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Travel time picking of ambient noise cross-correlation using a deep neural network combining convolutional neural networks and Transformer

Jin Chunwei, Ye Fang, Zhang Han, Bao Xue

Acta Geophysica

2024

Vol. 72, no. 1

97--114

The travel time of ambient noise cross-correlation is widely used in geophysics, but traditional methods for picking the travel time of correlation are either difficult to be applied to data with low signal-to-noise ratio (SNR), or make some assumptions which fail to be achieved in many realistic situations, or require a lot of complex calculations. Here, we present a neural network based on convolutional neural networks (CNN) and Transformer for the travel time picking of ambient noise crosscorrelation. CNNs expand the dimension of the vector of each time step for the input of Transformer. Transformer focuses the model’s attention on the key parts of the sequence. Model derives the travel time according to the attention. 102,000 cross-correlations are used to train the network. Compared with traditional methods, the approach is easy to use and has a better performance, especially for the low SNR data. Then, we test our model on another ambient noise cross-correlation dataset, which contains cross-correlations from different regions and at different scales. The model has good performance on the test dataset. It can be seen from the experiment that the travel time of the cross-correlation function of ambient noise with an average SNR as low as 9.3 can be picked. 97.2% of the picked travel times are accurate, and the positive and negative travel time of most cross-correlations are identical (90.2%). Our method can be applied to seismic instrument performance verification, seismic velocity imaging, source location and other applications for its good ability to pick travel time accurately.

Seepage process on weathered crust elution-deposited rare earth ores with ammonium carboxylate solution

Zou Hualiang, Zhang Zhenyue, Chen Zhuo, Liu Defeng, Chai Xiuwei, Zhang Han, Chi Ru-an

Physicochemical Problems of Mineral Processing

2020

Vol. 56, iss. 1

89--101

In order to reveal the seepage law of ammonium carboxylate solution in the in-situ leaching process of weathered crust elution-deposited rare earth ores, the effects of concentration, pH, temperature, particle size and porosity on permeability were discussed in this paper. The results shown that the seepage of the leaching agent solutions in the rare earth ore follows Darcy's law and displays a laminar flow under the conditions of this experiment and seepage velocity can be increased by changing leaching conditions. The permeability coefficients are inversely proportional to concentrations of ammonium acetate, ammonium tartrate and ammonium citrate whose concentration is greater than 0.7wt%, because the insoluble complexes formed by the reaction of ammonium citrate with RE3+ at lower concentration n decrease the permeability coefficient. The permeability coefficients of ammonium carboxylate solutions increase firstly and then decrease with the pH increased. The maximum of permeability coefficients of ammonium acetate, ammonium tartrate and ammonium citrate solution were 2.92, 1.91 and 2.70, respectively, while the pH of solution were 5, 6 and 7, respectively. Increasing temperature is beneficial for the seepage of ammonium carboxylate solution in orebody, therefore, it is helpful for leaching operation in summer. Moreover, clay minerals particle size and porosity are the key factors affecting the permeability of ammonium carboxylate solution in orebody. The permeability coefficients of ammonium acetate, ammonium tartrate and ammonium citrate solutions are 2.92×104cm/s,1.90×10-4cm/s and 2.69×10-4cm/s, respectively, at the same temperature of 293K, original particle size and porosity of the ore. Ammonium acetate solution has the best permeability in orebody.