Carbon capture and storage (CCS) is forecast to play a significant role towards CO2 emissions reduction. Cost-effective and simplified monitoring will be essential for rapid adoption and growth of CCS. Seismic imaging methods are regularly utilized to monitor low-velocity anomalies generated by injection of CO2 in the subsurface. In this study we generate active and passive synthetic seismic datasets at different stages of CO2 injection in the subsurface based on geologically constrained subsurface models of the Pelican storage site in the Gippsland Basin, Australia. We apply full waveform inversion (FWI) and wave-equation dispersion (WD) inversion to seafloor deployed distributed acoustic sensing (DAS) data to reconstruct the low-velocity anomalies. We model both strain (DAS) and displacement datasets for the active data component of the study and show that they result in similar reconstruction of the CO2 anomaly. FWI based time-lapse imaging of active data yields the most accurate results. However, this approach is expensive and also suffers from complex issues because of the near-onshore location of the storage site. Alternatively inverting passive data results in only minor differences, but can still effectively monitor changes in the subsurface, and assist in monitoring the CO2 plume at the reservoir depth. Furthermore, we demonstrate the capability of WD for inverting Scholte-waves derived from ambient noise for shallow detection of CO2 in the unlikely event of a leakage. Therefore, we propose a mixed mode monitoring strategy where passive data is utilised for routine monitoring while active surveys are deployed only when further investigation is required.

We use seismic ambient noise recorded by dense ocean bottom nodes (OBNs) in the Gorgon gas field, Western Australia, to compute time-lapse seafloor models of shear-wave velocity. The extracted hourly cross-correlation (CC) functions in the frequency band 0.1 – 1 Hz contain mainly Scholte waves with very high signal to noise ratio. We observe temporal velocity variations (dv/v) at the order of 0.1% with a peak velocity change of 0.8% averaged from all station pairs, from the conventional time-lapse analysis with the assumption of a spatially homogeneous dv/v. With a high-resolution reference (baseline) model from full waveform inversion of Scholte waves, we present an elastic wave equation based double-difference inversion (EW-DD) method, using arrival time differences between the reference and time-lapsed Scholte waves, for mapping temporally varying dv/v in the heterogeneous subsurface. The time-lapse velocity models reveal increasing/decreasing patterns of shear-wave velocity in agreement with those from the conventional analysis. The velocity variation exhibits a ~24-hour cycling pattern, which appears to be inversely correlated with sea level height, possibly associated with dilatant effects for porous, low-velocity shallow seafloor and rising pore pressure with higher sea level. This study demonstrates the feasibility of using dense passive seismic surveys for quantitative monitoring of subsurface property changes in the horizontal and depth domain.

The sensitivity of seismic compressional and shear waves and their velocity ratios to rock lithology, pore fluids, and high-temperature materials makes these parameters very useful for constraining the physical state of the crust. In this study, we develop a joint inversion approach utilizing both radial and vertical components’ autocorrelations of tele seismic P-wave coda for imaging the crust by simultaneously characterizing the crustal Vp, Vs and Vp/Vs ratio. Autocorrelations of the radial and vertical components contain P and S waves that are reflected from the subsurface. Therefore, joint inversion of them can account for the variations of both Vp and Vs, and consequently, the Vp/Vs ratio. Synthetic inversions show significant improvement in the estimation of these parameters com pared to those from the inversion of either, receiver functions or the autocorrelation of the vertical component. The velocity models inferred from the application of the approach to teleseismic data recorded along a north-south passive seismic profile (BILBY experiment) in central Australia reveal a distinct pattern of the Moho and the Vp/Vs variations across the crustal blocks/domain. The general trend of the Moho structure corresponds well with the change of the reflectivity that can normally be seen at the base of the crust and also with the Moho estimated from the previous studies including the deep seismic reflection profiling method. The Vp/Vs structure at depths greater than 10 km shows dominant high values beneath locations where the crustal domains interact (e.g., at transition from one domain to another).

Ambient noise seismic data are widely used by geophysicists to explore subsurface properties at crustal and exploration scales. Two-step dispersion inversion schema is the dominant method used to invert the surface wave data generated by the cross-correlation of ambient noise signals. However, the two-step methods have a 1-D layered model assumption, which does not account for the complex wave propagation. To overcome this limitation, we employ a 2-D wave-equation dispersion inversion (WD) method which reconstructs the subsurface shear (S) velocity model in one step, and elastic wave-equation modeling is used to simulate the subsurface wave propagation. In the WD method, the optimal S velocity model is obtained by minimizing the dispersion curve differences between the observed and predicted surface wave data. This dispersion curve misfit makes the WD method less prone to getting stuck to local minima compared with full waveform inversion. In our study, the observed Scholte waves are generated by cross-correlating continuous ambient noise signals recorded by ocean-bottom nodes (OBN) in the 3-D Gorgon OBN survey, Western Australia. For every two OBN lines, the WD method is used to retrieve the 2-D S velocity structure beneath the first line. We then use a robust neural network based method to interpolate the inverted 2-D velocity slices to a continuous 3-D velocity model and also obtain a corresponding 3-D uncertainty model. Overall, a robust waveform and dispersion match between the observed and predicted data is observed across all of the Gorgon OBN lines both on inverted and interpolated velocity models.

We use seismic ambient noise recorded by ocean bottom nodes (OBNs) in the Gorgon gas field, Western Australia to compute time-lapse seafloor models. The extracted hourly cross-correlation (CC) functions of 0.1 – 1 Hz contain mainly Scholte waves with very high signal to noise ratio. The conventional time-lapse analysis suggests relative velocity variations (dv/v) up to 1% assuming a spatially homogeneous dv/v, with a likely 24-hour cycling pattern. With high-resolution baseline models from full waveform inversion of Scholte waves, we propose a double-difference waveform inversion (DD-WI) method using travel time differences for localizing the time-lapse dv/v in the heterogeneous subsurface in depth. The time-lapse velocity models show velocity increase/decrease patterns in agreement with that from conventional analysis, with more notable changes at the shallower depths. We demonstrate the feasibility of using ambient noise for quantitative monitoring of subsurface property changes in the horizontal and depth domain at an hourly basis.