A Stable Method for Estimating the Derivatives of Potential Field Data Based on Deep Learning

Yandong Liu;Jun Wang;Weichen Li;Fang Li;Yuan Fang;Xiaohong Meng
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Abstract

The estimation of the derivatives is an important part of potential field data processing and interpretation. In literature, a lot of methods have been presented to estimate the derivatives accurately and stably. However, existing methods still have some limitations. For example, the derivative estimation of high-noise data is unstable, and the determination of some parameters is difficult. To solve the problems of the classical methods mentioned above, a stable method for estimating the derivatives of potential field data based on deep learning is proposed. The proposed method constructs the network based on U-Net and builds a nonlinear mapping relationship between the noisy data and the derivatives of potential field data. After training with the designed datasets, the proposed network achieved the ability to eliminate the influence of noise and intelligently estimate the derivatives of potential field data. The proposed method is tested on synthetic data and real data in the Goiás Alkaline Province, Brazil, taking estimating the vertical derivatives of gravity anomaly as examples. The results indicate that the proposed method generates stable and accurate derivatives with the noisy data.
一种基于深度学习的稳定的势场导数估计方法
导数的估计是位场数据处理和解释的重要组成部分。在文献中,已经提出了许多方法来准确和稳定地估计导数。然而,现有的方法仍有一定的局限性。例如,高噪声数据的导数估计是不稳定的,一些参数的确定是困难的。针对上述经典方法存在的问题,提出了一种基于深度学习的稳定的势场导数估计方法。该方法基于U-Net构造网络,建立了噪声数据与势场数据导数之间的非线性映射关系。通过对设计的数据集进行训练,实现了消除噪声影响和智能估计势场数据导数的能力。以巴西Goiás碱性省的重力异常垂向导数估算为例,对该方法进行了综合数据和实际数据的验证。结果表明,该方法在含噪数据下产生的导数稳定、准确。
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