Using provenance analysis to build an accurate source-to-sink relationship is the key to infer mountain building scenarios around the Qaidam Basin, and also important to understanding the uplift and expansion of the Tibetan Plateau. However, some conflicting provenance inferences are caused by different interpretations for the prevalent existence of the late Paleozoic to early Mesozoic age group in detrital zircon U‒Pb age spectra of the Paleogene strata at the northern Qaidam Basin, and these need to be resolved. In this article, an integrated study of sediment distribution, heavy mineral assemblages, and detrital zircon U‒Pb geochronology is carried out to analyze provenance of the Paleogene strata at the northern Qaidam Basin. The decreasing trends of the net sand to gross thickness ratios and conglomerate percentages away from the Qilian Mountains and Altyn Tagh range to basin interior clearly support they are the provenance areas. Sedimentation of materials from the Altyn Tagh range is spatially confined to a small area in front of the mountains. A large sandy body with a uniform distribution of detrital zircon ages (containing a lot of the late Paleozoic to early Mesozoic zircon ages) and heavy mineral assemblages in the Xiaganchaigou Formation is supplied by the Qilian Mountains.
Geochemical pattern recognition has long been of interest for geologists to reveal geochemical anomalies associated with mineralization. In regional-scale exploration, geochemical anomalies are derived conventionally from stream sediment samples and processed in the form of vectors, resulting in row-wise outliers. However, geochemical anomalies derived through various means of pattern recognition have shown their limits in depicting complex geochemical distributions. In this paper, we propose to utilize the Shapley value, linked to the Mahalanobis distance (MD), and cell-wise outlier detection to facilitate the recognition of anomalous geochemical indicator elements. First, by considering the compositional nature of geochemical data, multivariate outliers are detected based on the MD in isometric log-ratio coordinates. Secondly, to quantify the contributions of individual elements to the outlyingness of an outlier, Shapley values are used to express the MDs of data as outlyingness contributions of single elements. Finally, cell-wise outlier detection is introduced to examine and quantify the outlyingness of each cell in a geochemical data matrix. The outlying cells serve as criteria for further recognition of element associations. By analysing the Shapley values of individual elements and the outlying cells in a geochemical data matrix, more information contained in multivariate outliers can be recognized. Using this proposed methodology, the element associations that relate to regional mineralization in the study area were Au-only anomalies, Au–As–Sb anomalies, As–Sb–Hg anomalies and Ag-related anomalies.
Head-to-vehicle contact boundary condition and criteria and corresponding thresholds of head injuries are crucial in evaluation of vehicle safety performance for pedestrian protection, which need a constantly updated understanding of pedestrian head kinematic response and injury risk in real-world collisions. Thus, the purpose of the current study is to investigate the characteristics of pedestrian head-to-vehicle contact boundary condition and pedestrian AIS3+ (Abbreviated Injury Scale) head injury risk as functions of kinematic-based criteria, including HIC (Head Injury Criterion), HIP (Head Impact Power), GAMBIT (Generalized Acceleration Model for Brain Injury Threshold), RIC (Rotational Injury Criterion), and BrIC (Brain Injury Criteria), in real-world collisions. To achieve this, 57 vehicle-to-pedestrian collision cases were employed, and a multi-body modeling approach was applied to reconstruct pedestrian kinematics in these real-world collisions. The results show that head-to-windscreen contacts are dominant in pedestrian collisions of the analysis sample and that head WAD (Wrap Around Distance) floats from 1.5 to 2.3 m, with a mean value of 1.84 m; 80% of cases have a head linear contact velocity below 45 km/h or an angular contact velocity less than 40 rad/s; pedestrian head linear contact velocity is on average 83 ± 23% of the vehicle impact velocity, while the head angular contact velocity (in rad/s) is on average 75 ± 25% of the vehicle impact velocity in km/h; 77% of cases have a head contact time in the range 50-140 ms, and negative and positive linear correlations are observed for the relationships between pedestrian head contact time and WAD/height ratio and vehicle impact velocity, respectively; 70% of cases have a head contact angle floating from 40° to 70°, with an average value of 53°; the pedestrian head contact angles on windscreens (average = 48°) are significantly lower than those on bonnets (average = 60°); the predicted thresholds of HIC, HIP, GAMBIT, RIC, BrIC2011, and BrIC2013 for a 50% probability of AIS3+ head injury risk are 1,300, 60 kW, 0.74, 1,470 × 10
Machine learning (ML) has shown its effectiveness in handling multi-geoinformation. Yet, the black-box nature of ML algorithms has restricted their widespread adoption in the domain of mineral prospectivity mapping (MPM). In this paper, methods for interpreting ML model predictions are introduced to aid ML-based MPM, with the goal of extracting richer insights from the ML modeling of an exploration geochemical dataset. The partial dependence plot (PDP) and accumulated local effect (ALE) plot, along with the SHAP value analysis, were utilized to demonstrate the application of random forest (RF) modeling within both regression and classification frameworks. Initially, the random forest regression (RFR) model established the relationship between the concentrations of Au and those of elements such as As, Sb, and Hg in the study area, and from this model, the most important geochemical elements and their quantitative relationships with Au were revealed by their contributions in the modeling through PDP and ALE analyses. Secondly, the RF classification modeling established the relationships of mineralization occurrences (i.e., known mineral deposits) with geochemical elements (i.e., Au, As, Sb, Hg, Cu, Pb, Zn, and Ag), as did RFR modeling. The most important geochemical elements for indicating regional Au mineralization and the trajectories of PDP and ALE reached a consensus that As and Sb contributed the most, both in the regression and classification modeling, with regard to Au mineralization. Finally, the SHAP values illustrated the behavior of the training samples (i.e., known mineral deposits) in RF modeling, and the resulting prospectivity map was evaluated using receiver operating characteristics.
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