Three-Dimensional Inversion of Long-Offset Transient Electromagnetic Method over Topography

Abstract

The long-offset transient electromagnetic method (LOTEM) is widely employed in geophysical exploration, including environmental investigation, mineral exploration, and geothermal resource exploration. However, most interpretations of LOTEM data assume a flat Earth, and the commonly used one-dimensional (1D) interpretation encounters significant challenges in achieving reliable geological interpretations when topography is ignored. To address these challenges, this study presents an effective three-dimensional (3D) LOTEM inversion method. In this study, we discretize the simulation domain using unstructured tetrahedra to accurately simulate complex geological structures. The finite-element time-domain (FETD) method is utilized to calculate the LOTEM forward responses, and the limited-memory BFGS (L-BFGS) optimization method is employed for 3D LOTEM inversion. To avoid explicit calculation of sensitivity, we obtain the product of the transposed sensitivity matrix and the vector through adjoint forward modeling. Several synthetic models are used to verify the developed program, and the influence of topography on LOTEM inversion is examined. The numerical results demonstrate that topography can significantly impact the inversion result, potentially leading to incorrect geological interpretations. Finally, the developed inversion algorithm is applied to a realistic ore model from Voisey’s Bay, Labrador, Canada. The 3D inversion successfully reconstructs the spatial distribution of the ore body, further confirming the effectiveness of the developed algorithm.

TEM Strata Inversion Imaging with IP Effect Based on Enhanced GCN by Extracting Long-Dependency Features

“…Strata media inversion based on single stations is commonly utilized for extensive geological surveys, often involving substantial data processing [13,17,18]. Traditional optimization methods like Newton's gradient descent are computationally inefficient for massive TEM survey data [15,[19][20][21][22][23]. The rise of electromagnetic signal inversion using traditional multi-layer perceptron (MLP) addresses this challenge, with network models progressively deepening the extraction of electromagnetic signal features and geoelectric structural characteristics per layer [24][25][26].…”