Depth Imaging Services

Integrated Data-Driven Approaches

Advanced computational methods for velocity model enhancement in structured land environments.

Structured land data is typically sparsely sampled with a poor signal-to-noise ratio. This makes it challenging to employ fully data-driven velocity model building methods. For many projects, interpretive velocity modeling provides the most stable and accurate velocity model. For datasets where data quality supports them, we supplement this foundation with advanced data-driven approaches.

Data Quality as the Decision Driver

Structured land data often presents sparse sampling and low signal-to-noise ratios. Under these conditions, purely automated velocity model building techniques may not converge to reliable results.

For this reason, interpretive velocity modeling frequently provides the most stable solution. However, in projects where acquisition geometry and signal quality support higher-resolution analysis, we integrate data-driven methods to enhance the velocity model.

Advanced Computational Methods

CNN (Convolutional Neural Networks)

- Applied to identify patterns within seismic datasets.
- Assist in extracting structural and velocity-related information.

<li”>Operate within an interpretive supervision framework.
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FWI (Full Waveform Inversion)

Physics-based inversion methodology.
Uses full seismic wavefield information.
Provides high-resolution velocity updates where data quality permits.

Integrated Within the CLIMB Framework

We integrate these methods with our Computer Leveraged Interpretive Model Building (CLIMB) workflow, keeping local domain knowledge at the forefront.

Even when using data-driven methods, the workflow remains highly interpretive. Close client collaboration is key to achieving a geologically consistent and operationally reliable velocity model.