2results about How to "Strong non-linear mapping ability" patented technology

This invention discloses a groundwater model parameter estimation method combining deep learning and local set updating, comprising: constructing a groundwater numerical model; constructing a prior parameter sample set; obtaining the corresponding model output set; acquiring observation data; determining the number and proportion of local sets; randomly selecting local center samples and calculating similarity; forming a local prior sample set; updating all local prior sample sets one by one to a local posterior sample set; randomly selecting posterior samples to obtain the overall posterior sample set; replacing the prior sample set with the posterior sample set, and iteratively outputting the parameter estimation results. By combining a deep learning update mechanism with a local set update strategy, this invention effectively addresses the challenges of high-dimensionality, strong nonlinearity, non-Gaussian parameter distribution, and the "different parameters, same effect" problem in groundwater model parameter estimation, significantly improving the computational efficiency and accuracy of data assimilation algorithms in complex groundwater system parameter estimation tasks.

This invention relates to the field of oil and gas reservoir exploration technology, specifically a method, apparatus, electronic device, and storage medium for predicting the content of multiple minerals. The method includes acquiring input features of the well section to be predicted; inputting these features into a multi-component mineral content prediction model; and outputting the corresponding prediction results for the content of each mineral component. The multi-component mineral content prediction model is trained using multiple samples on a pre-set deep learning model, which employs a strategy of bidirectional multi-scale feature extraction and cross-scale attention fusion. The model constructed by this invention can not only efficiently capture the global long-term trend and local abrupt fluctuations of logging curves, but also automatically learn the mutual constraints between minerals, achieving accurate mapping between multi-scale logging features and specific mineral categories. Therefore, the multi-component mineral content prediction model enables efficient and collaborative prediction of multiple minerals, providing an effective data foundation for oil and gas exploration and development, reservoir evaluation, and production capacity prediction.