Igneous rocks are widely developed in various Mesozoic and Cenozoic continental and marine basins. Igneous reservoirs are the key reservoirs for current oil and gas development. Accurate prediction of lithology and lithofacies is a prerequisite for the effective exploration of igneous reservoirs. Igneous lithology and lithofacies are complex and correlated. The existing single-label igneous rock identification methods only consider the prediction of individual properties, and less consideration is given to the correlation of reservoir properties. Therefore, lithology and lithofacies prediction based on conventional logging data is regarded as a typical class-imbalanced multilabel classification problem when considering both attribute correlation in algorithms and evaluation metrics. To solve this problem, an ensemble method of data optimization combined with multigrained cascade forest (CF) is used in this study to develop a new multilabel lithology and lithofacies prediction model based on data from nine conventional logs in the eastern depressional reservoirs of the Liaohe Basin, and satisfactory results are obtained. The imbalance problem of the conventional logging data sets is first solved by using K-means and synthetic minority oversampling technique methods; then, the model is trained by scenario transformation and stripping with multigrained CF; and next, a multilabel classification evaluation index with multiple perspectives is introduced. The differences between the model and typical intelligent algorithms such as CF, adaptive boosting, random forest, and support vector machine are compared in simulated wells, and the new model is found to have obvious advantages. The model is finally applied to an actual well, and the accurate prediction results illustrate that the new model designed and trained for the class imbalance multilabel classification problem in this paper has application value in the lithology and lithofacies multilabel prediction of igneous rocks and also provides a theoretical basis for more complex multilabel reservoir evaluation using machine learning in the future.

Applying the staggered grid finite difference method (SGFDM) for simulating acoustic responses in large-scale, complex, 3-D models poses substantial challenges in geophysics, especially in high-resolution stratigraphic model, due to the high computational burden. To address this problem, we have proposed a low overhead parallel optimization method (LOPOM) suitable for heterogeneous architectures, using high-resolution borehole models as examples. The LOPOM enhances computational intensity and optimizes memory bandwidth utilization. This is achieved through the technique of data reuse along the discontinuities of the model and by minimizing such discontinuities within the halo region. LOPOM was implemented and tested on the CUDA platform and the Sunway supercomputer. In each instance, LOPOM demonstrated an optimal acceleration ratio, thus proving its robust performance. Furthermore, the method’s effectiveness was confirmed through its application to fracture-vuggy formation models and digital core models. Finally, the numerical simulation results and the actual logging data are combined to illustrate the application value of LOPOM.

The inversion of the relative content and spatial distribution characteristics of radioactive elements on the lunar surface, as inferred from gamma-ray spectrometer data, holds substantial importance for forecasting lunar surface compositions. During the inversion process of gamma-ray spectrometer data, galactic cosmic rays (GCRs) have engendered disruptive “stripe noise” in the distribution map of radioactive elements. This phenomenon significantly hampers the interpretation of data and the extraction of lunar surface information. The proposed approach adeptly separates the influence of GCR from the counting rate distribution map of the lunar surface by employing the empirical mode decomposition method. It achieves the deduction of GCR background from the Chang’e-2 gamma-ray spectrometer data with precision. Compared to conventional GCR background deduction methods employed by predecessors, this model does not need to process a large amount of original data repeatedly. Moreover, it achieves an accurate deduction of the GCR background without intricate formulaic derivations. The procedural simplicity and reduced time investment make this approach significantly superior.