Abstract
Seismicity and distribution of earthquakes can provide active fault structural information on the crust at a regional scale. The morphology of faults can be derived from the epicentral distribution of micro-earthquakes. In this study, we combined both the relocated earthquake catalogue and related preliminary geophysical information for 3D modeling of the crust in the Xichang area, Sichuan province, China. The fault morphology and deep crustal structure were automatically extracted by the machine learning approach, such as the supervised classification and cluster analysis methods. This new 3D crustal model includes the seismic velocity distribution, fault planes in 3D and 3D seismicity. There are many earthquake clusters located in the folded basement and low-velocity zone. Our model revealed the topological relation between the folded basement and faults. Our work show the crustal model derived is supported by the earthquake clusters which in turn controls the morphological characteristics of the crystalline basement in this area. Our use of machine learning techniques can not only be used to predict the refined fault geometry, but also to combine the seismic velocity structure with the known geological information. This 3D crustal model can also be used for geodynamic analysis and simulation of strong motionseismic waves.
Highlights
In recent years, a large number of high-precision observation arrays have been deployed in Sichuan and Yunnan provinces with the support of the China Seismic Experimental Site (CSES)
By means of automatic modeling, the results can well reflect the features of the regional crustal medium structure
The crustal medium structure in the Xichang area shows that with the continuous enhancement of tectonic stress, the high-speed body is not easy to fracture due to its high degree of crystallization and strong mechanical properties of rock
Summary
A large number of high-precision observation arrays have been deployed in Sichuan and Yunnan provinces with the support of the China Seismic Experimental Site (CSES). The accuracy of seismic location and the inversion method of velocity structure was greatly improved [1] based on enhancing the microseismic monitoring capability in this region [2]. Fruitful scientific research achievements have been produced, such as precise location data, focal mechanism solutions [6,7], and velocity structure models based on different methods and data sources [8–16]. Automatic detection of rupture using source location [22], earthquake relocation [23], automatic acquisition and application of source mechanism [24,25], seismic probabilistic hazard analysis [26], fault geometry based on source information [27–30], etc. We will refine the crustal medium structure by local refinement with a combination of fault model and source model. We integrate the multi-source model is by generating the crustal medium structure model, This geometric model will be used for numerical simulation of seismic wave propagation
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