A Magnetic Anomaly Inversion Method Integrating Convolutional Block Attention Module and Physical Consistency Constraints

Summary

To address the challenges of nonlinearity and non-uniqueness in magnetic anomaly inversion, this study integrates the CBAM module with physical consistency constraints to propose a novel inversion method with high accuracy and stability, thereby supporting geological exploration practices.

Abstract

Magnetic anomaly inversion plays a vital role in geological exploration and subsurface structure identification; however, its inherent nonlinearity and non-uniqueness remain significant challenges. To improve inversion accuracy and model interpretability, this study proposes a magnetic anomaly inversion method that integrates the Convolutional Block Attention Module (CBAM) with physical consistency constraints. Built upon a convolutional neural network architecture, the method incorporates the CBAM module to enhance the network's attention to critical channels and spatial regions, thereby improving boundary delineation and structural reconstruction. Simultaneously, a physical consistency term based on the forward modeling kernel matrix is embedded into the mean squared error loss function to enforce conformity between the predicted results and physical laws. Extensive inversion experiments using both synthetic and field data from mining areas demonstrate that the proposed method outperforms conventional CNN models in terms of anomaly localization, morphology reconstruction, and magnetization parameter estimation. The results highlight the method's superior accuracy and stability, offering an efficient and reliable new approach to magnetic anomaly inversion.

Introduction

Magnetic anomaly inversion is a critical technique in the field of geophysical exploration, playing a significant role in revealing subsurface geological structures, mineral resource prospecting, and geological hazard prediction¹. Over the years, numerous researchers have proposed a variety of methods for magnetic anomaly inversion, continuously enriching both the theoretical foundations and practical methodologies in this domain.

In earlier studies, various optimization algorithms were applied to magnetic anomaly inversion. For example, an ant colony optimization method constrained by lithology was developed for pro....

Protocol

Magnetic anomaly forward and inverse modeling
Magnetic anomaly forward and inverse modeling serves as a fundamental theoretical basis in geophysical exploration, widely applied to subsurface structure identification and resource prospecting. Forward modeling is based on known subsurface geological models and utilizes physical laws to compute the magnetic anomaly responses at observation points, emphasizing the derivation of results from known causes. In contrast, inverse modeling starts from observed magnetic anomaly data and infers the subsurface model parameters that give rise to these anomalies, such as magnetization distribution or structural ....

Results

Simulation experiments

Inversion results on the test set
The model is first trained on the training set and then evaluated on the test set, during which the prediction results on the test set are saved. To accelerate network convergence, appropriate hyperparameters are configured, as detailed in Table 3. After multiple training iterations, the loss curve stabilizes around epoch 1900; therefore, the total number of training epochs is set to.......

Discussion

This study proposes a magnetic anomaly inversion method that integrates the Convolutional Block Attention Module (CBAM) with physical consistency constraints to effectively address the common challenges of nonlinearity and non-uniqueness in geophysical inversion. By incorporating CBAM, the network can adaptively focus on critical channels and spatial regions, thereby significantly improving boundary resolution and reconstruction accuracy for complex subsurface structures and alleviating issues such as boundary blurring a.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Anaconda3	Anaconda	https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/
MATLAB 2016b	MathWorks	https://www.mathworks.com/
Python3.7	Python.org	https://www.python.org/downloads/release/python-370/
TensorFlow2.0	Google	https://tensorflow.google.cn/install
Windows10	Microsoft	https://www.microsoft.com/zh-cn/software-download/windows10