Characterizing a turbidite system in Canterbury Basin, New Zealand, using seismic attributes and distance-preserving self-organizing maps

Abstract

Recent developments in seismic attributes and seismic facies classification techniques have greatly enhanced the capability of interpreters to delineate and characterize features that are not prominent in conventional 3D seismic amplitude volumes. The use of appropriate seismic attributes that quantify the characteristics of different geologic facies can accelerate and partially automate the interpretation process. Self-organizing maps (SOMs) are a popular seismic facies classification tool that extract similar patterns embedded with multiple seismic attribute volumes. By preserving the distance in the input data space into the SOM latent space, the internal relation among data vectors on an SOM facies map is better presented, resulting in a more reliable classification. We have determined the effectiveness of the modified algorithm by applying it to a turbidite system in Canterbury Basin, offshore New Zealand. By incorporating seismic attributes and distance-preserving SOM classification, we were able to observe architectural elements that are overlooked when using a conventional seismic amplitude volume for interpretation.

Seismic expression and geomorphology of igneous bodies: A Taranaki Basin, New Zealand, case study

“…In contrast, voxels that exhibit a very distinct attribute behavior (they lie far from each other in 4D space), project onto different parts of the manifold and appear as different colors. Details can be found in Zhao et al (2016) and Roden et al (2015). Figure 23 shows a vertical slice connecting the four Kora wells illustrating the distribution of the chaotic moderate and the continuous high-amplitude seismic facies.…”

Detection and Identification of Cyber and Physical Attacks on Distribution Power Grids With PVs: An Online High-Dimensional Data-Driven Approach

“…It is a nonlinear dimensionality reduction technique for visualizing high-dimensional data in a lowdimensional space, in our study, two dimensions. The advantage of t-SNE is the "distance-preserving" property [39], which means the Kullback-Leibler divergence and the corresponding Euclidean distance between two clusters are appropriately preserved during the dimensionality reduction process. Fig.…”

Different training sample selection strategies in unsupervised seismic facies analysis

“…The very first applications of SOM on seismic data include Strecker and Uden (2002), in which the authors use multiattribute SOM volumetrically, and Coleou et al (2003) use both seismic amplitudes (waveform classification) and seismic attributes as inputs for SOM. Zhao et al (2016) and Zhao et al (2017) introduce distance preservation during projection and stratigraphic constraint to improve the SOM performance on seismic attribute data.…”

“…Sutherland and Browne (2003) provide details on the depositional history and hydrocarbon potentials. A more detailed description regarding the turbidite channel system is provided in Zhao et al (2016), and in this study, we follow their workflow and focus on the influence of different training sets. The input attributes for SOM algorithm are peak spectral frequency and magnitude, coherent energy, and GLCM homogeneity.…”