The unmanned aerial vehicle (UAV), as a remote sensing platform, has attracted many researchers in precision agriculture because of its operational flexibility and capability of producing high spatial and temporal resolution images of agricultural fields. This study proposed machine learning (ML) models and their ensembles for peanut yield prediction using UAV multispectral data. We utilized five bands (red, green, blue, near-infra-red (NIR) and red-edge) multispectral images acquired at various growth stages of peanuts using UAV. The correlation between spectral bands and yield was analyzed for each growth stage, which showed that the maturity stages had a significant correlation between peanut yield and spectral bands: red, green, NIR and red edge (REDE). Using these four bands spectral data, we assessed the potential for peanut yield prediction using multiple linear regression and seven non-linear ML models whose hyperparameters were optimized using simulated annealing (SA). The best three ML models, random forest (RF), support vector machine (SVM) and XGBoost, were then selected to construct a cooperative yield prediction framework with both the best ML model and the ensemble scheme from the best three as comparable recommendations to the farmers.

Based on the tutorial cases accumulated in the past several years, by reclassifying the arithmetic functions (A) in LIATE into the polynomial function (P), the standard power function (Po), and the integer power function (nPo), a new guide, comprising three sub-guides, Pet, LoPo and InPo, or Pet-LoPo-InPo, is summarized in this note to guide practicing integration by parts. This new guide removes many incompatible combinations included in LIATE, rationalizes the relationship between the exponential and trigonometric functions in LIATE, and expands the coverage of the P-functions beyond the traditional definitions. Hence, the new guide can reduce potential confusions that students may experience in using LIATE for their practices of integration by parts. The advantages of this new guide are demonstrated by many worked examples in this note.

The genesis models of the iron-ores hosted in banded iron formations (BIFs) in the Hamersley Province of Western Australia have been debated since the iron-ore deposits were discovered in the 1960s. The existing models considered the few physicochemical conditions for the iron-ore enrichment from BIFs. This study incorporates the latest research outcomes in conversions among the major magnetic minerals under different physicochemical conditions with the thermal magnetic analysis for BIFs and iron-ores collected from the Hamersley Province to fill the gap in knowledge highlighted by existing studies of the iron ores and BIFs. The results indicate that the high-grade hematite ores might have been undergone a physicochemical process under hydrothermal conditions between 120 ℃ and 220 ℃ during the major stage of enrichment from the original BIFs in the Brockman Iron Formation. Such physicochemical conditions would require either that the BIF units were buried 4000–5000 m underground with tilted broad channels formed by large-scale deformation in the region that facilitates hydrothermal reactions and leaching by the fluids flowing down deep to 4000–5000 m, somehow similar to the deep-seated supergene model proposed in previous works, or that the BIF units were still buried but the hydrothermal fluids coming up from deeper sources spread widely over the broad channels to ensure the high-grade hematite ores are consistently uniform over the entire deposit. The large-scale martite-goethite deposits in the Marra Mamba Iron Formation might be derived from multiple supergene phases from hematite-martite ores below 100 ℃ in the natural process of oxidization near surface, somewhat similar to the existing model for the channel iron deposits. Magnetite contained within current BIFs and iron ores was least likely derived from primary hematite in BIFs.

The Hamersley Province in the northwest of Western Australia contains extensive banded iron formations (BIFs) and large hematite-goethite deposits. Density information of rocks and ores in this region has been scarce. This study reports the results of a systematic density investigations based on more than eight hundred density datasets in the province. This study not only provides a better understanding of density distribution of the rocks and ores in the province, but also allows forward gravity modeling over the known iron-ore deposits to be conducted for exploring the usefulness and effectiveness of gravity surveys for detecting concealed iron-ore deposits in the region. This should have a significant impact on iron-ore mining in the province as the outcropped ores have been mined for over 40 years in the province and the future targets are likely the concealed deposits below the surface. The analysis shows a clear density contrast around 1.0 g/cm³ between the Brockman iron ores and the host BIFs, which should generate clear positive net gravity anomalies over buried large iron-ore deposits. However, porous goethite ores hosted in the Marra Mamba BIFs have an average density of about 2.8 g/cm³ due to porosity about 30–40% in the ores. A density contrast of −0.5 g/cm³ may exist between the goethite ores and BIFs, which would produce net negative gravity anomalies over the deposits. Since most goethite deposits are layered consistently with the host rocks and associated with broad folds, the net gravity anomaly of an orebody itself may generally have the similar shape to the corresponding BIF bedrock. This implies that gravity surveys may be able to detect paleochannels which host the goethite ores, rather than directly detecting the orebody.

Correlations between magnetic susceptibility and contents of magnetic minerals in rocks are important in interpreting magnetic anomalies in geophysical exploration and understanding magnetic behaviors of rocks in rock magnetism studies. Previous studies were focused on describing such correlations using a sole expression or a set of expressions through statistical analysis. In this paper, we use neural network techniques to approximate the nonlinear relations between susceptibility and magnetite and/or hematite contents in rocks. This is the first time that neural networks are used for such study in rock magnetism and magnetic petrophysics. Three multilayer perceptrons are trained for producing the best possible estimation on susceptibility based on magnetic contents. These trained models are capable of producing accurate mappings between susceptibility and magnetite and/or hematite contents in rocks. This approach opens a new way of quantitative simulation using neural networks in rock magnetism and petrophysical research and applications.