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We are measuring the low frequency velocities and damping factors of acoustic waves in rock powders in vacuo with a view to understanding the lunar seismic signals. Our results combined with those of others, when extrapolated to lunar conditions, lend support to the Gold-Soter model of the outer moon in which the lunar surface is covered with a fine rock powder to a depth of at least 4 km.
A seismic model of the moon, refined from previous models with the aid of new data and analyses for seismic events recorded by four Apollo lunar seismic stations and a gravimeter, is outlined. The current model has five zones. The crust is 55-60 km thick. The surface is covered by regolith of varying thicknesses and low seismic velocity. The seismic Q's are extremely high. The upper mantle, 250 km thick, is characterized by seismic velocities close to those of the earth's upper mantle, and shear wave Q of about 4000. Tidally controlled deep moonquakes are concentrated near the boundary of the middle mantle, 600-700 km thick. A lower mantle extending to an undetermined depth is characterized by high attenuation of shear waves. The existence of a low-velocity core is only tentatively proposed.
SUMMARY We present an analysis of combining translational and rotational seismic data in an inversion for the time-variable source time functions corresponding to the components of the seismic moment tensor. We conduct a series of numerical experiments where the data are simulated by a combination of an underground explosion and a co-located double couple shear source and recorded on surface-mounted seismometers within 1–2 km of the source. The experiments are designed to mimic explosion seismology experiments, and thus the data are in the 1–10 Hz frequency range and contain very few surface waves. We use a Monte Carlo method to propagate Earth model uncertainty into the estimates of seismic source parameters. In our experiments, we find that the uncertainty of the estimated seismic source parameters increases when we add rotational seismic motions to the inversion when using a constant number of data channels. In this case, the increased degree of uncertainty in the final results is most likely due to the near-surface Earth model uncertainty that we introduce in our simulations. However, for a fixed number of seismic stations, adding rotational seismic motions to the inversion acts to decrease the uncertainty of the estimated seismic source parameters, most likely due to the increase in the number of data channels used in the inversion.
Subduction zones transport significant amounts of water into Earth’s mantle, primarily through hydrous minerals such as lawsonite. However, the seismic detectability of lawsonite-bearing oceanic crust at mantle depths remains uncertain. To address this issue, we measured sound velocities of lawsonite up to 7.4 GPa and 600 °C. Both P- and S-wave velocities exhibited unexpected increases with temperature under high-pressure conditions. Our result suggest that hydrous oceanic crust exhibits higher seismic velocities than the surrounding mantle at depths of 150 to 250 km, resulting in high-velocity anomalies rather than the previously assumed low-velocity anomalies. Furthermore, the seismic velocity difference between hydrous and dry oceanic crust is less than 2%, making it challenging to distinguish between them using seismic velocities. This limitation may hinder the detection of the hydration state in subducted crust. In addition, lawsonite remains stable in 90% of subduction zones, and thus, such “seismically invisible water” may exist in most subducted slabs around the world.
New methods for predicting the amplitude and variability of ground shaking from earthquakes (and explosions) are needed for seismic hazard analysis for buildings, nuclear power plants, and critical infrastructure at the NNSS. We are comparing existing 1-D and new 3-D geophysical methods for estimating the shear-wave velocity structure in the upper 30 meters of the ground surface (Vs30), which plays a major role in ground motion amplification and seismic response of buildings. We evaluate the performance of these methodologies at the U1a Complex at the NNSS and develop simple 1-D and high-resolution 3-D Vs30 models. We then emplace these high-resolution models into a background seismic velocity model. We will collaborate with Lawrence Livermore National Laboratory (LLNL) to conduct numerical modeling of the ground shaking at the NNSS using their high-performance computing technology and state-of-the-art ground motion simulation methodology. The primary work that was completed in FY 2019 was to acquire the seismic systems and familiarize staff at the NNSS with their use. We also worked on developing a collection plan with the Device Assembly Facility (DAF) at the NNSS, but due to time constraints and other ongoing projects at the DAF, we had to use U1a as a backup. We were able to coordinate the seismic survey, and we will complete the collection of seismic data in FY 2020. Additionally, during FY 2019, we completed the geologic framework model (GFM) for the U1a Complex and modeled the Yucca fault. LLNL worked with us through FY 2019 to prepare the data files for their modeling software and tested the software for reliability. In FY 2020 we will develop the end-to-end capability so that any facility could easily be modeled and the expected shaking from a local earthquake understood. The work in FY 2020 will include building fault models from the GFM and finalizing the velocity model analysis. The final simulations will be run for multiple rupture models, and final assessments will demonstrate the seismic hazard at the U1a Complex.
Earth-based gravitational wave detectors suffer from the need to support the large antenna masses against the earth's gravity without transmitting a significant amount of seismic noise. Passive vibration isolation is difficult to achieve below 1 Hz on the earth. Vibration-free space environment thus gives an opportunity to extend the frequency window of gravitational wave detection to ultralow frequencies. The weightless condition of a space laboratory also enables construction of a highly symmetric multimode antenna which is capable of resolving the direction of the source and the polarization of the incoming wave without resorting to multiantenna coincidence. Two types of earth-orbiting resonant-mass gravitational wave detectors are considered. One is a skyhook gravitational wave detector, proposed by Braginsky and Thorne (1985). The other is a spherical detector, proposed by Forward (1971) and analyzed by Wagoner and Paik (1976).
Seismic data fron the four stations of the Apollo passive seismic network have been analyzed to obtain the velocity structure of the moon. Analysis of body wave phases from artificial impacts of known impact time and position yields a crustal section. In the Mare Cognitum region the crust is about 60 km thick and is layered. In the 20-km-thick upper layer, velocity gradients are high and microcracks may play an important role. The 40-km-thick lower layer has a nearly constant 6.8-km/sec velocity. There may be a thin high-velocity layer present beneath the crust. The determination of seismic velocities in the lunar mantle is attempted by using natural impacts and deep moonquakes. The simplest model that can be proposed for the mantle consists of a 'lithosphere' overlying an 'asthenosphere'.
Grooved and hilly terrains occur at the antipode of major basins on the moon (Imbrium, Orientale) and Mercury (Caloris). Such terrains may represent extensive landslides and surface disruption produced by impact-generated P-waves and antipodal convergence of surface waves. Order-of-magnitude calculations for an Imbrium-size impact on the moon indicate P-wave-induced surface displacements of 10 m at the basin antipode that would arrive prior to secondary ejecta. Comparable surface waves would arrive subsequent to secondary ejecta impacts beyond 1000 km and would increase in magnitude as they converge at the antipode. Other seismically induced surface features include: subdued, furrowed crater walls produced by landslides and concomitant secondary impacts; emplacement and leveling of light plains units owing to seismically induced 'fluidization' of slide material; and perhaps the production and enhancement of deep-seated fractures that led to the concentration of farside lunar maria in the Apollo-Ingenii region.
Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. Here, in this study, we introduce a new end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub-networks together with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.
Here, we perform seismic attenuation tomography of the shallow structure for the San Jacinto Fault in the Ramona Reservation of southern California. The study uses ambient seismic noise recorded by a linear array of 65 3-C sensors across the fault. We extract amplitude decay information, with uncertainty, from noise interferometry functions. To account for strong heterogeneities in the complex shallow fault zone structure, we apply a frequency-dependent amplitude correction for focusing/defocusing effects using inverted phase velocity maps obtained by solving the transport equation. We then estimate the attenuation structure based on the linear station-triplet method for both Love and Rayleigh waves. The attenuation tomography indicates strong attenuation correlated with known San Jacinto Fault surface traces. The Love wave attenuation tomography reveals an asymmetric damage zone that exists primarily on the fault side with faster seismic velocity, consistent with earthquake ruptures on a fault bimaterial interface with preferred propagation direction to the northwest.
Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.
The terrestrial planets aggregated essentially from small particles, to begin as solid cool bodies with the same general compositions, and there is no possibility of an iron-core developing within any of them at any stage. Their differing internal and surface properties receive ready explanation from their different masses which determine whether the pressures within are sufficient to bring about phase-changes. The claim that the terrestrial core can be identified by means of shock-wave data as nickel-iron is based on theoretical misconception, whereas the actual seismic data establish an uncompressed-density value much lower than any such mixture could have. The onset of the Ramsey phase-change in the earth takes the form of a rapid initial collapse to produce a large core in metallic state which thereafter continues to grow secularly as a result of radioactive heating and leads to reduction of surface-area at long last adequate to account for folded and thrusted mountain-building.
Review of some of the problems associated with the determination of epicenters and mechanisms of moonquakes with the aid of the four seismic stations now operating on the moon. No decoupling of compressional, transverse, and surface waves takes place there, no clear first arrivals can be found, and coherence between vertical and horizontal components on a seismograph stationed on the moon is poor. New techniques for locating epicenters are presented.
Mechanisms and depths of 40 earthquakes on major transforms along the Mid-Atlantic Ridge are studied in order to identify events that deviate from the transform-parallel strike-slip motion. Long and short period waves and Rayleigh wave spectral amplitudes are used to analyze focal mechanisms, depths, source time functions, and seismic moments of earthquakes. The relationship between centroid depths and transform thermal structures is examined. The data reveal that transform earthquake centroid depths occur above the predicted 400 C isotherms and the oceanic intraplate depths extend to the 750 C isotherm. Slip rates inferred from seismic moment releases are compared to those predicted by plate motions and good correlation is detected. The difference in the centroid depths of transform and interplate seismicity indicates transforms are either weaker or higher temperatures than expected.
Recent tomographic views of mantle are used to estimate corresponding lateral variations in effective viscosity under the assumption that temperature fluctuations about spherically symmetric mean values are the sole source of shear wave velocity anomalies.
The largest source of uncertainty in any source inversion is the velocity model used in the transfer function that relates observed ground motion to the seismic moment tensor. However, standard inverse procedure often does not quantify uncertainty in the seismic moment tensor due to error in the Green’s functions from uncertain event location and Earth structure. Here, we incorporate this uncertainty into an estimation of the seismic moment tensor using a data-derived distribution of velocity models based on complementary geophysical data sets, including thickness constraints, velocity profiles, gravity data, surface-wave group velocities, and regional body-wave travel times. The data-derived distribution of velocity models is then used as a prior distribution of Green’s functions for use in Bayesian inference of an unknown seismic moment tensor using regional and teleseismic-P waveforms. The use of multiple data sets is important for gaining resolution to different components of the moment tensor. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and interpreted in terms of the most probable source type.
The Earth’s Coriolis force has been well–known to impact surface waves and normal modes, which is essential to accurately interpret these waves. However, the Coriolis force on body waves has been assumed to be negligible and mostly ignored. It has been previously shown that the Coriolis force impacts polarizations of shear waves, whereas the wavefronts remain unaffected. We expand on the potential influences of Earth’s Coriolis force on shear–wave polarization measurements by conducting 3D numerical simulations for elastic waves generated by earthquake and explosive sources in a radially symmetric, and 3D mantle and crustal models. The Coriolis force can produce polarization anomalies of mantle shear waves up to 7° and core phases, such as SKS and SKKS, up to 4°. Uncorrected shear–wave polarizations due to the Coriolis force can cause an additional source of error (5°–10° in fast direction, and 0.2–0.3 s delay time depending on the method and seismic phase), inaccurate interpretation of station misalignments, and imprecise estimates of the core–mantle boundary topography. We show how to correct for the Coriolis force on teleseismic shear waves using 1D ray tracing for well–isolated phases. Here, we recommend the use of full waveform simulations to accurately account for earthquake sources parameters, poorly isolated phases that could include interfering phase arrivals within the measurement time window, and the effect of the Coriolis force on the polarizations of shear waves.
Ionospheric disturbances caused by the earthquake of a relatively small and large epicentral distance have been detected by a network of HF-Doppler sounders in central Japan and Kyoto station, respectively. The HF-Doppler data of a small epicentral distance, together with the seismic data, have been used to formulate a mechanism whereby ionospheric disturbances are produced by the Urakawa-Oki earthquake in Japan. Comparison of the dynamic spectra of these data has revealed experimentally that the atmosphere acts as a low-pass filter for upward-propagating acoustic waves. By surveying the earthquakes for which the magnitude M is larger than 6.0, researchers found the ionospheric effect in 16 cases of 82 seismic events. As almost all these effects have occurred in the daytime, it is considered that it may result from the filtering effect of the upward-propagating acoustic waves.