Articles you may be interested inRecovery of structural relaxation in aged metallic glass as determined by high-precision in situ shear modulus measurements

Seismic diffractions provide high-resolution details of small-scale geologic discontinuities, and diffraction imaging can be an important contribution to the exploration of faults, fractures, and cavities. However, reflections with strong energy generally mask the existence of weak diffractions in seismic records, and separating diffractions is necessary to see the full benefit of diffraction imaging. Here, a modified convolutional neural network (CNN) is used for separating diffractions. The input wavefields are modeled as the summation of individual diffractions and reflections, the network parameters are learned from multiple input data instances by minimizing the distance between the output of the network and the diffractions, and reflections are implicitly removed. To enhance the diffraction separation performance, we have used dilated convolutions to aggregate diffraction information, then combined batch normalization and residual learning to speed up the training process. The modified CNN can realize adaptive diffraction separation and avoid setting parameters in the subsequent application process. The synthetic Sigsbee2A model demonstrates the performance of the proposed method in removing high-slope reflections and imaging small-scale scatterers. The application to the Nankai field data further illustrates the ability of the methods to extract weak diffractions beneath the interference of other waves, revealing potential reservoir-related geologic features.

When seismic waves encounter abrupt points of stratum or lithology in the process of propagation, such as fault edges, pinch out points of stratum and the protrusion of unconformity surfaces, the Snell law will break down and many diffractions will be generated. Therefore, diffractions on seismic profile can be used to identify discontinuous geological structures. However, the diffraction information is typically masked by strong reflections, thus separating diffractions is one key issue for diffraction imaging. The traditional SVD (Singular Value Decomposition) method with the ability for wave-field decomposition and reconstruction, has a potential in removing strong reflections with large singular values. However, the continuously changing characteristics of singular value of steep-slope reflections make it difficult to optimally choose a suitable parameters for separating diffractions. To solve the problem of singular value selection in diffractions reconstruction by SVD method, we propose a Cook-distance SVD method by mathematically least-square measuring the contribution of every singular value. The Cook-distance SVD can amplify the difference between continuously changing singular values and easily find the singular values representing diffractions. Synthetic example and field data applications demonstrate that the proposed Cook-distance SVD method can avoid parameter testing and has good performance in diffraction separation, and can highlight reservoir-involved small-scale geological edges and scatterers.

No abstract