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Provenance of uppermost Carboniferous–Lower Triassic sandstones, Bogda Mountains, NW China: implication on late Paleozoic tectonic history of southern Central Asian Orogenic Belt The Permian-Triassic time is a critical stage in the Paleozoic continental amalgamation and Cenozoic orogenic reactivation of southern Central Asian Orogenic Belt (CAOB). Field, petrographic and detrital zircon U-Pb geochronological data of the uppermost Carboniferous– Lower Triassic sandstones from 3 sections in Bogda Mountains, greater Turpan-Junggar basin, NW China, are used to decipher the tectonic history. They are Tarlong- Taodonggou (TT) and Zhaobishan (ZBS) in the south and Dalongkou (DLK) in the north, 100 km apart and ~7,000 m in total thickness. Four petrofacies of 229 sandstones are defined using the abundance of volcanic, sedimentary, and metamorphic (with polycrystalline quartz) lithics. Petrofacies A (Lv73Ls21(Qp+Lm)6) contains mainly volcanic lithics, indicating a volcanic arc as the main source. Petrofacies B (Lv14Ls41(Qp+Lm)45) and Petrofacies C (Lv38Ls14(Qp+Lm)48) contain mixed sedimentary, metamorphic, and volcanic lithics, indicating multiple sources. Petrofacies D (Lv11Ls82(Qp+Lm)7) contains mainly sedimentary lithics with a trace amount of volcanic and metamorphic lithics, indicating local rift-shoulder sedimentary sources. Additionally, the U-Pb dates of 3505 detrital zircon grains of 35 sandstones were analyzed. The predominant Paleozoic zircon grains yield major age populations at ca. 360–280 Ma and 485–385 Ma. Precambrian dates are present, ranging from 542 Ma to 3329 Ma. During Gzhelian–Asselian, andesite and basalt are the major source lithologies in TT. Zircon ages peak at ~300 Ma. During Sakmarian–Kungurian, basalt and andesite are the main source rocks in TT and ZBS; and zircon ages of both areas peak at ~300 Ma. The Roadian–Wordian is probably represented by a regional unconformity. The Guadalupian source lithology and zircon date show a major change. Andesite is the common and rhyolite and basalt minor source lithologies for TT and DLK; but rhyolite significant for ZBS. A unimodal peak at ~305 Ma occurs in TT; two peaks at 305 and 455 Ma with common Precambrian dates in ZBS; and peaks of 310–295 Ma in DLK. During Wuchiapingian–mid Olenekian, andesite and rhyolite are the common source lithologies for TT and DLK, and rhyolite as the primary volcanic lithology for ZBS. In TT, Wuchiapingian-Induan samples have a major age peak at ~300 Ma, and an Olenekian sample has two peaks at ~300 and ~450 Ma. In ZBS, the age pattern is similar to that of the Guadalupian sample. In DLK, samples have a major age peak at ~310 Ma and a minor peak at ~450 Ma. The comparable age clusters identified by multi-dimensional scaling indicate that North Tianshan is the source for TT and ZBS during the latest Carboniferous–early Permian. But south Central Tianshan became the main source solely to ZBS. During late Permian–Early Triassic, both north and central Tianshan became the common sources to the three areas due to enhanced denudation. The source change in mid-Permian across a regional unconformity is synchronous with Paleo-Asian Ocean closure and arc-continent and continent-continent collisions, which occurred no later than Guadalupian. 

Abstract Linear magnetic anomalies (LMA), resulting from Earth's magnetic field reversals recorded by seafloor spreading serve as crucial evidence for oceanic crust formation and plate tectonics. Traditionally, LMA analysis relies on visual inspection and manual interpretation, which can be subject to biases due to the complexities of the tectonic history, uneven data coverage, and strong local anomalies associated with seamounts and fracture zones. In this study, we present a Machine learning (ML)‐based framework to identify LMA, determine their orientations and distinguish spatial patterns across oceans. The framework consists of three stages and is semi‐automated, scalable and unbiased. First, a generation network produces artificial yet realistic magnetic anomalies based on user‐specified conditions of linearity and orientation, addressing the scarcity of the labeled training dataset for supervised ML approaches. Second, a characterization network is trained on these generated magnetic anomalies to identify LMA and their orientations. Third, the detected LMA features are clustered into groups based on predicted orientations, revealing underlying spatial patterns, which are directly related to propagating ridges and tectonic activity. The application of this framework to magnetic data from seven areas in the Atlantic and Pacific oceans aligns well with established magnetic lineations and geological features, such as the Mid‐Atlantic Ridge, Reykjanes Ridge, Galapagos Spreading Center, Shatsky Rise, Juan de Fuca Ridge and even Easter Microplate and Galapagos hotspot. The proposed framework establishes a solid foundation for future data‐driven marine magnetic analyses and facilitates objective and quantitative geological interpretation, thus offering the potential to enhance our understanding of oceanic crust formation.