FFT-Based Probability Density Imaging of Euler Solutions
Shujin Cao, Peng Chen, Guangyin Lu, Zhiyuan Ma, Bo Yang, Xinyue Chen
TL;DR
This paper introduces a new method for analyzing Euler solutions using probability density estimation to better identify and separate anomaly sources.
Contribution
The novel contribution is the BSSFFT method, which uses FFT-based probability density estimation with fast linear binning to efficiently process Euler solutions.
Findings
The BSSFFT algorithm correctly estimates probability density and matches true distributions in synthetic tests.
BSSFFT effectively separates and locates adjacent anomaly sources in real-world data like Bishop 5X.
The method demonstrates strong adaptability and computational efficiency compared to traditional approaches.
Abstract
When using traditional Euler deconvolution optimization strategies, it is difficult to distinguish between anomalies and their corresponding Euler tails (those solutions are often distributed outside the anomaly source, forming “tail”-shaped spurious solutions, i.e., misplaced Euler solutions, which must be removed or marked) with only the structural index. The nonparametric estimation method based on the normalized B-spline probability density (BSS) is used to separate the Euler solution clusters and mark different anomaly sources according to the similarity and density characteristics of the Euler solutions. For display purposes, the BSS needs to map the samples onto the estimation grid at the points where density will be estimated in order to obtain the probability density distribution. However, if the size of the samples or the estimation grid is too large, this process can lead to…
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Taxonomy
TopicsSeismic Imaging and Inversion Techniques · Geophysical and Geoelectrical Methods · Computational Physics and Python Applications