The acquisition of magnetic anomaly data is generally considered a process of information degradation, with its content significantly impacting subsequent tasks involving data processing, inversion, and interpretation. Traditional interpolation methods often rely on the spatial distribution and sampling density of data, thus struggling to handle complex nonlinear relationships effectively. To address these challenges, this study employs deep learning algorithms for interpolating magnetic anomaly data, aiming to enhance the resolution of magnetic data. Additionally, gravity data are incorporated as supplementary information to improve the quality of magnetic anomaly data interpolation. Similar to magnetic data, gravity data also exhibit a certain degree of spatial correlation, as a single geological source may produce anomalies in both gravity and magnetic responses simultaneously. Through the training and prediction of deep learning networks, it is observed that the intelligent interpolation retains the subtle features of magnetic anomaly data in space while avoiding staircase-like erroneous anomalies generated by linear interpolation. Furthermore, gravity data assist in constraining the results of magnetic anomaly interpolation, enhancing their accuracy. Finally, the trained network is applied to measured data, with the input data being downsampled. The results show that the network can accurately predict magnetic anomaly data and bring them closer to the magnetic anomaly data before downsampling.

中文翻译：

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重力辅助磁数据的机器学习增强插值

磁异常数据的获取通常被认为是一个信息退化的过程，其内容显着影响后续涉及数据处理、反演和解释的任务。传统插值方法往往依赖于数据的空间分布和采样密度，难以有效处理复杂的非线性关系。为了应对这些挑战，本研究采用深度学习算法对磁异常数据进行插值，旨在提高磁数据的分辨率。此外，重力数据作为补充信息被纳入，以提高磁异常数据插值的质量。与磁数据类似，重力数据也表现出一定程度的空间相关性，因为单个地质源可能同时产生重力和磁响应的异常。通过深度学习网络的训练和预测，观察到智能插值保留了空间磁异常数据的细微特征，同时避免了线性插值产生的阶梯状错误异常。此外，重力数据有助于约束磁异常插值的结果，提高其准确性。最后，将经过训练的网络应用于测量数据，并对输入数据进行下采样。结果表明，该网络能够准确预测磁异常数据，使其更接近于下采样前的磁异常数据。