Tunnel detection at Yuma Proving Ground, Arizona, USA — Part 2: 3D full-waveform inversion experiments

Abstract

We have applied time-domain 3D elastic full-waveform inversion (FWI) to a known tunnel constructed 10 m below the surface with no distinguishing surface expressions. Multicomponent inversion experiments that use an initial model estimated from surface wave methods suggest that the vertical sources and the combination of vertical and longitudinal receivers result in the clearest image of the tunnel. We obtain an approximate 3D image of the tunnel using 24 vertical sources and 720 vertical and 720 longitudinal receivers. We find that increasing the number of vertical sources to 216 does not significantly improve the details of the tunnel. Further experimentation indicates that we can detect the tunnel using a reduced data set of 6–10 vertical sources and 216 vertical sensors. In addition, calculating the [Formula: see text] ratio helps remove artifacts from the inverted model and highlights the location of the tunnel. We compare the 3D inversion results with the 2D FWI results for the same tunnel that we previously evaluated. The variety of successful inversion experiments suggest that FWI is capable of imaging shallow tunnels in desert geologies.

Seismic wavefield imaging of Earth’s interior across scales

“…FWI is computationally and algorithmically demanding, and, although these demands hampered progress for many decades, these restrictions have been largely overcome in the past decade, due to advances in algorithms and computing technology. Today, FWI is widely used in applications including medical imaging [10][11][12][13][14] , nondestructive testing [15][16][17][18][19][20][21][22][23][24] , near-surface characterization [25][26][27][28][29][30] , onshore and offshore exploration seismology [31][32][33][34][35][36][37][38][39] , deep crustal seismic imaging [40][41][42][43][44][45][46][47] , earthquake seismology [48][49][50][51][52][53][54] and ambient-noise seismology 55,56 . A comprehensive ove...…”

“…Applications of seismic FWI can be categorized as controlled-source, earthquake and ambient-noise seismology. In addition to hydrocarbon exploration and deep crustal imaging [40][41][42][43][44][45][46][47] , controlled-source applications can be further subdivided by scale into medical imaging [10][11][12][13][14] , nondestructive testing [15][16][17][18][19][20][21][22][23][24] and near-surface charac terization of the top tens of metres of the Earth [25][26][27][28][29][30] , but these are beyond the scope of this Technical Review. Since 2010, ambient-noise FWI based on seismic interferometry [159][160][161] has emerged 55,56 .…”

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

“…To accurately consider the void shapes and low velocity zones (e.g., backfill material), an accurate wave field modelling could be adopted, which can account for the finite frequency nature of the wave propagation and improve the location accuracy. Currently, wavefield modelling still has quite a high computational cost and can involve complex mesh generation: within the air-filled void V P equals the air sound speed (∼340 m/s), which is an extreme value for the numerical modelling that is hardly achievable due to the restriction of numerical dispersion [55], [56]. If the above problems can be solved, the wave field modelling could be a promising way to improve the 3-D model resolution and the accuracy of locating mining microseismicity.…”

Efficient tunnel detection with waveform inversion of back-scattered surface waves

“…Chen et al (2016) successfully applied a frequency-dependent traveltime tomography and the FWI on the P-and SH-wave refraction data to detect a known near-surface tunnel. Wang et al (2018) and Smith et al (2018) applied the 2D and 3D FWI to detect a 10meter-deep hand-dug tunnel at the Yuma Proving Ground, Arizona. Tran and Sperry (2018) applied FWI with a land-streamer acquisition system to assess roadway subsidence.…”