A Physics-guided Generative AI Toolkit for Geophysical Monitoring

Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface. It utilizes the seismic wave to image the subsurface velocity map. As the machine learning (ML) technique evolves, the data-driven approaches using ML for FWI tasks have emerged, offering enhanced accuracy and reduced computational cost compared to traditional physics-based methods. However, a common challenge in geoscience, the unprivileged data, severely limits ML effectiveness. The issue becomes even worse during model pruning, a step essential in geoscience due to environmental complexities. To tackle this, we introduce the EdGeo toolkit, which employs a diffusion-based model guided by physics principles to generate high-fidelity velocity maps. The toolkit uses the acoustic wave equation to generate corresponding seismic waveform data, facilitating the fine-tuning of pruned ML models. Our results demonstrate significant improvements in SSIM scores and reduction in both MAE and MSE across various pruning ratios. Notably, the ML model fine-tuned using data generated by EdGeo yields superior quality of velocity maps, especially in representing unprivileged features, outperforming other existing methods

SDFL-FC: Semi-supervised Deep Feature Learning with Feature Consistency for Hyperspectral Image Classification

Semisupervised deep learning methods (DLMs) can mitigate the dependence on large amounts of labeled samples using a small number of labeled samples. However, for semisupervised deep feature learning (SDFL), the quality of extracted features cannot be well ensured without a certain amount of labeled samples. To address this issue, we develop the SDFL method with feature consistency (SDFL-FC) for the hyperspectral image (HSI) classification. The SDFL-FC first adopts the convolutional neural network (CNN) to extract spectral-spatial features of HSI and then uses the fully connected layers (FCLs) to model the feature consistency. Moreover, two constraints that enforce both the feature consistency of single pixel (FCS) and feature consistency of group pixels (FCG) are introduced to obtain the representative and discriminative features. The FCS is achieved by the generative adversarial network (GAN) regularization, which can reconstruct the original data from extracted features. The FCG is based on the assumption that the features of group pixels should have similar characteristics within a superpixel, which is embedded in each FCL. The final FCL outputs the class labels, and the cross-entropy (CE) loss is calculated with the labeled samples, while the two losses of FCS and FCG are calculated with all the training samples (both labeled and unlabeled). SDFL-FC integrates the FCS, FCG, and CE loss into a unified objective function and uses a customized iterative optimization algorithm to optimize it. Experiments demonstrate that the SDFL-FC can outperform the related state-of-the-art HSI classification methods

Changes in the three-dimensional molecular structure of coal during methane adsorption induced swelling

Methane (CH4) adsorption-induced swelling is one of the critical factors controlling the permeability of coalbed methane (CBM). CH4 adsorption alters the molecular structure of coal so as to induce coal swelling, and many uncertainties still exist in the process. In this study, the change in the molecular structures of different chemical structures by CH4 adsorption was investigated using the Grand Canonical Monte Carlo method to simulate the alteration of bond lengths and bond angles during swelling. The results demonstrate that the alteration of chemical structure is more extensive than a chemical bond, which is the critical factor causing the swelling behavior. Owing to the complex molecular structure of coal, among the different types of chemical structures, the C-O-C (-O-) chemical structure showed the most significant change in bond angle, with the largest degree of change is 12.89%. Compared with other chemical structures, the C-C-C (aromatic -C-) chemical structures are more stable and the largest degree of change is 0.65%. For the different types of chemical bonds, the C-C chemical bonds showed the most significant change in bond lengths, with the largest degree of change is 2.94%. And the O-H chemical bond showed the smallest change, with the largest degree of change is 0.79%. Considering the structure evolution of coal, the C-O-C (-O-) chemical structure decreases with increasing maturity and changes to the greatest after the adsorption of methane. The aromatic structure increases and the degree of deformation decreases, which is consistent with the previous experimental values for swelling. These results reveal the details of different types of chemical group deformation, providing a molecular-level insight into adsorption swelling and permeability changes

Biodegradation of Di-(2-ethylhexyl) Phthalate by Rhodococcus ruber YC-YT1 in Contaminated Water and Soil

Di-(2-ethylehxyl) phthalate (DEHP) is one of the most broadly representative phthalic acid esters (PAEs) used as a plasticizer in polyvinyl chloride (PVC) production, and is considered to be an endocrine-disrupting chemical. DEHP and its monoester metabolites are responsible for adverse effects on human health. An efficient DEHP-degrading bacterial strain Rhodococcus ruber YC-YT1, with super salt tolerance (0–12% NaCl), is the first DEHP-degrader isolated from marine plastic debris found in coastal saline seawater. Strain YC-YT1 completely degraded 100 mg/L DEHP within three days (pH 7.0, 30 °C). According to high-performance liquid chromatography–mass spectrometry (HPLC-MS) analysis, DEHP was transformed by strain YC-YT1 into phthalate (PA) via mono (2-ethylehxyl) phthalate (MEHP), then PA was used for cell growth. Furthermore, YC-YT1 metabolized initial concentrations of DEHP ranging from 0.5 to 1000 mg/L. Especially, YC-YT1 degraded up to 60% of the 0.5 mg/L initial DEHP concentration. Moreover, compared with previous reports, strain YC-YT1 had the largest substrate spectrum, degrading up to 13 kinds of PAEs as well as diphenyl, p-nitrophenol, PA, benzoic acid, phenol, protocatechuic acid, salicylic acid, catechol, and 1,2,3,3-tetrachlorobenzene. The excellent environmental adaptability of strain YC-YT1 contributed to its ability to adjust its cell surface hydrophobicity (CSH) so that 79.7–95.9% of DEHP-contaminated agricultural soil, river water, coastal sediment, and coastal seawater were remedied. These results demonstrate that R. ruber YC-YT1 has vast potential to bioremediate various DEHP-contaminated environments, especially in saline environments

Relationship between multiscale nanopore structure and coal connectivity during coalification process

The complex nanopore structures in coal provide the space for gas adsorption and migration, which is crucial for the development of coalbed methane. However, the mechanism of the evolution of multi-scale nanopore structures during coalification is still unclear. In this work, a combined method of CO2/N2 adsorption and synchrotron radiation Nano-CT experiments were used to reveal the multi-scale pore structure characterization during coalification. The synchrotron radiation Nano-CT experiment reconstructed the 3D pore network model for different rank coal and revealed the effective diameter is less than 0.5 & mu;m, accounting for 97.4%-99.6% of the total number of macropores. The combination of these methods, including CO2/N2 adsorption and Nano-CT, accurately characterizes the multi-scale pore distribution in coal, ranging from <2 nm, 2-300 nm and 64 nm - 3.5 & mu;m. The ultra-micropores occupy the primary advantage, accounting for approximately 60.3%-95.2% of the total pore volume and the micropores, mesopores and macropores are more poorly developed than ultramicropores. During the coalification process, the proportion of porosity contributed by ultra-micropores to the total porosity gradually increases, with the contribution rising by 57.9%. The proportion of porosity contributed by micropores, mesopores and macropores to the total porosity gradually decreases, with the contribution decreasing by 81.0%, 82.8% and 93.6%, respectively. Besides, with growing coal maturity, the total permeability gradually decreases by 9.26 x 10-3 - 3.05 x 10-1 mD, which is negatively correlated with coal maturity during coalification. And the total permeability is mainly provided by macropores, which account for about 99% of the total permeability. This research provides an in-depth understanding of the storage and transport of coalbed methane in a multi-scale nanopore structure