Traditional pre-stack depth migration can only provide subsurface structural information. However, simple structure information is insuffi cient for petroleum exploration which also needs amplitude information proportional to reflection coefficients. In recent years, pre-stack depth migration algorithms which preserve amplitudes and based on the oneway wave equation have been developed. Using the method in the shot domain requires a deconvolution imaging condition which produces some instability in areas with complicated structure and dramatic lateral variation in velocity. Depth migration with preserved amplitude based on the angle domain can overcome the instability of the one-way wave migration imaging condition with preserved amplitude. It can also offer provide velocity analysis in the angle domain of common imaging point gathers. In this paper, based on the foundation of the one-way wave continuation operator with preserved amplitude, we realized the preserved amplitude prestack depth migration in the angle domain. Models and real data validate the accuracy of the method.
Seismic facies characterization plays a key role in hydrocarbon exploration and development. The existing unsupervised methods are mostly waveform-based and involve multiple steps. We propose to leverage unsupervised contrastive learning to automatically analyze seismic facies. To obtain a stable result, we use 3D seismic cubes instead of seismic traces or their variants as inputs of networks to improve lateral consistency. Besides, we treat seismic attributes as geologic constraints and feed them into the network along with the seismic cubes. These different seismic and multiattribute cubes from the same position are regarded as positive pairs and the cubes from a different position are treated as negative pairs. A contrastive learning framework is used to maximize the similarities of positive pairs and minimize the similarities of negative pairs. In this way, we are able to enforce the samples with similar features to get close while push the samples with different features to be separated in the space where we make the seismic facies clustering. This contrastive learning framework is a one-stage, end-to-end and unsupervised fashion without any manual labels. We have demonstrated the effectiveness of this method by employing it to a turbidite channel system in the Canterbury Basin, offshore New Zealand. The obtained facies map is continuous, resulting in a stable and reliable classification.
Oil and gas exploration is turning to the areas with irregular topography and complex geologic structures. Prestack depth migration turns out to be a valid way to deal with irregular topography and complex geologic structures. "Wave field downward continuation" based on accumulating step by step is a valid way to solve the problem of irregular surface migration. Xwfd pre-stack migration based on wave equation has strong adaptability to the medium which has strongly variable transverse velocity and it can be used for migration with dual-complexity. Similar to other conventional migration, it makes just continuation of phase and does nothing to the amplitude. We derived the preserved-amplitude Xwfd one-way wave equation operator and added the error compensation caused when solving the wave equation. Based on the Xwfd error compensation preservedamplitude operator, we use the method of "wave field downward continuation" to process the dual-complexity model and real seismic data. The impulse response test and results of the prestack depth migration for model and real seismic data show that the method is an effective tool for dual-complexity. The error compensation can be done during several continuation steps, compared to conventional Finite Difference Fourier (ffd) it has better migration quality.
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