Effect of the Kaikōura Earthquake on Velocity Changes In and Around the Ruptured Region: A Noise Cross-Correlation Approach

<p>Seismic velocity changes before and after large magnitude earthquakes carry information about damage present within the faults in the surrounding region. In this thesis, temporal velocity changes are measured before and after the 2016 Kaikōura earthquake using ambient noise interferometry between 2012 - 2018. This period contains the Mw 7.8 2016 Kaikoura earthquake as well as the 2013 Cook Strait earthquake sequence and a few deep large magnitude earthquakes in 2015 - 2016. Three primary objectives are identified: (1) investigate seismic velocity changes in the Kaikōura region and their connection to the 2016 Kaikōura earthquake to try and determine if there was a change before/after the earthquake, (2) determine how this change varied across the region, and (3) consider if ambient noise can lead to improved detection and understanding of geological hazard. The primary approach used to measure velocity changes in the Kaikōura region involved cross correlating noise recorded by seismic stations across the region. Velocity changes are sought by averaging the best result from multiple onshore station pairs. A secondary approach was also used, in which specific station pairs were averaged to determine if there were more localised velocity changes over more specific regions. This was to determine if the velocity changes observed following the 2016 Kaikōura earthquake occurred over the entire ruptured region. Following the 2016 Kaikōura earthquake a velocity decrease of 0.24±0.02% was observed on the average of the vertical-vertical components for eight stations. The remaining eight cross-component pairs showed a smaller seismic decrease with an average value of 0.22±0.05%. After the decrease following the Kaikōura earthquake, there is a steady velocity increase of 0.13±0.02% over a one-and-a-half-year period. This indicates that prior to the earthquake, seismic velocity was at a steady state until it was perturbed by the Kaikōura earthquake, and seismic velocities rapidly decreased over all stations. Across the region, stations with a longer interstation distance and further away from ruptured faults had a smaller decrease in velocity than station pairs with a smaller interstation distance that were closer to ruptured faults. We interpret the velocity decrease following the Kaikōura earthquake as a result of cracks opening during the earthquake. The velocity increase following the earthquake is indicative of the cracks slowly healing. The Cook Strait earthquake sequence that occurred in 2013 did not cause any velocity changes at the stations used in this thesis. This has been interpreted to be because the changes were too small compared to the background noise or the stations were not recording during the time of the earthquake sequence. Two other decreases were also observed in the region following two deep earthquakes in April 2015 (Mw 6.2, depth = 52km) and February 2016 (Mw 5.7, depth = 48km). Both of these events resulted in a small seismic decrease of 0.1±0.02%. Although these earthquakes were close to seismic stations when they occurred, they were much deeper and had a smaller magnitude than the Kaikōura earthquake so did not cause a large velocity decrease. By understanding what causes velocity changes it is possible to have an improved understanding of the geological hazard in the region.</p>

Effect of the Kaikōura Earthquake on Velocity Changes In and Around the Ruptured Region: A Noise Cross-Correlation Approach

<p>Seismic velocity changes before and after large magnitude earthquakes carry information about damage present within the faults in the surrounding region. In this thesis, temporal velocity changes are measured before and after the 2016 Kaikōura earthquake using ambient noise interferometry between 2012 - 2018. This period contains the Mw 7.8 2016 Kaikoura earthquake as well as the 2013 Cook Strait earthquake sequence and a few deep large magnitude earthquakes in 2015 - 2016. Three primary objectives are identified: (1) investigate seismic velocity changes in the Kaikōura region and their connection to the 2016 Kaikōura earthquake to try and determine if there was a change before/after the earthquake, (2) determine how this change varied across the region, and (3) consider if ambient noise can lead to improved detection and understanding of geological hazard. The primary approach used to measure velocity changes in the Kaikōura region involved cross correlating noise recorded by seismic stations across the region. Velocity changes are sought by averaging the best result from multiple onshore station pairs. A secondary approach was also used, in which specific station pairs were averaged to determine if there were more localised velocity changes over more specific regions. This was to determine if the velocity changes observed following the 2016 Kaikōura earthquake occurred over the entire ruptured region. Following the 2016 Kaikōura earthquake a velocity decrease of 0.24±0.02% was observed on the average of the vertical-vertical components for eight stations. The remaining eight cross-component pairs showed a smaller seismic decrease with an average value of 0.22±0.05%. After the decrease following the Kaikōura earthquake, there is a steady velocity increase of 0.13±0.02% over a one-and-a-half-year period. This indicates that prior to the earthquake, seismic velocity was at a steady state until it was perturbed by the Kaikōura earthquake, and seismic velocities rapidly decreased over all stations. Across the region, stations with a longer interstation distance and further away from ruptured faults had a smaller decrease in velocity than station pairs with a smaller interstation distance that were closer to ruptured faults. We interpret the velocity decrease following the Kaikōura earthquake as a result of cracks opening during the earthquake. The velocity increase following the earthquake is indicative of the cracks slowly healing. The Cook Strait earthquake sequence that occurred in 2013 did not cause any velocity changes at the stations used in this thesis. This has been interpreted to be because the changes were too small compared to the background noise or the stations were not recording during the time of the earthquake sequence. Two other decreases were also observed in the region following two deep earthquakes in April 2015 (Mw 6.2, depth = 52km) and February 2016 (Mw 5.7, depth = 48km). Both of these events resulted in a small seismic decrease of 0.1±0.02%. Although these earthquakes were close to seismic stations when they occurred, they were much deeper and had a smaller magnitude than the Kaikōura earthquake so did not cause a large velocity decrease. By understanding what causes velocity changes it is possible to have an improved understanding of the geological hazard in the region.</p>

Seismic velocity changes at White Island volcano, New Zealand, using ten years of ambient noise interferometry

<p>Seismic velocity changes at volcanoes carry information about stresses present within hydrothermal and magmatic systems. In this thesis, temporal velocity changes are measured at White Island volcano using ambient noise interferometry between 2007–2017. This period contains multiple well-documented eruptions starting in 2012, following an inactive period that extends back over a decade. Three primary objectives are identified: (1) investigate what seismic velocity changes can tell us about dynamic changes beneath the volcano, (2) investigate non-volcanic sources and their possible influence on interpretations, and (3) consider the potential for real-time monitoring using ambient-noise. These objectives extend beyond White Island volcano, with implications for ambient noise monitoring of volcanoes globally. Two different approaches are used to measure velocity changes at White Island. The first involves cross-correlating noise recorded by pairs of seismic stations. Velocity changes are sought by averaging changes recorded across ten station-pairs that consist of an onshore station and a station on the volcano. The second approach involves cross-correlating the different components of individual seismic stations. This represents a less traditional approach to monitoring volcanoes, but is well-suited to White Island which has one permanent station active throughout eruptive activity. Single seismic stations located onshore are also processed to investigate background regional changes. Two periods of long-term velocity increases are detected at the volcano. The first occurs during a highly active period in 2012–2013 and the second occurs in the months preceding an explosive eruption in April 2016. Comparison with velocities recorded by onshore stations suggest a meteorological source for these changes is unlikely. Velocity increases are therefore interpreted to reflect cracks closing under increased pressures beneath the volcano. Similarly, a rapid decline in the velocity within 2–3 months of the April 2016 eruption is interpreted to reflect depressurization of the system. In addition to volcanic sources, we also find clear evidence of non-volcanic processes influencing velocity changes at the volcano. Two clear co-seismic velocity decreases of approximately 0.05–0.1% are associated with a Mw 5.2 earthquake in 2008 — within 10 km of the volcano — and the Mw 7.1 East Cape earthquake in 2016. The East Cape earthquake — located 200 km away from the volcano — produces significant velocity decreases over a large region, as detected by stations onshore and on White Island. This likely reflects dynamic stress changes as a result of passing seismic waves, with an eruption two weeks later interpreted here to have been triggered by this event. Finally, we identify similarities between annual variations recorded by onshore stations and changes at the volcano, suggesting an environmental influence. Velocity changes at White Island therefore represent a complex interaction of volcanic and non-volcanic processes, highlighting the need for improved understanding of external sources of change to accurately detect short-term eruptive precursors.</p>

Seismic velocity changes at White Island volcano, New Zealand, using ten years of ambient noise interferometry

<p>Seismic velocity changes at volcanoes carry information about stresses present within hydrothermal and magmatic systems. In this thesis, temporal velocity changes are measured at White Island volcano using ambient noise interferometry between 2007–2017. This period contains multiple well-documented eruptions starting in 2012, following an inactive period that extends back over a decade. Three primary objectives are identified: (1) investigate what seismic velocity changes can tell us about dynamic changes beneath the volcano, (2) investigate non-volcanic sources and their possible influence on interpretations, and (3) consider the potential for real-time monitoring using ambient-noise. These objectives extend beyond White Island volcano, with implications for ambient noise monitoring of volcanoes globally. Two different approaches are used to measure velocity changes at White Island. The first involves cross-correlating noise recorded by pairs of seismic stations. Velocity changes are sought by averaging changes recorded across ten station-pairs that consist of an onshore station and a station on the volcano. The second approach involves cross-correlating the different components of individual seismic stations. This represents a less traditional approach to monitoring volcanoes, but is well-suited to White Island which has one permanent station active throughout eruptive activity. Single seismic stations located onshore are also processed to investigate background regional changes. Two periods of long-term velocity increases are detected at the volcano. The first occurs during a highly active period in 2012–2013 and the second occurs in the months preceding an explosive eruption in April 2016. Comparison with velocities recorded by onshore stations suggest a meteorological source for these changes is unlikely. Velocity increases are therefore interpreted to reflect cracks closing under increased pressures beneath the volcano. Similarly, a rapid decline in the velocity within 2–3 months of the April 2016 eruption is interpreted to reflect depressurization of the system. In addition to volcanic sources, we also find clear evidence of non-volcanic processes influencing velocity changes at the volcano. Two clear co-seismic velocity decreases of approximately 0.05–0.1% are associated with a Mw 5.2 earthquake in 2008 — within 10 km of the volcano — and the Mw 7.1 East Cape earthquake in 2016. The East Cape earthquake — located 200 km away from the volcano — produces significant velocity decreases over a large region, as detected by stations onshore and on White Island. This likely reflects dynamic stress changes as a result of passing seismic waves, with an eruption two weeks later interpreted here to have been triggered by this event. Finally, we identify similarities between annual variations recorded by onshore stations and changes at the volcano, suggesting an environmental influence. Velocity changes at White Island therefore represent a complex interaction of volcanic and non-volcanic processes, highlighting the need for improved understanding of external sources of change to accurately detect short-term eruptive precursors.</p>

Development of Time Lapse VSP Integration Workflow: A Case Study at Farnsworth CO2-EOR Project

This study aims to develop a 4D Vertical Seismic Profile (VSP) integration workflow to improve the prediction of subsurface stress changes. The selected study site is a 5-spot pattern within the ongoing CO2-EOR operations at the Farnsworth Field Unit FWU in Ochiltree County, Texas. The specific pattern has undergone extensive geological and geomechanical characterization through the acquisition of 3D seismic data, geophysical well logs, and core. This workflow constrains a numerical hydromechanical model by applying a penalty function formed between "modeled" versus "observed" time-lapse compressional and shear seismic velocity changes. Analyses of geophysical logs and ultra-sonic measurements on core exhibit measurable sensitivities to changes in both fluid saturation and mean effective stress. These data are used to develop a site-specific rock physics model and stress-velocity relationship, which inform the numerical models used to generate the "modeled" portion of the penalty function. The "observed" portion of the penalty function is provided by a novel elastic full-waveform inversion of the available 3D baseline and three monitor surveys to produce high-quality estimates of time-lapse compressional and shear seismic velocity changes. The modeling workflow accounts sequentially for fluid substitution and stress impacts. Hydrodynamic and geomechanical properties of the 3D coupled numerical model are estimated through geostatistical integration of well log and core data with 3D seismic inversion products. Changes in seismic velocities due to fluid substitution are computed using the Biot-Gassmann workflow and site-specific rock physics. Stress impacts on time-lapse seismic velocity changes are modeled from the effective stress output of the hydromechanical model and are initially based on the velocity versus effective stress relationship extracted from core mechanical testing. Based on the principle of superposition of seismic wavefields, seismic velocity changes attributed to fluid substitution and that due to changes in mean effective stress are treated as linearly additive. The modeled results are upscaled using Backus averaging to reconcile scale discrepancies between the modeled and measured datasets to formulate the penalty function. This manuscript presents the forward modeling process and concludes that for the base case, the seismic velocity changes due to mean effective stress dominates over the seismic velocity changes attributed to fluid substitution because of the extensive range of the pressure perturbations. Successful minimization of this penalty function calibrates the coupled hydrodynamic geomechanical numerical model and affirms the suitability of acoustic time-lapse measurements such as 4D-VSP for geomechanical calibration.

Regional seismic velocity changes following the 2019 Mw7.1 Ridgecrest California earthquake from autocorrelations and P/S converted waves

Summary We examine regional transient changes of seismic velocities generated by the Mw 7.1 2019 Ridgecrest earthquake in California, using autocorrelations of moving time windows in continuous waveforms recorded at regional stations. We focus on travel time differences in a prominent phase generated by an interface around 2 km depth, associated with transmitted Pp waves and converted Ps waves from the ongoing microseismicity. Synthetic tests demonstrate the feasibility of the method for monitoring seismic velocity changes. Taking advantage of the numerous aftershocks in the early period following the mainshock, we obtain a temporal resolution of velocity changes up to 20 min in the early post-mainshock period. The results reveal regional coseismic velocity drops in the top 1–3 km with an average value of ∼2 per cent over distances up to 100 km from the Ridgecrest event. These average velocity drops are likely dominated by larger changes in the shallow materials, and are followed by rapid recoveries on timescales of days. Around the north end of the Ridgecrest rupture and the nearby Coso geothermal region, the observed coseismic velocity drops are up to ∼8 per cent. The method allows monitoring temporal changes of seismic velocities with high temporal resolution, fast computation, and precise spatial mapping of changes. The results suggest that significant temporal changes of seismic velocities of shallow materials are commonly generated on a regional scale by large events.

Spatial and Temporal Influence of Sea Level on Inland Stress based on Seismic Velocity Monitoring

Earth’s crust responds to perturbations from various environmental factors. To evaluate this response, seismic velocity changes offer an indirect diagnostic, especially where velocity can be monitored on an ongoing basis from ambient seismic noise. Investigating the connection between the seismic velocity changes and external perturbations could be useful for characterizing dynamic activities in the crust. The seismic velocity is known to be sensitive to variations in meteorological signals such as temperature, snow, and precipitation as well as changes in sea level. Among these perturbations, the impact of variations in sea level on velocity changes inferred from seismic interferometry of ambient noise is not well known. This study investigates the influence of the ocean in a 3-year record of ambient noise seismic velocity monitoring in the Chugoku and Shikoku regions of southwest Japan. First, we applied a bandpass filter to determine the optimal period band for discriminating among different influences on seismic velocity. Then, we applied a regression analysis between the proximity of seismic station pairs to the coast and the ocean influence, as indicated by the correlation of sea level to seismic velocity changes between pairs of stations. Our study suggests that for periods between 0.0036 to 0.01 cycle/day (100–274 days), the ocean’s influence on seismic velocity decreases with increasing distance of station pairs from the coast. The increasing sea level deforms the ocean floor, affecting the stress in the adjacent coast. The stress change induced by the ocean loading may extend at least dozens of kilometers from the coast. The correlation between sea level and inland seismic velocity changes are negative or positive. Although it is difficult to clearly interpret the correlation based on simple model, they could depend on the in situ local stress, orientation of dominant crack, and hydraulic conductivity. Our study shows that seismic monitoring may be useful for evaluating the perturbation in the crust associated with an external load.