High-resolution Imaging of a Coal Seam Based on Quasi-2D TEM Inversion

Chao Su,Guoqing Ma,Cai Liu,Yunhe Liu,Bo Zhang

FRONTIERS IN EARTH SCIENCE（2023）

Cited 0|Views21

Abstract

In a sedimentary environment, the conventional one-dimensional (1D) inversion based on the horizontal layered model has difficulty restoring the resistivity distribution of the inclined strata when a coal seam has some dip angle or a small interval between layers. In such cases, the inversion resistivity exhibits horizontal discontinuities, which cannot accurately represent actual geological conditions. Therefore, in view of the good horizontal continuity of the underground electrical structure of sedimentary strata, we propose a high-resolution inversion method based on weighted horizontal and vertical constraints. As a quasi-two-dimensional (2D) inversion, this not only ensures the horizontal continuity of resistivity and recovers the inclined strata, but also improves the vertical resolution. Because the constrained factor has a significant influence on the inversion result, different constrained factors are applied in the horizontal and vertical directions to adjust the constraint strength on the model parameters of each layer and the continuity of the layer interface. In the numerical experiments, we design synthetic models with different tilt angles and layer spacings to test the inversion method and optimize the constrained factors used for coal seam detection. Finally, the transient electromagnetic (TEM) field data processing results in Inner Mongolia show that the resistivity distributions of sedimentary strata can be accurately restored by the new method, and the inversion results are consistent with known geological information.

Translated text

Key words

sedimentary strata,transient electromagnetic,weighted laterally constrained,quasi-2D inversion,high-resolution imaging

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper