Multiple Signal Classification Algorithm–Based Reflection Matrix Imaging Method for Tunnel–Array Acoustic Wave Prospecting Technique

ABSTRACT Urban underground tunnelling faces challenges from small‐scale unfavourable geological bodies such as boulders and karst caves, the diameters of which are less than 1 m mostly. To address this issue, the tunnel‐array acoustic wave prospecting technique has been proposed. It utilizes piezoelectric transducers to excite acoustic waves with a central frequency of 4000 Hz, enabling the detection of small‐scale unfavourable geological bodies ahead of tunnel. However, due to the excavation by the shield cutterhead, the cracks and fissures in the rock mass near the cutterhead will significantly develop, forming a disturbed zone with high inhomogeneity. The existence of the disturbed zone will cause severe multiple scattering, which induces artefacts in the imaging results and reduces the accuracy of the advanced prospecting results. In terms of above issues, we introduce the idea of multiple signal classification (MUSIC) algorithm into the reflection matrix method and propose a novel MUSIC algorithm–based reflection matrix method. The reflection matrix can achieve the imaging of reflectors through re‐projecting the acquired data into the media at excitation and reception using Green's function. But it cannot deal with the artefacts induced by multiple scattering. The idea of MUSIC algorithm is to calculate the correlation between Green's function and the singular vectors of the signal or noise subspace, which are obtained by singular value decomposition (SVD) of covariance matrix of the acquired data, achieving estimation of the reflectors. Referring to this idea, we further improved the reflection matrix using MUSIC algorithm. The reflection matrix method is applied first, and the reflection matrix is obtained. Then by SVD of covariance matrix of the reflection matrix, we obtain the signal vectors related to the imaging results of reflectors and noise vectors related to artefacts. The signal vectors are used to calculate the correlation with an imaging operator K , which is derived from the product of the conjugate of Green's function and itself. When the computing grid within reflectors, the results reach the local maximum; otherwise, it tends to 0. In this way, we mitigate the imaging artefacts introduced by the multiple scattering. Through synthetic experiment, we verified that the proposed method can effectively suppress the imaging noise and improve resolution of the imaging results compared to the reflection matrix method. Finally, the proposed method was applied on field data obtained in Zhanmatun Iron Mine and successfully predicted the interface of the opposite tunnel in the target area.