SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction

Training specific deep learning models for particular tasks is common across various domains within seismology. However, this approach encounters two limitations: inadequate labeled data for certain tasks and limited generalization across regions. To address these challenges, we develop SeisCLIP, a seismology foundation model trained through contrastive learning from multi-modal data. It consists of a transformer encoder for extracting crucial features from time-frequency seismic spectrum and an MLP encoder for integrating the phase and source information of the same event. These encoders are jointly pre-trained on a vast dataset and the spectrum encoder is subsequently fine-tuned on smaller datasets for various downstream tasks. Notably, SeisCLIP's performance surpasses that of baseline methods in event classification, localization, and focal mechanism analysis tasks, employing distinct datasets from different regions. In conclusion, SeisCLIP holds significant potential as a foundational model in the field of seismology, paving the way for innovative directions in foundation-model-based seismology research.Comment: 27 pages, 9 figures, 4 table

Seismic Foundation Model (SFM): a new generation deep learning model in geophysics

While computer science has seen remarkable advancements in foundation models, which remain underexplored in geoscience. Addressing this gap, we introduce a workflow to develop geophysical foundation models, including data preparation, model pre-training, and adaption to downstream tasks. From 192 globally collected 3-D seismic volumes, we create a carefully curated dataset of 2,286,422 2-D seismic images. Fully using these unlabeled images, we employ the self-supervised learning to pre-train a Transformer-based Seismic Foundation Model (SFM) for producing all-purpose seismic features that work across various tasks and surveys. Through experiments on seismic facies classification, geobody identification, interpolation, denoising, and inversion, our pre-trained model demonstrates versatility, generalization, scalability, and superior performance over baseline models. Conclusively, we provide a foundation model and vast dataset to advance AI in geophysics, addressing challenges (poor generalization, lacking labels, and repetitive training for task-specified models) of applying AI in geophysics and paving the way for future innovations in geoscience.Comment: 27 pages, 9 figures, and 4 table

Long-term effects of straw and straw-derived biochar on soil aggregation and fungal community in a rice–wheat rotation system

Background Soil aggregation is fundamental for soil functioning and agricultural productivity. Aggregate formation depends on microbial activity influencing the production of exudates and hyphae, which in turn act as binding materials. Fungi are also important for improving soil quality and promoting plant growth in a symbiotic manner. There is a scarcity of findings comparing the long-term impacts of different yearly double-crop straw return modes (e.g., straw return to the field and straw-derived biochar return to the field) on soil aggregation and fungal community structure in rice–wheat rotation systems. Methods The effects of 6-year continuous straw and straw-derived biochar amendment on soil physicochemical properties and the fungal community were evaluated in an intensively managed crop rotation system (rice–wheat). Soil samples of different aggregates (macroaggregates, microaggregates, and silt clay) from four different fertilization regimes (control, CK; traditional inorganic fertilization, CF; straw returned to field, CS; straw-derived biochar addition, CB) were obtained, and Illumina MiSeq sequencing analysis of the fungal internal transcribed spacer gene was performed. Results Compared to CF, CS and CB enhanced soil organic carbon, total nitrogen, and aggregation in 0–20 and 20–40 cm soil, with CB exhibiting a stronger effect. Additionally, agrowaste addition increased the mean weight diameter and the geometric diameter and decreased the fractal dimension (p < 0.05). Principal coordinates analysis indicated that fertilization management affected fungal community structure and aggregation distribution. In addition, CS increased fungal community richness and diversity, compared to CK, CB decreased these aspects. Ascomycota, unclassified_k_Fungi, and Basidiomycota were the dominant phyla in all soil samples. At the genus level, CB clearly increased fungi decomposing biosolids (Articulospora in macroaggregates in 0–20 cm soil and Neurospora in macroaggregates in 20–40 cm soil); decreased pathogenic fungi (Monographella in macroaggregates and Gibberella in microaggregates in 0–20 cm soil) and CO2-emission-related fungi (Pyrenochaetopsis in microaggregates and silt clay in 0–40 cm soil) (p < 0.05). Straw and biochar with inorganic fertilizer counteracted some of the adverse effects of the inorganic fertilizer with biochar showing better effects than straw

Charge order induced Dirac pockets in the nonsymmorphic crystal TaTe4_4

The interplay between charge order (CO) and nontrivial band topology has spurred tremendous interest in understanding topological excitations beyond the single-particle description. In a quasi-one-dimensional nonsymmorphic crystal TaTe$_4$, the (2a$\times$2b$\times$3c) charge ordered ground state drives the system into a space group where the symmetry indicator features the emergence of Dirac fermions and unconventional double Dirac fermions. Using angle-resolved photoemission spectroscopy and first-principles calculations, we provide evidence of the CO induced Dirac fermion-related bands near the Fermi level. Furthermore, the band folding at the Fermi level is compatible with the new periodicity dictated by the CO, indicating that the electrons near the Fermi level follow the crystalline symmetries needed to host double Dirac fermions in this system.Comment: 9 pages, 4 figures. Second version of the manuscript following the first submission in April 202