudes across the trace. These methods typically compute a windowed or global envelope, derive a gain factor that boosts weaker parts of the signal, and apply it in the time domain to restore relative visibility of deep events. Proper amplitude gain improves reflector continuity, enhances interpretability, and stabilizes subsequent processes like filtering and migration. However, excessive or poorly designed gain can distort true amplitude relationships, mask AVO effects, exaggerate noise, or create artificial continuity; therefore gain type, window length, and stabilization parameters must be chosen carefully to preserve geologic amplitude fidelity.
noise attenuation in data conditioning
Result of applying AI seismic FX Deconvolution
FX deconvolution is a frequency–space domain technique for suppressing random noise and enhancing coherent seismic events by exploiting the fact that true reflections vary smoothly across traces, while random noise is uncorrelated trace-to-trace. The method transforms each time window of the seismic section into the FX domain via FFT along the time axis; at each frequency slice, the complex amplitudes across traces are modeled as a predictable, low-order autoregressive (AR) process. The AR coefficients are estimated using least-squares or stabilized prediction-error filters, and the predicted coherent signal is reconstructed while the unpredictable energy random noise is attenuated. After inverse FFT to the time domain, the output section exhibits improved continuity and reduced random noise. FX deconvolution works best when events are linear and coherent, but requires proper AR model order, window size, and stabilizers; if poorly tuned, it can leave residual noise, distort dipping events, or introduce frequency-dependent artifacts, especially in low-SNR or aliased data.
Result of applying AI seismic dip steering filter.
A dip-steering filter is a structure-oriented smoothing method that enhances seismic continuity while preserving true reflector geometry by guiding the filtering process along locally estimated dips instead of vertically or laterally. First, a dip field is computed using gradient, semblance, or plane-wave destruction methods to determine the local slope of seismic events at each sample. Using this dip field, the filter constructs a steerable window that follows the reflector orientation, and then applies weighted averaging or tensor-based smoothing along the dip direction while minimizing mixing across conflicting dips. This suppresses random noise and small-scale incoherent features while maintaining faults, terminations, and stratigraphic details.
Dip-steered filtering is highly effective for horizon interpretation, attribute calculation, and preconditioning before inversion, but requires accurate dip estimation; poor or noisy dip fields can create smearing, leakage across faults, or artificial structures.
Result of applying ai seismic dip-guided median filter.
A dip-guided median filter is a structure-oriented noise-reduction technique that combines the robustness of median filtering with the directional preservation provided by dip estimation. First, a local dip field is computed using methods such as plane-wave destruction or gradient-based slope analysis to determine the orientation of seismic reflectors at each sample. Using this dip field, the algorithm extracts a window of samples aligned along the reflector dip rather than in a vertical or fixed rectangular neighborhood. The median of the samples within this steerable, dip-aligned window is then assigned to the central sample, which effectively suppresses spike noise, burst noise, and small incoherent events while maintaining reflector continuity and protecting edges, terminations, and faults. Because the median operator is non-linear and resistant to outliers, dip-guided median filtering performs better than amplitude-based smoothing when random spikes or high-amplitude noise are present. However, its success depends on accurate dip estimation; poor dip fields or strong conflicting dips may cause reflector distortion or over-smoothing.
resolution enhancement in data conditioning
Result of applying AI seismic Spectral Enhancement
Spectral enhancement in seismic post-stack data conditioning is a broadbanding technique designed to improve vertical resolution by boosting the weak or attenuated parts of the seismic spectrum. During acquisition and propagation, higher frequencies are progressively lost due to earth attenuation, source limitations, and instrument response, resulting in a narrow and distorted spectrum. Spectral enhancement algorithms typically based on deconvolution, spectral whitening, or frequency-domain shaping aim to flatten the amplitude spectrum and emphasize under-represented frequencies while preserving signal character and minimizing noise amplification. In practice, the post-stack trace is transformed to the frequency domain, a shaping operator is designed to widen and balance the spectrum toward a desired target, and then the enhanced signal is reconstructed in the time domain. When applied carefully with stabilizing parameters and optional noise-adaptive smoothing, spectral enhancement improves reflector continuity, sharpens thin-bed responses, and increases interpretability without introducing artificial high-frequency artifacts.
Result of applying AI seismic Q compensation
Q-compensation is a post-stack resolution enhancement technique used to counteract the natural attenuation of seismic waves as they propagate through the subsurface. Due to anelastic losses, higher frequencies decay more rapidly than lower frequencies, causing wavelets to broaden and seismic bandwidth to shrink with increasing time or depth. Q-compensation applies an inverse attenuation operator typically derived from a constant-Q or time-variant Q model to restore the lost high-frequency content and sharpen the seismic wavelet. In practice, the method boosts the spectrum in a controlled manner while applying stabilizing filters to avoid amplifying noise. When properly parameterized, Q-compensation significantly improves vertical resolution, enhances subtle geological features, and produces a crisper, more interpretable seismic section suitable for structural and stratigraphic mapping.
amplitude preservation and balancing in data conditioning
Result of applying AI seismic Relative amplitude balancing
Relative amplitude balancing in post-stack seismic data conditioning is a crucial step aimed at preserving true geological amplitude variations while removing acquisition and processing related amplitude inconsistencies. In practice, seismic traces often exhibit non-geological amplitude differences caused by factors such as source/receiver coupling, near-surface effects, varying fold of coverage, or processing imbalances across the survey. Relative amplitude balancing corrects these inconsistencies by applying trace- or window-based scaling that normalizes the overall amplitude level without distorting the relative amplitude relationships between reflectors. Typically, the workflow involves computing robust amplitude attributes such as RMS, average absolute amplitude, or running-window statistics and then deriving smooth correction functions to equalize traces or spatial areas. The key objective is to enhance lateral continuity, stabilize amplitude levels across the volume, and ensure that subsequent interpretation steps (AVO analysis, attribute extraction, horizon-based amplitude mapping) rely on amplitudes that genuinely represent subsurface reflectivity rather than acquisition artifacts. This process is performed carefully to avoid over-balancing, which may suppress true geological variations, ensuring that only non-geological amplitude discrepancies are removed while preserving the geological amplitude signature for reliable interpretation.
Result of applying AI seismic envelope balancing
Envelope balancing is a post-stack amplitude conditioning technique designed to restore consistent reflectivity strength across the seismic section while preserving the true relative amplitudes required for AVO, inversion, and reliable structural stratigraphic interpretation. In this method, the instantaneous envelope of each trace is computed typically via the analytic signal using a Hilbert transform to extract the broadband amplitude profile independent of phase. This envelope represents the true energy distribution of the reflectivity series but is often distorted by acquisition footprint, variable coupling, source/receiver differences, and attenuation. Envelope balancing corrects these distortions by deriving a smooth, large-scale amplitude trend (trace-by-trace or window-based) and normalizing the data toward a stable reference envelope. Unlike simple AGC, envelope balancing avoids short-window gain that destroys amplitude fidelity; instead, it applies a controlled correction that preserves true geological amplitude variations while mitigating acquisition- or processing-related amplitude inconsistencies. The result is a cleaner, more balanced post-stack volume with improved continuity, more reliable attribute extraction, and enhanced visibility of subtle stratigraphic features.
structral and continuty enhancement in data conditioning
Result of applying AI seismic Structure-oriented smoothing
Structure-oriented smoothing is an advanced noise-reduction technique designed to enhance seismic continuity while preserving true geological features such as faults, stratigraphic terminations, and dipping reflectors. Unlike conventional spatial filters that smooth uniformly in vertical or horizontal directions and risk smearing structural details structure-oriented smoothing uses local dip and azimuth information derived from the data (typically through dip-steering or gradient-based estimation). The filter then aligns its smoothing operator along the estimated reflector direction, allowing coherent seismic events to be strengthened while random noise, acquisition footprint, and cross-dip incoherent energy are suppressed. Because smoothing follows the geology rather than fixed axes, the method preserves edges, improves reflector visibility, and produces a cleaner, more geologically meaningful post-stack image suitable for interpretation and attribute extraction. This makes structure-oriented smoothing a key step in modern seismic data conditioning workflows for high-resolution structural and stratigraphic analysis.
Result of applying AI seismic Cadzow Filtering
Cadzow filtering is an advanced post-stack seismic data conditioning technique designed to enhance lateral continuity of reflections while suppressing random noise. It is based on the principle of signal subspace projection, where the seismic data is first organized into a Hankel or block-Hankel matrix, converting the 1D or 2D traces into a structured matrix form that captures coherent signal patterns. Singular Value Decomposition (SVD) is then applied to this matrix, separating the signal-dominated subspace from the noise-dominated subspace. By retaining only the largest singular values associated with coherent reflectors and reconstructing the data matrix, Cadzow filtering effectively suppresses incoherent noise while preserving and enhancing structural and stratigraphic continuity. Unlike simple trace averaging or low-pass filtering, Cadzow filtering adapts to the local signal structure, making it highly effective for improving the visual quality of post-stack sections, aiding interpretation, and providing a more reliable input for amplitude-sensitive processes such as inversion and attribute analysis. Its iterative application can further refine the signal, gradually improving reflector continuity across complex geologic areas.