Summary: Switchgrass (Panicum virgatum) is a bioenergy model crop valued for its energy efficiency and drought tolerance. The related monocot species rice (Oryza sativa) and maize (Zea mays) deploy species‐specific, specialized metabolites as core stress defenses. By contrast, specialized chemical defenses in switchgrass are largely unknown. To investigate specialized metabolic drought responses in switchgrass, we integrated tissue‐specific transcriptome and metabolite analyses of the genotypes Alamo and Cave‐in‐Rock that feature different drought tolerance. The more drought‐susceptible Cave‐in‐Rock featured an earlier onset of transcriptomic changes and significantly more differentially expressed genes in response to drought compared to Alamo. Specialized pathways showed moderate differential expression compared to pronounced transcriptomic alterations in carbohydrate and amino acid metabolism. However, diterpenoid‐biosynthetic genes showed drought‐inducible expression in Alamo roots, contrasting largely unaltered triterpenoid and phenylpropanoid pathways. Metabolomic analyses identified common and genotype‐specific flavonoids and terpenoids. Consistent with transcriptomic alterations, several root diterpenoids showed significant drought‐induced accumulation, whereas triterpenoid abundance remained predominantly unchanged. Structural analysis verified select drought‐responsive diterpenoids as oxygenated furanoditerpenoids. Drought‐dependent transcriptome and metabolite profiles provide the foundation to understand the molecular mechanisms underlying switchgrass drought responses. Accumulation of specialized root diterpenoids and corresponding pathway transcripts supports a role in drought stress tolerance.

Aeromagnetic exploration is a magnetic exploration method that detects changes of the earth’s magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magnetometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays an increasingly important role in mineral exploration and other fields due to its advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude, magnetic field interference will occur and affect the measurement of the geomagnetic field by the magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic interference and improve the magnetic measurement accuracy of the magnetometer. This paper focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy of UAV based aeromagnetic data measurement. Based on the Tolles–Lawson model, a numerical simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out, and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN, the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has higher compensation accuracy and stronger generalization ability.

Gravity inversion can be used to obtain the spatial structure and physical properties of subsurface anomalies through gravity observation data. With the continuous development of machine learning, geophysical inversion methods based on deep learning have achieved good results. Geophysical inversion methods based on deep learning often employ large-scale data sets to obtain inversion networks with strong generalization. They are widely used but face a problem of lacking information constraints. Therefore, a self-constrained network is proposed to optimize the inversion results, composed of two networks with similar structures but different functions. At the same time, a fine-tuning strategy is also introduced. On the basis of data-driven deep learning, we further optimized the results by controlling the self-constrained network and optimizing fine-tuning strategy. The results of model testing show that the method proposed in this study can effectively improve inversion precision and obtain more reliable and accurate inversion results. Finally, the method is applied to the field data of Gonghe Basin, Qinghai Province, and the 3D inversion results are used to effectively delineate the geothermal storage area.

Aeromagnetic measurement plays an important role in mineral exploration, but unmanned aerial vehicles generate maneuvering noise during aerial flight, which negatively impacts the accuracy of aeromagnetic measurement data. Therefore, aeromagnetic compensation is an indispensable step in aeromagnetic data processing. The multicollinearity of variables in the aeromagnetic compensation model based on linear regression affects its accuracy, resulting in a large difference in the compensation effect of the same group of compensation coefficients in different directions. In order to obtain high-quality aeromagnetic data, this study proposes an adaptive model-based method for suppressing aeromagnetic maneuvering noise. First, due to the fact that the variables that cause multiple collinearity in the compensation model are related to the flight heading, the model variables are adaptively assigned to each heading based on the characteristics of the variable data for different headings. The compensation model is optimized and improved, and the impact of multiple collinearity is thus suppressed. In adaptive modeling, variables with greater significance and smaller multicollinearity are automatically allocated to build the optimal heading model, and then high-precision compensation coefficients are obtained. This algorithm was applied to the data collected by a certain unmanned aerial vehicle aeromagnetic measurement platform in Ma’anshan and compared with traditional methods. The experimental results show that the adaptive modeling-based aeromagnetic compensation algorithm is superior to traditional algorithms, with fewer errors and a higher improvement ratio. Hence, the method can effectively solve the ill-conditioned problem of a model affected by multicollinearity and further improve its compensation accuracy and robustness. Moreover, the feasibility and value of this algorithm were verified in actual mineral resource detection.

Aeromagnetic compensation is a crucial step in the processing of aeromagnetic data. The aeromagnetic compensation method based on the linear regression model has poorer fitting capacity than the neural network aeromagnetic compensation algorithm. The existing gradient updating neural network-based aeromagnetic compensation algorithm is subject to the problem that the gradient disappears during the backpropagation process, resulting in poor fitting ability and affecting aeromagnetic compensation accuracy. In this paper, we propose a neural network compensation algorithm with strong fitting ability: residual backpropagation neural network (Res-bp). The algorithm realizes the cross-layer propagation of the gradient through a residual connection so that the network not only preserves the original information but also acquires additional information during training, successfully solving the problem of gradient disappearance and boosting the network’s fitting capacity. The algorithm is applied to the data collected by unmanned aerial vehicles (UAVs) to verify its effectiveness. The results show that the improvement ratio is improved compared with the traditional neural network, demonstrating that the algorithm has a significant compensation effect on aeromagnetic interference and improves the quality of aeromagnetic data.