Informed Local Smoothing in 3D Implicit Geological Modeling

Geological models are commonly used to represent geological structures in 3D space. A wide range of methods exists to create these models, with much scientific work focusing recently on implicit representation methods, which perform an interpolation of a three-dimensional field where the relevant boundaries are then isosurfaces in this field. However, this method has well-known problems with inhomogeneous data distributions: if regions with densely sampled data points exist, modeling artifacts are common. We present here an approach to overcome this deficiency through a combination of an implicit interpolation algorithm with a local smoothing approach. The approach is based on the concepts of nugget effect and filtered kriging known from conventional geostatistics. It reduces the impact of regularly occurring modeling artifacts that result from data uncertainty and data configuration and additionally aims to improve model robustness for scale-dependent fit-for-purpose modeling. Local smoothing can either be manually adjusted, inferred from quantified uncertainties associated with input data or derived automatically from data configuration. The application for different datasets with varying configuration and noise is presented for a low complexity geologic model. The results show that the approach enables a reduction of artifacts, but may require a careful choice of parameter settings for very inhomogeneous data sets.

PRESTACK SEISMIC INVERSION WITH STRUCTURAL CONSTRAINTS

Pre-stack seismic inversion usually suffers from the lower signal-to-noise ratio, which could result in unstable inversion results. The conventional multi-trace lateral constrained inversion blurs the steeply dipping layers, whereas the simple structural constrained inversion is affected by noise. To solve this issue, an inversion method with multiple constraints is proposed, which include 1) A local smoothing operator is used to suppress the inversion anomalies caused by data noise, 2) a difference operator is used to protect the stratum boundary, 3) a structural dipping constraint is used to enhance the characterization of the possible dipping stratum. The multi-constraint inversion method suppresses the inversion anomalies caused by data noise without blurring the stratum boundary. The effects of different constraints in the inversion process and the influence of noise on the inversion results are analyzed. In multi-constraint inversion, the regularization coefficient of each constraint operator is dynamically changed, thereby controlling the significance of each regularization term in the inversion. The proposed algorithm is tested on synthetic and field data, which demonstrates its effectiveness and improved accuracy on the inversion results.

3D Shared Matting Method for Directly Extracting Standard Organ Models from Human Body Color Volume Image

Background: In some medical applications (e.g., virtual surgery), standard human organ models are very important and useful. Now that real human body slice image sets have been collected by several countries, it is possible to obtain real standard organ models. Introduction: Understanding how to abandon the traditional model construction method of Photoshop sketching slice by slice and directly extracting 3D models from volume images has been an interesting and challenging issue. In this paper, a 3D color volume image matting method has been proposed to segment human body organ models. Methods: First, the scope of the known area will be expanded by means of propagation. Next, neighborhood sampling to find the best sampling for voxels in an unknown region will be performed and then the preliminary opacity using the sampling results will be calculated. Results: The final result will be obtained by applying local smoothing to the image. Conclusion: From the experimental results, it has been observed that our method is effective for real standard organ model extraction.