Near Surface Characterization by Seismic Surface Wave Inversion

Ahmed Ferhan Y,Tsingas Constantinos,Almubarak Mohammed S

doi:10.35840/2631-5114/3508

Abstract

Surface waves are usually treated as unwanted coherent noise and steps are taken to remove them using geophone receiver arrays during seismic survey acquisition. During early data processing, attempts are made to further remove the ground roll. Recently, there has been a growing interest in using surface waves for estimating the near surface velocity model. Surface waves propagate in the shallow subsurface and can be used to derive shear wave velocities of the near surface.

Highlights

In land seismic surveys, surfaces waves (Rayleigh waves or ground roll) account for over 65% of the total energy recorded (Figure 1) and are the dominant events in any seismic records
Surface waves are usually treated as unwanted coherent noise and steps are taken to remove them using geophone receiver arrays during seismic survey acquisition
This is important for seismic data acquired over land desert environment where the near surface geology is very complex

Summary

Introduction

Surfaces waves (Rayleigh waves or ground roll) account for over 65% of the total energy recorded (Figure 1) and are the dominant events in any seismic records. With closely spaced point-receivers, the wavefield is better sampled in space and time so that surface waves can be analyzed and inverted to characterize the near surface [1]. This is important for seismic data acquired over land desert environment where the near surface geology is very complex. By extracting and inverting dispersion curves to obtain local 1D S-wave velocity profiles, multichannel analysis of surface waves (MASW) has been proven as an efficient way to analyze shallow-seismic surface waves [2]. Surface wave inversion is normally performed by inverting the dispersion curve to derive shear wave velocity model for the near surface. The presence of a low frequency component in the single sensor data along with denser coverage was critical for the inversion

Methodology

Findings

Conclusions