Compressive seismic data acquisition in a desert area of western China: A case study

Abstract

The seismic exploration industry continuously demands better imaging quality and consequently requires denser spatial sampling, which increases acquisition cost and time. To alleviate this burden, compressive sensing (CS) theory has been introduced in the design of acquisition geometry, as it requires fewer shot and receiver locations than traditional methods. In 2017, we conducted a field experiment in a desert area in western China. This was the first such test in China to utilize CS theory in a field test. The survey had 1760 shot records with irregular shot and receiver locations designed with guidance from CS theory. By way of data reconstruction, a seismic data set with higher sampling density (7.5 × 7.5 m bin size) was acquired, and the imaging quality was improved significantly compared to existing legacy data (15 × 15 m bin size). These results indicate that a CS-designed acquisition may reduce cost while enhancing imaging quality.