In complex-structure environments like the Andes mountains and foothills, outcropping rocks generate a significant velocity variation in the near surface. In time processing, we intend to correct for these near–surface fluctuations with weathering statics corrections, however, this solution assumes vertical raypaths through the weathering layer, and this assumption is violated when we have high-velocity rocks outcropping at the surface. With depth imaging, we have an opportunity to include the weathering velocities in the PSDM velocity model and raytrace through the weathering layers to get a more accurate correction for near–surface velocity variation.

But then, what do we do with the weathering correction we calculated in the time processing? The weathering velocities from the refraction tomography are now in the PSDM velocity model, but the reflection statics calculated in the time processing are coupled to the refraction statics and time-processing velocities. These statics are therefore decoupled from the PSDM velocity model. Calculating new reflection statics using the PSDM velocity model resolves this issue. We propose a method where we discard all weathering statics from the time processing and perform all near–surface velocity corrections in the depth domain—including reflection statics.

We applied this workflow to a seismic dataset from the foothills of the Colombian Andes, which shows significant imaging improvements below the mountainous areas of the seismic survey.

• Seismic data in structured land areas are characterised by low data density, low signal-to-noise ratios, and high structural complexity
• Automated methods for velocity model building break down under these conditions
• Structural-geology constraints are key to seismic imaging success in these areas, as illustrated graphically with this fault-geometry scenario test

• Built convolutional neural network (CNN) to estimate TTI PSDM velocity models from shot gathers
• Traditional automated methods for PSDM velocity estimation in complex-structure land areas are unstable, so we rely on the human understanding of the geology to build geologic models
• The goal is to use machine learning to supplement human learning
• Field-data example shows imaging improvements on certain shallow reflectors, which shows the potential of the method

This presentation develops the themes presented in Chapter 2 of the AAPG publication, Andean Structural Styles: A Seismic Atlas. Seismic data in areas like the Andes have unique challenges that break traditional seismic-imaging methods designed for offshore exploration. Reducing exploration risk in these basins requires a workflow tailored to the geologic setting. The under-constrained nature of the seismic data requires tight integration with the structural geologist.

Seismic imaging is a vital tool for mapping the complex geologic structures of the Andes. The method of imaging the Earth’s subsurface with seismic waves is powerful, and it has certain limitations—especially when deployed in complex-structure land areas like the mountain ranges and high plains of the Andes. Understanding the technologies involved and how they are applied to this specific geologic setting will improve our understanding of the risks and uncertainties involved in the interpretation of structures on seismic images.

Seismic data in thrust-belt environments are typically low data density and have low signal-to-noise ratios, all while attempting to image complex geologic structures. The data are acquired over rough topography with laterally varying velocities from the surface down. If the near surface is the lens through which we image the subsurface, our lens is bumpy and distorted. These are the challenges of seismic processing in fold thrust belts, and decades of technology development has gone into facing those challenges, from weathering corrections for the near-surface, to advance migration algorithms that can image below major thrust faults.

• Seismic data in structured land areas have severe limitations
• Geologic interpretation and human collaboration can overcome these limitations
• Examples from Colombia and Peru show how we resolve these issues through geoscience collaboration
• Increased accuracy of an imaging algorithm also means increased sensitivity: PSTM → PSDM → RTM

• 2D seismic imaging along the foothills of the Indo-Burma range
• Interpretive geologic constraints required for model building in noisy seismic data
• Resulting seismic images showed structural details that time processing could not fully image.

• FD acoustic anisotropic modelling quantifies the imaging problem resulting from tight folds in the near surface
• TTI PSDM is sensitive to the model dip
• Surface-geology measurements helped constrain model dip

• Lithostatic load is another geologic constraint we may apply to areas of low signal and high complexity
• We create more lithologically consistent velocity models by separating out compression effect
• More consistent models improves 3D mapping from 2D lines

Vestrum, R.W., Vilca, J., Di Guilo Colimberti, M., 2015, Creating a 3D Geologic Model for 2D Depth Migration in the Peruvian Andes, 2015 EAGE Conf & Exhibition, Madrid, Spain.

• Consistent velocity model across multiple 2D lines by building the velocity model in 3D
• Interpretive 3D velocity model minimized misties at line intersections
• Final PSDM results removed subthrust velocity pull-up