| Gravity exploration is a passive source geophysical method with poorer depth resolution compared to other geophysical methods.In addition,source field parameter inversion often encounters problems such as low inversion accuracy and poor depth resolution.To address these issues,this study utilized artificial intelligence algorithms for gravity data source parameter inversion.By establishing a series of inversion algorithms based on deep learning of the horizontal boundary position,physical property parameters,and interface undulation parameters of geological bodies,the reliability and accuracy of gravity data inversion results were improved.Furthermore,the applicability of deep learning technology in gravity data inversion was verified by using Gonghe Basin in Qinghai province as a demonstration area for method application.Through quantitative analysis of the characteristics of the cover,thermal reservoir,heat source,and heat control channel in this area using gravity data,the geothermal genesis mode in the Republic Basin was also analyzed.These results provide theoretical support for geothermal exploration and development in this region,with the potential to have a positive impact on resource development and utilization in the area.A deep learning-based method was developed for the inversion of the horizontal boundary location of gravity data sources to address the weak recognition ability of existing boundary identification methods for deep weak anomalies and the problem of false boundaries under complex positive-negative superimposed anomalies.In designing the deep learning model,the total horizontal derivative of the gravity anomaly(Thd)was used as input to improve the deep learning network’s ability to recognize deep weak anomalies,considering the characteristics of the convolutional neural network structure.During the data preprocessing process,the Thd conversion of the gravity anomaly was transformed into regular matrix data and input into the convolutional neural network.The feature values of the gravity anomaly were learned through the constructed convolutional neural network,and the trained network model was saved for geological body boundary location delineation.To analyze the method’s effectiveness,the deep learning boundary recognition method was tested using adjacent stacking models,deep-shallow stacking models,and noisy combination models compared to traditional Thd and Theta graph boundary recognition methods,demonstrating the method’s effectiveness.The results show that the deep learning-based boundary recognition method has higher accuracy and stability than traditional methods,especially in dealing with deep weak anomalies and complex positive-negative superimposed anomalies.Therefore,this method can effectively improve the accuracy and reliability of gravity data source horizontal boundary location inversion,providing new ideas and means for geological exploration and resource development.A deep learning-based physical property inversion method was developed by introducing a constraint term that characterizes the fitting degree of gravity anomalies and a depth weighting function into the construction of the deep learning network model.This method solves the problems of low depth resolution and poor data fitting in traditional deep learning physical property inversion methods and improves the accuracy and depth resolution of inversion.Firstly,by analyzing the traditional physical property forward and inverse theory algorithms based on the Tikhonov regularization,the role of the depth weighting function and data fitting term in traditional physical property inversion is demonstrated,providing a theoretical basis for the establishment and comparative advantages and disadvantages of deep learning physical property inversion methods.Secondly,based on the gravity data forward theory and using the random walk method,a large-scale data set is established to provide more complete training data for the deep learning network model.Finally,in the construction of the network model,by improving the traditional loss function and constraining the fitting error of the forward data and the depth weighting function in the existing data-driven deep learning training process,the accuracy and depth resolution of the inversion are improved.The application effect of the method is demonstrated through various synthetic data tests.A gravity data density interface inversion method based on the deep learning U-net network is proposed.Firstly,a dataset of underground undulating interfaces is formed by randomly extracting and combining the ellipsoidal interface model,and the gravity anomaly forward calculation is performed on the interface dataset based on the Parker forward theory,providing a feature-complete data source for the deep learning network model training.Secondly,a deep learning interface inversion algorithm based on the Unet network model is designed,adding smoothness loss and overfitting suppression terms to the traditional loss function to improve the smoothness and convergence efficiency of the gravity interface inversion results.Thirdly,the generalization of the deep learning network model is validated by inverting prediction on the test sample set.Finally,by comparing with the Parker-Oldenburg interface forward and inversion method and analyzing the theoretical model data experimentally,the effectiveness and practicality of the proposed method in density interface inversion are verified.The Gonghe Basin in Qinghai Province has a relatively abundant reserve of geothermal resources.However,China’s development and utilization of these resources remain at a low level,with research on dry hot rock geothermal resources starting relatively late and facing many difficulties.Although some progress has been made in studying thermal storage in the Gonghe area,there are still issues with uncertainty in the location of dry hot rock target areas or sweet spots and unclear geothermal genesis models.This study used different scales of gravity data from the Gonghe Basin to quantitatively invert geothermal resources through a deep learning inversion method.Specifically,a deep learning gravity data parameter inversion method was used to analyze measured gravity data from the Gonghe Basin,including fault identification,three-dimensional density spatial distribution inversion,and deep Moho depth inversion,to obtain parameters related to the dry hot rock system,such as cap rock,reservoir,heat source,and heat control channel parameters.Additionally,a geothermal genesis model was established for the Gonghe Basin,providing technical support for further exploration of dry hot rock resources in the region. |
