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The recently developed Frequency-Bessel (F-J) transform array technique has significantly advanced multi-mode surface wave tomography, providing precise constraints on shear wave velocity (VS) models of the crust and uppermost mantle. This technique has primarily focused on extracting inter-station surface waves from ambient noise cross-correlation. Energetic typhoons generate abundant surface wave signals, prompting growing interest in utilizing these signals to investigate subsurface structures. This study explores the feasibility and application of using microseisms excited by strong typhoons for F-J transform surface wave tomography. By analyzing microseisms recorded by a broadband seismic array in Mongolia during energetic typhoons, we observed multi-mode surface waves with high signal-to-noise ratios. Our analysis revealed that the sources of these waves closely correspond to typhoon tracks, confirming that they were primarily excited by typhoons. We successfully extracted multi-mode dispersion curves for imaging the VSstructure beneath the array using the F-J transform. Additionally, we derived multi-mode dispersion curves from a two-year noise dataset recorded by the same array, which were also used to invert for the VSstructure. Comparison of VSmodels derived from typhoon-induced microseisms and the two-year noise dataset showed negligible differences within the depth range of 0–140 km. Our findings demonstrate the potential value of microseisms generated by energetic typhoons in precisely probing the VSstructure of the crust and uppermost mantle.

Accurate delineation of plant cell organelles from electron microscope images is essential for understanding subcellular behaviour and function. Here we develop a deep-learning pipeline, called the organelle segmentation network (OrgSegNet), for pixel-wise segmentation to identify chloroplasts, mitochondria, nuclei and vacuoles. OrgSegNet was evaluated on a large manually annotated dataset collected from 19 plant species and achieved state-of-the-art segmentation performance. We defined three digital traits (shape complexity, electron density and cross-sectional area) to track the quantitative features of individual organelles in 2D images and released an open-source web tool called Plantorganelle Hunter for quantitatively profiling subcellular morphology. In addition, the automatic segmentation method was successfully applied to a serial-sectioning scanning microscope technique to create a 3D cell model that offers unique views of the morphology and distribution of these organelles. The functionalities of Plantorganelle Hunter can be easily operated, which will increase efficiency and productivity for the plant science community, and enhance understanding of subcellular biology.

Ambient seismic noise tomography has been applied to probe the Earth's structure. To accurately map geological structures, a considerable amount of time is required to pick fundamental and higher modes of dispersion curves. We used frequency‐Bessel (F‐J) transform to calculate phase velocity‐frequency diagrams to obtain more higher modes of dispersion curves from the cross‐correlation function. Several studies have recently focused on picking dispersions automatically using deep learning to reduce time consumption. However, the generalization of neural networks has a degradation for untrained diagrams to some degree. Here, based on domain adaptation in computer vision, we used gamma transform to change the image contrast of the phase velocity‐frequency diagrams, rendering the test data closer to the training data. Introducing domain adaptation into the dispersion region extraction effectively improves the generalization of the neural network. Here, the dispersion regions are the regions in the phase velocity‐frequency diagram located around the dispersion curves where the energy is above a given threshold. We validated our method by using phase velocity‐frequency diagrams from different areas. We used one synthetic phase velocity‐frequency diagram and three phase velocity‐frequency diagrams of different areas to test domain adaptation. In particular, we tested one phase velocity‐frequency diagram that belongs to the same area for the training diagram, except for the processing steps. The results showed that our domain adaptation method successfully enhanced the generalization of the neural network. After domain adaptation, our trained network could effectively extract more higher modes of dispersion regions than before. Our dispersion curves picking method combined with domain adaptation can pick sufficient dispersion information for numerous phase velocity‐frequency diagrams. Furthermore, our method can facilitate the study of ambient noise tomography and illumination of the Earth's interiors. Our research provides a strategy for enhancing the generalization of neural networks for other deep learning‐based geophysical image segmentation tasks. Seismic surface wave has been used to probe the structure of our planet at regional and global scales. To pick dispersion curves efficiently and quickly, several researchers have attempted to utilize neural networks for the automatic picking of dispersion curves. However, in the automatic dispersion picking method, an inadaptation phenomenon occurs for different types of data that originate from different geological areas or have been processed by different manual processing methods. We used gamma transform to conduct image transformation in an attempt to alleviate inadaptation for different types of data. We verified the results of our domain adaptation with several samples. Changing the image contrast (i.e., amplitude of image) via gamma transform, we improved the performance of the trained neural network and used it to artificially control the quantity to which regions are extracted. Our method enabled solving the inadaptation problem. The extraction ability of neural network using domain adaptation was enhanced significantly and verified by untrained phase velocity‐frequency diagramsThe modified gamma transform effectively enhanced the extraction of dispersion regions and simultaneously suppressed the noiseOur results validate that the extent to which dispersion regions are extracted could be controlled by a change in gray‐level distribution The extraction ability of neural network using domain adaptation was enhanced significantly and verified by untrained phase velocity‐frequency diagrams The modified gamma transform effectively enhanced the extraction of dispersion regions and simultaneously suppressed the noise Our results validate that the extent to which dispersion regions are extracted could be controlled by a change in gray‐level distribution

Ambient seismic noise cross‐correlation has been widely applied in surface wave tomography at regional to global scales, including for seismic exploration of near‐surface structures. Reliable seismic imaging requires the accurate selection of dispersion curves. However, manual picking has become cumbersome work with the increase in available correlation traces; it is even more difficult when the number of dispersion curves increases by using frequency‐Bessel (F‐J) transform. Here, we show that the neural network Res‐Unet++ can automatically and accurately extract both fundamental dispersion curves and overtones from the F‐J dispersion spectra after training the network. Results show that selected dispersion curves had high accuracies in the synthetic data (greater than 95%). The network could effectively extract both the fundamental and higher modes in real data, and transfer learning improved the adaptability of neural networks for different geological areas. The obtained dispersion curves from the real data agreed well with those acquired manually and were advantageous for generating more effective dispersion points. Seismic surface wave has been widely used in investigating structure of our planet. When it propagates in the underground structure, the velocity of seismic surface wave changes with frequency. To accurately investigate structure of our planet, we need to get the relation between velocity and frequency, and this relation is expressed as dispersion curves, which is recorded in an image. We usually obtain this relation manually, but it is labor‐intensive and time‐consuming. To save time and energy, we use computer to automatically obtain the relation between velocity and frequency. After the processing based on computers' learning, we can obtain the velocities at different frequencies in a short time. The obtained results for synthetic data and real data proved to be reliable, and the accuracies are relatively high, which can help us investigate the structure of our planet. The automatic method can apply to more data from different areas by keeping the computer learning some new data. The fully convolutional network Res‐Unet++ is applicable to effectively extract multimode dispersions from dispersion spectraThe automatically and manually picked dispersions have significant similaritiesUsing transfer learning has alleviated effectively the inadaptation problem in spectra from different areas The fully convolutional network Res‐Unet++ is applicable to effectively extract multimode dispersions from dispersion spectra The automatically and manually picked dispersions have significant similarities Using transfer learning has alleviated effectively the inadaptation problem in spectra from different areas

Spirulina platensis can synthesize a large amount of phycocyanin, which had been developed as a health food. At the same time, Spirulina can absorb the nitrogen and phosphorus in wastewater and provide for its own growth. Here, we studied the optimal nitrogen and phosphorus supply for the Spirulina production process. For the first time, 405 nm portable Raman spectrometer was used to estimate phycocyanin content for real-time industrial applications. We obtained three Raman characteristic peaks of phycocyanin through density functional theory combined with home-built Raman spectrometer, which were 1272, 1337, and 1432cm−1. There was a good linear correlation between the sum of the three peak intensities and the PCL concentration (y = 18.887x + 833.530, R2=0.890). The least squares support vector machine model based on the characteristic peaks was used to estimate the concentration of phycocyanin and obtained good results with a correlation coefficient of prediction of 0.907 and residual predictive deviation of 3.357. The results can provide decision-making for integration of Spirulina effluent treatment and phycocyanin production and provide references for real-time Spirulina-based biorefinery applications.