We invert seismic data collected at Blue Canyon Dome, west of Socorro, New Mexico, to obtain a time-domain representation of the evolution of eight seismic sources from Blue Canyon Dome Phase II. This experiment consisted of eight chemical explosions (or shots), three with dry emplacements and five with wet emplacements. The three dry shots are co-located at depth with three of the wet shots. Seismic and distributed acoustic sensing data recorded at the site both show a polarity reversal in the dry shot data when compared with the wet shot data located at the same depth. This implies that there is a physical explanation for this polarity reversal because it is captured across two different data modalities. In addition, the moment functions (MFs) recovered from our inversions show source mechanisms consistent with the polarities present in the observed data. Inversion of 24 three-component seismic stations filtered in four frequency bands, 5-10, 9-15, 5-15, and 8-20 Hz, as well as inversions run using data from three different station geometries, resulted in an early-time net negative volumetric change in the MFs from the dry shots and a net positive volumetric change for the wet shots. The water in the near-source region, in which the wet shots were emplaced, appears to have a significant impact on the recovered yield for these shots.

ABSTRACT The Rock Valley fault zone (RVFZ), an intraplate strike-slip fault zone in the southern Nevada National Security Site (NNSS), hosted a series of very shallow (<3 km) earthquakes in 1993. The RVFZ may also have hydrological significance within the NNSS, potentially playing a role in regional groundwater flow, but there is a lack of local hydrological data. In the Spring of 2021, we collected active-source accelerated weight drop seismic data over part of the RVFZ to better characterize the shallow subsurface. We manually picked ∼17,000 P-wave travel times and over 14,000 S-wave travel times, which were inverted for P-wave velocity (VP), S-wave velocity (VS), and VP/VS ratio in a 3D joint tomographic inversion scheme. Seismic velocities are imaged as deep as ∼700 m in areas and generally align with geologic and structural expectations. VP and VS are relatively reduced near mapped and inferred faults, with the most prominent lower VP and VS zone around the densest collection of faults. We image VP/VS ratios ranging from ∼1.5 to ∼2.4, the extremes of which occur at a depth of ∼100 m and are juxtaposed across a fault. One possible interpretation of the imaged seismic velocities is enhanced fault damage near the densest collection of faults with relatively higher porosity and/or crack density at ∼100 m depth, with patches of semiperched groundwater present in the sedimentary rock in higher VP/VS areas and drier rock in lower VP/VS areas. A relatively higher VP/VS area beneath the densest faults persists at depth, which suggests percolation of groundwater via the fault damage zone to the regionally connected lower carbonate aquifer. Potentially, the presence and movement of groundwater may have played a role in the 1993 earthquake aftershocks.

Accurate event locations are important for many endeavors in seismology, and understanding the factors that contribute to uncertainties in those locations is complex. In this article, we present a case study that takes an in-depth look at the accuracy and precision possible for locating nine shallow earthquakes in the Rock Valley fault zone in southern Nevada. These events are targeted by the Rock Valley Direct Comparison phase of the Source Physics Experiment, as candidates for the colocation of a chemical explosion with an earthquake hypocenter to directly compare earthquake and explosion sources. For this comparison, it is necessary to determine earthquake hypocenters as accurately as possible so that different source types have nearly identical locations. Our investigations include uncertainty analysis from different sets of phase arrivals, stations, velocity models, and location algorithms. For a common set of phase arrivals and stations, we find that epicentral locations from different combinations of velocity models and algorithms are within 600 m of one another in most cases. Event depths exhibit greater uncertainties, but focusing on the S-P times at the nearest station allows for estimates within approximately 500 m.

Characterizing explosion sources and differentiating between earthquake and underground explosions using distributed seismic networks becomes non-trivial when explosions are detonated in cavities or heterogeneous ground material. Moreover, there is little understanding of how changes in subsurface physical properties affect the far-field waveforms we record and use to infer information about the source. Simulations of underground explosions and the resultant ground motions can be a powerful tool to systematically explore how different subsurface properties affect far-field waveform features, but there are added variables that arise from how we choose to model the explosions that can confound interpretation. To assess how both subsurface properties and algorithmic choices affect the seismic wavefield and the estimated source functions, we ran a series of 2-D axisymmetric non-linear numerical explosion experiments and wave propagation simulations that explore a wide array of parameters. We then inverted the synthetic far-field waveform data using a linear inversion scheme to estimate source-time functions (STFs) for each simulation case. We applied principal component analysis (PCA), an unsupervised machine learning method, to both the far-field waveforms and STFs to identify the most important factors that control variance in the waveform data and differences between cases. For the far-field waveforms, the largest variance occurs in the shallower radial receiver channels in the 0-50 Hz frequency band. For the STFs, both peak amplitude and rise times across different frequencies contribute to the variance. We find that the ground equation of state (i.e. lithology and rheology) and the explosion emplacement conditions (i.e. tamped versus cavity) have the greatest effect on the variance of the far-field waveforms and STFs, with the ground yield strength and fracture pressure being secondary factors. Differences in the PCA results between the far-field waveforms and STFs could possibly be due to near-field non-linearities of the source that are not accounted for in the estimation of STFs and could be associated with yield strength, fracture pressure, cavity radius and cavity shape parameters. Other algorithmic parameters are found to be less important and cause less variance in both the far-field waveforms and STFs, meaning algorithmic choices in how we model explosions are less important, which is encouraging for the further use of explosion simulations to study how physical Earth properties affect seismic waveform features and estimated STFs.

Marine hydrokinetic devices, such as wave energy converters (WECs), can unlock untapped energy from the ocean's currents and waves. Acoustic impact assessments are required to ensure that the noise these devices generate will not negatively impact marine life, and accurate modeling of noise provides an a priori means to viably perform this assessment. We present a case study of the PacWave South site, a WEC testing site off the coast of Newport, Oregon, demonstrating the use of ParAcousti, an open-source hydroacoustic propagator tool, to model noise from an array of 28 WECs in a 3-dimensional (3-D) realistic marine environment. Sound pressure levels are computed from the modeled 3-D grid of pressure over time, which we use to predict marine mammal acoustic impact metrics (AIMs). We combine two AIMs, signal to noise ratio and sensation level, into a new metric, the effective signal level (ESL), which is a function of propagated sound, background noise levels, and hearing thresholds for marine species and is evaluated across 1/3 octave frequency intervals. The ESL model can be used to predict and quantify the potential impact of an anthropogenic signal on the health and behavior of a marine mammal species throughout the 3-D simulation area.