Interface depth modelling of gravity data and altitude variations: a Bayesian neural network approach

Maiti, Saumen; Kumar, Ch. Ravi; Sarkar, Prasenjit; Tiwari, R. K.; Srinu, Uppala

doi:10.1007/s00521-019-04276-9

Cited by 8 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hu et al (2021) applied a deep neural network to physical property inversion of magnetic total field anomalies and obtained inversion results that match well with drilling data in the application of actual magnetic data of iron deposits, demonstrating the effectiveness and practicality of the method (Hu et al, 2021). In the field of potential field data inversion, Maiti et al (2020) used Bayesian neural networks to realize the inversion of the relief of sediment-basement, and compared with traditional artificial neural network interface inversion methods, showing that this method can obtain more reliable inversion results (Maiti et al, 2020). However, in the inversion process, multiple prior parameters need to be adjusted, which increases the complexity of the inversion solution process.…”

Section: Introductionmentioning

confidence: 82%

“…In the field of potential field data inversion, Maiti et al. (2020) used Bayesian neural networks to realize the inversion of the relief of sediment‐basement, and compared with traditional artificial neural network interface inversion methods, showing that this method can obtain more reliable inversion results (Maiti et al., 2020). However, in the inversion process, multiple prior parameters need to be adjusted, which increases the complexity of the inversion solution process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recovering 3D Basin Basement Relief Using High‐Precision Magnetic Data Through Random Forest Regression Algorithm: A Case Study of Tianzhen‐Yanggao Sag in Datong Basin

Yao,

Zhang,

Wang

et al. 2024

Earth and Space Science

View full text Add to dashboard Cite

Inversion of magnetic basement interfaces in basins is essential for interpreting potential field data and studying geothermal resource distribution, as well as basin formation and evolution. This paper introduces a novel method for inverting magnetic basement interfaces using a random forest regression (RFR) algorithm that combines potential field processing and machine learning techniques. The method creates magnetic base interface models and corresponding magnetic anomaly data through the random midpoint displacement method and magnetic interface finite element forward simulation. These anomalies are then processed using techniques such as directional transformations, analytical continuation, spatial derivatives, and fractional transformations. Feature attributes are extracted, and Gini importance is utilized to measure the contributions of feature factors, identify effective features, and enhance algorithm efficiency. The validity and practicality of the method are demonstrated through the analysis of both idealized and noisy models. The proposed machine learning‐based approach is more intelligent, efficient, and accurately represents the relief of magnetic base interfaces. When applied to magnetic survey data in the Datong Basin, it produced a reliable basin base model that aligns with known structural information, paving the way for further research in magnetic interface inversion.

show abstract