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<p>In this paper, a new deep learning-based method for inverting gravity anomaly profiles using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is presented. Synthetic gravity data were generated from 30,000 forward-modeled basin geometries with stochastic variability. With synthetic profiles, CNN-LSTM architecture is trained and validated based on normalized Bouguer anomalies. In synthetic validation data, a hybrid CNN-LSTM architecture successfully recovered both basin shapes and high-frequency structural features to high extent and reveals an average error less than 10%. A comparison of the model’s accuracy with seismic-derived depths in Wadi Kharit Basin (Eastern Desert, Egypt) reveals an average error of 16%. The proposed hybrid inversion slightly outperforms the constrained Parker-Oldenburg gravity inversion technique in terms of forward gravity fit accuracy. The hybrid deep learning approach offers an alternative to conventional gravity inversion. These results illustrate the potential for deep learning-based inversion to enhance subsurface structural interpretation, especially in structurally complex or data-limited circumstances.</p>

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A hybrid CNN-LSTM deep learning approach for basement depth prediction in structurally complex sedimentary basins using gravity data

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Osama Elghrabawy

摘要

In this paper, a new deep learning-based method for inverting gravity anomaly profiles using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is presented. Synthetic gravity data were generated from 30,000 forward-modeled basin geometries with stochastic variability. With synthetic profiles, CNN-LSTM architecture is trained and validated based on normalized Bouguer anomalies. In synthetic validation data, a hybrid CNN-LSTM architecture successfully recovered both basin shapes and high-frequency structural features to high extent and reveals an average error less than 10%. A comparison of the model’s accuracy with seismic-derived depths in Wadi Kharit Basin (Eastern Desert, Egypt) reveals an average error of 16%. The proposed hybrid inversion slightly outperforms the constrained Parker-Oldenburg gravity inversion technique in terms of forward gravity fit accuracy. The hybrid deep learning approach offers an alternative to conventional gravity inversion. These results illustrate the potential for deep learning-based inversion to enhance subsurface structural interpretation, especially in structurally complex or data-limited circumstances.