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Earthquake prediction is discussed from the point of view of a new development in geodesy known as space geodesy, which involves the use of extraterrestrial sources or reflectors to measure earth-based distances. Space geodesy is explained, and its relation to terrestrial geodesy is examined. The characteristics of earthquakes are reviewed, and the ways that they can be exploited by space geodesy to predict earthquakes is demonstrated.
Research conducted for determining the location of stations for measuring crustal dynamics and predicting earthquakes is discussed. Procedural aspects, the extraregional kinematic tendencies, and regional tectonic deformation mechanisms are described.
The state of the art in earthquake prediction is discussed. Short-term prediction based on seismic precursors, changes in the ratio of compressional velocity to shear velocity, tilt and strain precursors, electromagnetic precursors, hydrologic phenomena, chemical monitors, and animal behavior is examined. Seismic hazard assessment is addressed, and the applications of dynamical systems to earthquake prediction are discussed.
Geodetic laser system for earthquake prediction and engineering research
Earthquakes represent one of our greatest natural hazards and in recent years human induced seismicity is adding to the threat. Even a modest improvement in the ability to forecast devastating large earthquakes or smaller shallow events associated with fluid injection could save thousands of lives and billions of dollars. Current efforts to forecast earthquakes are limited by knowledge of earthquake physics and hampered by a lack of reliable lab or field observations. However, recent work has provided a critical opportunity for advancement. We have found: 1) clear and consistent precursors prior to earthquake-like failure in the laboratory and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the fault zone stress state, the failure time and in some cases the magnitude of lab earthquakes. In addition, the observations include clear precursors to failure in the form of changes in fault zone properties prior to lab earthquakes. Precursors have been observed in previous laboratory studies but their origin is poorly understood and their possible connection to ML based earthquake prediction is unknown. The work conducted under our project has dramatically expanded these efforts. We have developed an integrated data science approach to illuminate the physics of earthquake precursors and lab earthquake prediction. Our work has accelerated the development of ML, artificial intelligence (AI), and related data science approaches by providing massive data sets that are tightly connected to critical scientific problems and by bringing together leading subject matter experts and data scientists. Earthquake physics involves phenomena that are far from equilibrium. Our work has leveraged data science methods to illuminate these phenomena and investigate how they relate to earthquake prediction. In addition to a large database with many types of labeled events that is available to everyone, our work has advanced the fundamental understanding of seismic forecasting, earthquake physics, and fault rheology
The Satellite Telemetry Earthquake Monitoring Program was started to evaluate the applicability of satellite relay telemetry in the collection of seismic data from a large number of dense seismograph clusters laid out along the major fault systems of western North America. Prototype clusters utilizing phone-line telemetry were then being installed by the National Center for Earthquake Research in 3 regions along the San Andreas fault in central California; and the experience of installing and operating the clusters and in reducing and analyzing the seismic data from them was to provide the raw materials for evaluation in the satellite relay telemetry project. The principal advantages of the satellite relay system over commercial telephone or microwave systems were: (1) it could be made less prone to massive failure during a major earthquake; (2) it could be extended readily into undeveloped regions; and (3) it could provide flexible, uniform communications over large sections of major global tectonic zones. Fundamental characteristics of a communications system to cope with the large volume of raw data collected by a short-period seismograph network are discussed.
During the past year, the grant supported research on several aspects of crustal deformation. The relation between earthquake displacements and fault dimensions was studied in an effort to find scaling laws that relate static parameters such as slip and stress drop to the dimensions of the rupture. Several implications of the static relations for the dynamic properties of earthquakes such as rupture velocity and dynamic stress drop were proposed. A theoretical basis for earthquake related phenomena associated with slow rupture growth or propagation, such as delayed multiple events, was developed using the stress intensity factor defined in fracture mechanics and experimental evidence from studies of crack growth by stress corrosion. Finally, extensive studies by Japanese geologists have established the offset across numerous faults in Japan over the last one hundred thousand years. These observations of intraplate faulting are being used to establish the spatial variations of the average strain rate of subregions in southern Japan.
From seismicity studies, evidence is presented for several aspects of plate-tectonic theory, including ideas of sea-floor spreading, transform faulting and underthrusting of the lithosphere in island arcs. Recent advances in seismic instrumentation, the use of computers in earthquake location, and the installation of local networks of instruments are shown to have vastly increased the data available for seismicity studies. It is pointed out that most of the world's earthquakes are located in very narrow zones along active plate margins and are intimately related to global processes in an extremely coherent manner. Important areas of uncertainty calling for further studies are also pointed out.
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Projects studying different aspects of crustal deformation are discussed. The rifting process at the plate boundary in northern Iceland was investigated using combined seismic, tilt, and displacement measurements. Stresses acting on the Alpine fault, New Zealand, were modeled based on observations of regional variation in metamorphism and argon loss. The relationship between the stress pattern shown by intraplate seismicity and possible stresses on plates arising from litohspheric motion over the asthenosphere was investigated. Small earthquakes in Southern California were studied to determine if subregions of uniform strain release exist and if interaction occurs between faults.
Microearthquake studies of the southern section of the San Andreas fault system were conducted in order to elucidate the role of known but little studied complications in the fault system which could affect the SAFE measurement. Data reduction from microearthquake studies in Baja California, Mexico, was completed. In addition, styles of deformation on the Alpine fault in New Zealand and on the San Andreas fault were compared. Sections of these faults with comparable physical characteristics seem to deform in a similar manner, indicating that deformation style is controlled by certain fundamental relations and will continue in the future. The geology of eastern New Guinea is also discussed. Deformation in the area is proposed to be related to collision of a series of island arcs with central New Guinea, which is part of the Australian continent. Among other unusual properties, eastern New Guinea contains the highest anomaly of the gravimetrically determined geoid based on GEM 6 and surface gravity data.
Abstract Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction.
Relatively little is known about the cavity between the Earth and the ionosphere, which opens opportunities for technological advances and unique ideas. One effective means to study this cavity is with extremely low frequency (ELF) antennas. Possible applications of these antennas are global weather prediction, earthquake prediction, planetary exploration, communication, wireless transmission of power, or even a free energy source. The superconducting quantum interference device SQUID) and the coil antenna are the two most acceptable receivers discovered for picking up ELF magnetic fields. Both antennas have the potential for size reduction, allowing them to be portable enough for access to space and even for personal ware. With improvements of these antennas and signal processing, insightful analysis of Schumann resonance (SR) can give the science community a band of radio frequency (RF) signals for improving life here on Earth and exploring beyond.
Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.
A number of observers claim to have seen thermal anomalies prior to earthquakes, but subsequent analysis by others has failed to produce similar findings. What exactly are these anomalies? Might they be useful for earthquake prediction? It is the purpose of this study to determine if thermal anomalies can be found in association with known earthquakes by systematically co-registering weather satellite images at the sub-pixel level and then determining if statistically significant responses occurred prior to the earthquake event. A new set of automatic co-registration procedures was developed for this task to accommodate all properties particular to weather satellite observations taken at night, and it relies on the general condition that the ground cools after sunset. Using these procedures, we can produce a set of temperature-sensitive satellite images for each of five selected earthquakes (Algeria 2003; Bhuj, India 2001; Izmit, Turkey 2001; Kunlun Shan, Tibet 2001; Turkmenistan 2000) and thus more effectively investigate heating trends close to the epicenters a few hours prior to the earthquake events. This study will lay tracks for further work in earthquake prediction and provoke the question of the exact nature of the thermal anomalies.
Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.
An analysis of geodetic data in the vicinity of the Crustal Dynamics Program (CDP) site at Vandenberg Air Force Base (VNDN) is presented. The utility of space-geodetic data in the monitoring of transient strains associated with earthquakes in tectonically active areas like California is investigated. Particular interest is in the possibility that space-geodetic methods may be able to provide critical new data on deformations precursory to large seismic events. Although earthquake precursory phenomena are not well understood, the monitoring of small strains in the vicinity of active faults is a promising technique for studying the mechanisms that nucleate large earthquakes and, ultimately, for earthquake prediction. Space-geodetic techniques are now capable of measuring baselines of tens to hundreds of kilometers with a precision of a few parts in 108. Within the next few years, it will be possible to record and analyze large-scale strain variations with this precision continuously in real time. Thus, space-geodetic techniques may become tools for earthquake prediction. In anticipation of this capability, several questions related to the temporal and spatial scales associated with subseismic deformation transients are examined.
MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need for benchmark carpentry for scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection called cloudmesh with its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).