Multi-Task Deep Learning Seismic Impedance Inversion Optimization Based on Homoscedastic Uncertainty

Seismic inversion is a process to obtain the spatial structure and physical properties of underground rock formations using surface acquired seismic data, constrained by known geological laws and drilling and logging data. The principle of seismic inversion based on deep learning is to learn the mapping between seismic data and rock properties by training a neural network using logging data as labels. However, due to high cost, the number of logging curves is often limited, leading to a trained model with poor generalization. Multi-task learning (MTL) provides an effective way to mitigate this problem. Learning multiple related tasks at the same time can improve the generalization ability of the model, thereby improving the performance of the main task on the same amount of labeled data. However, the performance of multi-task learning is highly dependent on the relative weights for the loss of each task, and manual tuning of the weights is often time-consuming and laborious. In this paper, a Fully Convolutional Residual Network (FCRN) is proposed to achieve seismic impedance inversion and seismic data reconstruction simultaneously, and a method based on the homoscedastic uncertainty of the Bayesian model is used to balance the weights of the loss function for the two tasks. The test results on the synthetic datasets of Marmousi2, Overthrust, and Volve field data show that the proposed method can automatically determine the optimal weight of the two tasks, and predicts impedance with higher accuracy than single-task FCRN model

First Documentation of Large Submarine Sinkholes on the Ganquan Carbonate Platform in the Xisha Islands, South China Sea

Submarine sinkholes are unique and important geomorphological features with a typical cavity structure that are of great scientific value. Submarine sinkholes were discovered for the first time in the isolated Ganquan carbonate platform on the Xisha Islands, the northwestern South China Sea. Based on high-resolution multibeam bathymetric data and seismic profile data, we identified 37 submarine sinkholes at water depths ranging from 550 to 1267 m. They are subcircular to circular negative-relief features, and most of them are V- or compound V-shaped in the cross-section. Their average diameters range from 57 to 667 m, and the depth of the depression ranges from 2.5 to 241 m. By comparing submarine sinkholes in the Ganquan platform with those in other carbonate platforms worldwide, we can infer that the Ganquan platform submarine sinkholes are the largest sinkholes developed on an isolated carbonate platform. Remotely operated vehicle (ROV) “Haima 2” images revealed that the inner walls of submarine sinkholes are characterized by stalactite-like structures, possible dikes, flow marks, and corroded holes, which are typical karstic landscape features. The temperature within submarine sinkholes is 2 °C higher than that of the open ocean at the same water depth. Based on the results of the shallow formation profile and multichannel seismic profiles, we propose that the submarine sinkholes in the Ganquan platform probably formed via the dissolution of the carbonate platform via acidic hydrothermal fluids that originated from magmatic activity and migrated along faults

Data from: Development of HPLC-ELSD method for determination of phytochelatins and glutathione in Perilla frutescens under cadmium stress condition

A rapid, accurate and simple method was developed for the simultaneous determination of glutathione (GSH) and phytochelatins (PCs) by high performance liquid chromatography (HPLC) with evaporative light-scattering detector(ELSD). GSH, Phytochelatin 2 (PC2), phytochelatin 3 (PC3), phytochelatin 4 (PC4), phytochelatin 5 (PC5) and phytochelatin 6 (PC6) can be separated with baseline separation within 9 minutes using a Venusil AA column (250 mm × 4.6 mm i.d., 5 μm particle sizes). The acetonitrile (A) and water containing 0.1% trifluoroacetic acid (0.1% TFA, B) were employed as the mobile phase for the gradient elution. The drifttube temperature and flow rateofcarriergas (N2) were 50℃ and 1.5 L·min-1, respectively. Under the optimum conditions, good linear regression equations of six analytes were obtained with the detection limits ranging from 0.2 to 0.5 µg·mL-1. The proposed method has been applied successfully for the quantification of GSH and PCs in Perilla frutescens (a cadmium hyperaccumulator) under cadmium stress. The recoveries were between 82.9% and 115.3%

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