Parallel-swath-array analysis of passive source surface waves based on beamforming

Abstract

The detection of mined-out coal area remains a pivotal subject in geophysical prospecting and presents an urgent issue requiring resolution. A refined and effective approach to tackle this challenge involves conducting dispersion analysis on passive surface waves, which are derived from ambient noises. While classical passive surface wave methods, such as the Spatial Autocorrelation Method (SPAC), rely on spatial and time domain stationary assumptions, these can be difficult to fulfill. This is especially true when using ambient noises with frequencies >1 Hz, leading to a growing disparity between basic assumptions and actual scenario. To reduce this discrepancy, it's crucial to account for the non-stationary features of noise sources. In this study, we introduce a pioneering approach for parallel-swath-array analysis of passive source surface waves based on beamforming, making full use of non-stationary ambient noises. The parallel-swath array allows for efficient data collection and high-density spatial observation. The method's core concept involves: 1) The collection of noise data by a 2-D array arranged in three parallel lines; 2) The use of a plane-wave basis function to directly scan the original noise records in a time-segment mode within the time domain. These are conducive to unveil the azimuths and time-variant characteristics of the noise sources, resulting in accurate surface wave dispersion information. To ascertain the feasibility and effectiveness of this innovative method, we simulated passive surface wave records for a geological model, accommodating both time-invariant and time-variant distribution of noise sources. The results demonstrate that the method successfully reveals the time-invariant or time-variant characteristics of noise sources and the associated surface wave dispersion information. Simultaneously, we employ the preprocessed steepest-descent (PSD) algorithm to invert the dispersion curves, and the low-velocity layer in the numerical tests can be clearly revealed. In conclusion, we deployed this method in a coal mine situated in Northern China, which encompasses abandoned mined-out areas. This procedure effectively disclosed the intricate azimuthal properties of the noise sources, yielding high-resolution dispersion images. The inversion outcomes depicted a 2-D S-wave velocity section plunging to a depth of 60 m. By interpreting the low-velocity features of the mined-out area, we can accurately delineate its boundaries. The results of the interpretation align coherently with the findings from the drilling data, to verify the feasibility and effectiveness of parallel-swath-array analysis of passive source surface waves based on beamforming in detecting coal mined-out area under non-stationary ambient noises.