Inversion of seismic source parameters from satellite InSAR data based on deep learning

Abstract

Satellite interferometric synthetic aperture radar (InSAR) has been playing an important role in the earthquake surface deformation observation and source model inversions with its advantage of fine spatial resolution in comparison with seismological data. At present, the two-step inversion approach, including a nonlinear inversion step for determining the fault geometry (e.g., length, width, depth, strike, dip, rake, slip) and a linear inversion step for estimating the slip distribution, is widely used to obtain seismic source parameters from satellite InSAR data. However, the nonlinear inversion step has some weaknesses, such as a prior knowledge requirement, local minima solution, complex and time-consuming. Coseismic differential interferogram is input into back propagation neural network (BPNN) to perform real time inversion of fault geometry in the previous study. But due to the simpleness of network structure, rake and slip parameters is fixed and inversion accuracy is limited. In this paper, we propose a deep learning approach of Earthquake Source Parameters Inversion using ResNet (abbreviated as ESPI-ResNet) from satellite InSAR data. ESPI-ResNet is able to firstly classify the 4 fault types and then invert the 7 fault geometry parameters based on uniform slip model. We train our model by firstly creating a large dataset of simulated interferograms with source parameters ranges confined by seismological and geological data. The accuracy of fault type classification is 99.6% and root mean squared error (RMSE) of inversed fault geometry parameters is low in the simulated test dataset. To find most suitable model for seismic source parameters inversion, we further compare the inversed accuracy of different networks, including BPNN, VGG-16, ResNet-18, ResNet-34, ResNet-50 and DenseNet. We find that moderate increase of network depth and the use of convolution, deep residual learning can improve the model's performance for source parameters inversion, and therefore ResNet-34 is chosen as the backbone network in this study. Finally, real differential interferograms of Yutian earthquake (2020 Mw 6.3), Jiuzhaigou earthquake (2017 Mw 6.5) and Menyuan earthquake (2016 Mw 5.9) are used to validate the proposed method. Real earthquakes validation shows all of them have correct fault type classification, and the inverted results are consistent with the current seismic source parameters data.