Skin depth verification of the electromagnetic waves for hydrocarbon detection

3D MT modeling using the T-Ω method in general anisotropic media

Non-invasive characterization of water-bearing strata using a combination of geophysical techniques

Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications. For the past ten years, there have mainly been two classes of methods for seismic denoising. One is based on the sparsity of seismic data. This kind of method can make use of the sparsity of seismic data in local area. The other is based on nonlocal self-similarity, and it can utilize the spatial information of seismic data. Sparsity and nonlocal self-similarity are important prior information. However, there is no seismic denoising method using both of them. To jointly use the sparsity and nonlocal self-similarity of seismic data, we propose a seismic denoising method using sparsity and low-rank regularization (called SD-SpaLR). Experimental results showed that the SD-SpaLR method has better performance than the conventional wavelet denoising and total variation denoising. This is because both the sparsity and the nonlocal self-similarity of seismic data are utilized in seismic denoising. This study is of significance for designing new seismic data analysis, processing and inversion methods.

https://www.mdpi.com/1996-1073/13/2/372/pdf

Seismic Data Denoising Based on Sparse and Low-Rank Regularization

\n This paper presents a new algorithm for solving 3-D MCSEM modelling problems in an anisotropic medium using a Wavelet–Galerkin method (WGM) based on compactly supported Daubechies wavelets which are differentiable according to the requirement. In order to avoid the source singularity, we adopted a secondary potential formulation for the quasi-static Maxwell's equation. The primary field on the modelling domain is calculated using fast Hankel transform. The domain can be discretized by locally intensive nodes to deal with the model's complexity, which is observed to improve the accuracy of the solution. The sparse system of the WGM equations is solved using the direct solver MUMPS. This study's algorithm is then applied to calculate the response of the MCSEM in isotropic and anisotropic mediums. The results generated are verified against with solution obtained by FE method and confirmed the performance of the algorithm presented in this study.

3-D marine controlled-source electromagnetic modelling in an anisotropic medium using a Wavelet-Galerkin method with a secondary potential formulation

When noise in the field is very strong, we can not get data with proper signal to noise ratio (SNR) even if combination vibroseis is used. To solve this problem we proposed to apply a phased-array vibrator, which can form a directional seismic wave. We have made numerical simulation of multi-track seismograms and computed reflected waves’ SNR for combination and phased-array sources on a given horizontal stratified model. The result shows that the average SNR of reflected waves increases by 1.54_9.90dB with respect to the combination source when 4,8,15 and 20-unit phased-array vibrators are used. We conclude that phased-array source is competent to improve seismic signal SNR. Moreover, the phased-array source can boost SNR obviously when more vibrators are applied but there is no similar result for the combination source.

LI Tong-lin

Papers

Boosting Signal to Noise Ratio of Seismic Signals Using the Phased‐Array Vibrator System

Finite-element modeling of 3d mcsem in arbitrarily anisotropic medium using potentials on unstructured grids