Limiting the effects of earthquakes on gravitational-wave interferometers

Ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to high-magnitude teleseismic events, which can interrupt their operation in science mode and significantly reduce the duty cycle. It can take several hours for a detector to stabilize enough to return to its nominal state for scientific observations. The down time can be reduced if advance warning of impending shaking is received and the impact is suppressed in the isolation system with the goal of maintaining stable operation even at the expense of increased instrumental noise. Here we describe an early warning system for modern gravitational-wave observatories. The system relies on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). Hypocenter and magnitude information is generally available in 5 to 20 minutes of a significant earthquake depending on its magnitude and location. The alerts are used to estimate arrival times and ground velocities at the gravitational-wave detectors. In general, 90\% of the predictions for ground-motion amplitude are within a factor of 5 of measured values. The error in both arrival time and ground-motion prediction introduced by using preliminary, rather than final, hypocenter and magnitude information is minimal. By using a machine learning algorithm, we develop a prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could prevent interruption of operation from 40-100 earthquake events in a 6-month time-period

Satellite SAR Interferometry for Earth’s Crust Deformation Monitoring and Geological Phenomena Analysis

Synthetic aperture radar interferometry (InSAR) and the related processing techniques provide a unique tool for the quantitative measurement of the Earth’s surface deformation associated with certain geophysical processes (such as volcanic eruptions, landslides and earthquakes), thus making possible long-term monitoring of surface deformation and analysis of relevant geodynamic phenomena. This chapter provides an application-oriented perspective on the spaceborne InSAR technology with emphasis on subsequent geophysical investigations. First, the fundamentals of radar interferometry and differential interferometry, as well as error sources, are briefly introduced. Emphasis is then placed on the realistic simulation of the underlying geophysics processes, thus offering an unfolded perspective on both analytical and numerical approaches for modeling deformation sources. Finally, various experimental investigations conducted by acquiring SAR multitemporal observations on areas subject to deformation processes of particular geological interest are presented and discussed

Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture

Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering

We have developed a fast method that allowed us to automatically detect and denoise microseismic phase arrivals from 3C multichannel data. The method is a two-step process. First, the detection is carried out by means of a pattern recognition strategy that seeks plausible hyperbolic phase arrivals immersed in noisy 3C multichannel data. Then, the microseismic phase arrivals are denoised and reconstructed using a reduced-rank approximation of the singular value decomposition of the data along the detected phase arrivals in the context of a deflation procedure that took into account multiple arrivals and/or phases. For the detection, we have defined an objective function that measured the energy and coherence of a potential microseismic phase arrival along an apex-shifted hyperbolic search window. The objective function, which was maximized using very fast simulated annealing, was based on the energy of the average signal and depended on the source position, receivers geometry, and velocity. In practice, the detection process did not require any a priori velocity model, leading to a fast algorithm that can be used in real time, even when the underlying velocity model was not constant. The reduced-rank filtering coupled with a crosscorrelation-based synchronization strategy allowed us to extract the most representative waveform for all the individual traces. Tests using synthetic and field data have determined the reliability and effectiveness of the proposed method for the accurate detection and denoising of 3C multichannel microseismic events under noisy conditions. Two confidence indicators to assess the presence of an actual phase arrival and the reliability of the denoised individual wave arrivals were also developed.Facultad de Ciencias Astronómicas y Geofísica

Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering

We have developed a fast method that allowed us to automatically detect and denoise microseismic phase arrivals from 3C multichannel data. The method is a two-step process. First, the detection is carried out by means of a pattern recognition strategy that seeks plausible hyperbolic phase arrivals immersed in noisy 3C multichannel data. Then, the microseismic phase arrivals are denoised and reconstructed using a reduced-rank approximation of the singular value decomposition of the data along the detected phase arrivals in the context of a deflation procedure that took into account multiple arrivals and/or phases. For the detection, we have defined an objective function that measured the energy and coherence of a potential microseismic phase arrival along an apex-shifted hyperbolic search window. The objective function, which was maximized using very fast simulated annealing, was based on the energy of the average signal and depended on the source position, receivers geometry, and velocity. In practice, the detection process did not require any a priori velocity model, leading to a fast algorithm that can be used in real time, even when the underlying velocity model was not constant. The reduced-rank filtering coupled with a crosscorrelation-based synchronization strategy allowed us to extract the most representative waveform for all the individual traces. Tests using synthetic and field data have determined the reliability and effectiveness of the proposed method for the accurate detection and denoising of 3C multichannel microseismic events under noisy conditions. Two confidence indicators to assess the presence of an actual phase arrival and the reliability of the denoised individual wave arrivals were also developed.Facultad de Ciencias Astronómicas y Geofísica

Inversion of Seismic Anisotropic Parameters Using Very Fast Simulated Annealing with Application to Microseismic Event Location

The study and interpretation of hydraulically stimulated regions, such as certain unconventional hydrocarbon reservoirs (e.g. Vaca Muerta Formation, Neuquén, Argentina), requires the accurate location of the induced microseismic events. The localization is carried out by means of the analysis of the travel times of the generated compressional and shear seismic waves from the unknown event position to a set of geophones, usually located in a nearby monitoring well. The accuracy of the localization, and thus the characterization of the fracturing process, can be strongly affected by the available seismic velocity model, from which only estimates are known. Also, the underlying medium usually shows an anisotropic behavior, meaning that the velocities of the seismic waves depend on the propagation direction. Therefore, knowledge of the parameters that characterize the anisotropy and an appropriate calibration of the velocities can reduce the errors in the localization of the microseismic events. In this paper we propose a strategy to simultaneously calibrate the velocity model and invert the anisotropy parameters from three-component microseismic data. The strategy relies on the hypothesis that the subsurface is composed of a finite number of horizontal layers with weak anisotropy, a widely used approximation that requires only three anisotropy parameters per layer. The differences between the observed and the calculated travel times, for a known seismic source, are quantified by means of an appropriate objective function that turns out to be non-linear and multimodal. For this reason, we minimize it using very fast simulated annealing (VFSA), a stochastic global optimization algorithm devised to find near-optimal solutions to hard optimization problems. Tests on synthetic data show that the proposed strategy can be used to effectively calibrate the seismic velocities and to provide appropriate estimates of the anisotropy parameters in spite of the severe non-uniqueness of the inverse problem at hand. Also, the stochastic nature of VFSA allows us to obtain the uncertainties of the solutions by repeating the inversion several times. Finally, by means of a simulated microseismic location example, we show the importance of having a well calibrated model to successfully estimate the locations of the hydraulically induced events.Facultad de Ciencias Astronómicas y GeofísicasConsejo Nacional de Investigaciones Científicas y Técnica

Inversion of Seismic Anisotropic Parameters Using Very Fast Simulated Annealing with Application to Microseismic Event Location

The study and interpretation of hydraulically stimulated regions, such as certain unconventional hydrocarbon reservoirs (e.g. Vaca Muerta Formation, Neuquén, Argentina), requires the accurate location of the induced microseismic events. The localization is carried out by means of the analysis of the travel times of the generated compressional and shear seismic waves from the unknown event position to a set of geophones, usually located in a nearby monitoring well. The accuracy of the localization, and thus the characterization of the fracturing process, can be strongly affected by the available seismic velocity model, from which only estimates are known. Also, the underlying medium usually shows an anisotropic behavior, meaning that the velocities of the seismic waves depend on the propagation direction. Therefore, knowledge of the parameters that characterize the anisotropy and an appropriate calibration of the velocities can reduce the errors in the localization of the microseismic events. In this paper we propose a strategy to simultaneously calibrate the velocity model and invert the anisotropy parameters from three-component microseismic data. The strategy relies on the hypothesis that the subsurface is composed of a finite number of horizontal layers with weak anisotropy, a widely used approximation that requires only three anisotropy parameters per layer. The differences between the observed and the calculated travel times, for a known seismic source, are quantified by means of an appropriate objective function that turns out to be non-linear and multimodal. For this reason, we minimize it using very fast simulated annealing (VFSA), a stochastic global optimization algorithm devised to find near-optimal solutions to hard optimization problems. Tests on synthetic data show that the proposed strategy can be used to effectively calibrate the seismic velocities and to provide appropriate estimates of the anisotropy parameters in spite of the severe non-uniqueness of the inverse problem at hand. Also, the stochastic nature of VFSA allows us to obtain the uncertainties of the solutions by repeating the inversion several times. Finally, by means of a simulated microseismic location example, we show the importance of having a well calibrated model to successfully estimate the locations of the hydraulically induced events.Facultad de Ciencias Astronómicas y GeofísicasConsejo Nacional de Investigaciones Científicas y Técnica