Earthquake predictions have been successfully achieved
the verdict
REFUTED
the evidence says no
refutedsupported
the weight of evidence
2 sources for · 7 against
Scholarly consensus and historical evaluations establish that demonstrably successful and reliable earthquake predictions have not been achieved, despite ongoing experimental research.
This study considers the problem of improving the accuracy of earthquake forecasting in Kazakhstan using deep learning methods. Special attention is paid to forecasting in zones with increased seismic activity, which is a unique feature of the regional plan. The main goal of the work is to develop a model based on the architecture of deep neural networks with direct propagation of signals to analyze historical data containing the time of occurrence, geographic coordinates and magnitude of earthquakes. This model is used as a basis for classification and regression, allowing to evaluate the level of agreement between the predicted values and those obtained. Evidence of earthquake prediction capability is demonstrated using this methodology, achieving 86% accuracy in magnitude classification, and a Mean Squared Error of 0.22 in forecasting the magnitude itself in regression utilizing computational power as well as advanced neural network techniques. The significance of this study is that it contributes to the development of processes and methods that promote the application of deep learning in seismology to improve the accuracy and efficiency of diagnosis and prevention of natural disasters, also opens new perspectives in this direction.
Following the 2009 L'Aquila earthquake, the Dipartimento della Protezione Civile Italiana (DPC), appointed an International Commission on Earthquake Forecasting for Civil Protection (ICEF) to report on the current state of knowledge of short-term prediction and forecasting of tectonic earthquakes and indicate guidelines for utilization of possible forerunners of large earthquakes to drive civil protection actions, including the use of probabilistic seismic hazard analysis in the wake of a large earthquake. The ICEF reviewed research on earthquake prediction and forecasting, drawing from developments in seismically active regions worldwide. A prediction is defined as a deterministic statement that a future earthquake will or will not occur in a particular geographic region, time window, and magnitude range, whereas a forecast gives a probability (greater than zero but less than one) that such an event will occur. Earthquake predictability, the degree to which the future occurrence of earthquakes can be determined from the observable behavior of earthquake systems, is poorly understood. This lack of understanding is reflected in the inability to reliably predict large earthquakes in seismically active regions on short time scales. Most proposed prediction methods rely on the concept of a diagnostic precursor; i.e., some kind of signal observable before earthquakes that indicates with high probability the location, time, and magnitude of an impending event. Precursor methods reviewed here include changes in strain rates, seismic wave speeds, and electrical conductivity; variations of radon concentrations in groundwater, soil, and air; fluctuations in groundwater levels; electromagnetic variations near and above Earth's surface; thermal anomalies; anomalous animal behavior; and seismicity patterns. The search for diagnostic precursors has not yet produced a successful short-term prediction scheme. Therefore, this report focuses on operational earthquake forecasting as the principle means for gathering and disseminating authoritative information about time-dependent seismic hazards to help communities prepare for potentially destructive earthquakes. On short time scales of days and weeks, earthquake sequences show clustering in space and time, as indicated by the aftershocks triggered by large events. Statistical descriptions of clustering explain many features observed in seismicity catalogs, and they can be used to construct forecasts that indicate how earthquake probabilities change over the short term. Properly applied, short-term forecasts have operational utility; for example, in anticipating aftershocks that follow large earthquakes. Although the value of long-term forecasts for ensuring seismic safety is clear, the interpretation of short-term forecasts is problematic, because earthquake probabilities may vary over orders of magnitude but typically remain low in an absolute sense (< 1% per day). Translating such low-probability forecasts into effective decision-making is a difficult challenge. Reports on the current utilization operational forecasting in earthquake risk management were compiled for six countries with high seismic risk: China, Greece, Italy, Japan, Russia, United States. Long-term models are currently the most important forecasting tools for civil protection against earthquake damage, because they guide earthquake safety provisions of building codes, performance-based seismic design, and other risk-reducing engineering practices, such as retrofitting to correct design flaws in older buildings. Short-term forecasting of aftershocks is practiced by several countries among those surveyed, but operational earthquake forecasting has not been fully implemented (i.e., regularly updated and on a national scale) in any of them. Based on the experience accumulated in seismically active regions, the ICEF has provided to DPC a set of recommendations on the utilization of operational forecasting in Italy, which may al
Abstract There are many reports on the occurrence of anomalous changes in the ionosphere prior to large earthquakes. However, whether or not these changes are reliable precursors that could be useful for earthquake prediction is controversial within the scientific community. To test a possible statistical relationship between ionospheric disturbances and earthquakes, we compare changes in the total electron content (TEC) of the ionosphere with occurrences of 1279 M ≥ 6.0 earthquakes globally for 2000–2014. We use TEC data from the global ionosphere map (GIM) and an earthquake list declustered for aftershocks. For each earthquake, we look for anomalous changes in GIM‐TEC within 2.5° latitude and 5.0° longitude of the earthquake location (the spatial resolution of GIM‐TEC). Although case studies of individual earthquakes that used short periods of data sometimes yield GIM‐TEC changes that were considered possible earthquake‐related phenomena, our analysis has not found any statistically significant changes prior to earthquakes when considering all 1279 earthquakes together. Thus, we have found no evidence that would suggest that monitoring changes in GIM‐TEC might be useful for predicting earthquakes.
Obtaining high-quality measurements close to a large earthquake is not easy: one has to be in the right place at the right time with the right instruments. Such a convergence happened, for the first time, when the 28 September 2004 Parkfield, California, earthquake occurred on the San Andreas fault in the middle of a dense network of instruments designed to record it. The resulting data reveal aspects of the earthquake process never before seen. Here we show what these data, when combined with data from earlier Parkfield earthquakes, tell us about earthquake physics and earthquake prediction. The 2004 Parkfield earthquake, with its lack of obvious precursors, demonstrates that reliable short-term earthquake prediction still is not achievable. To reduce the societal impact of earthquakes now, we should focus on developing the next generation of models that can provide better predictions of the strength and location of damaging ground shaking.
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.
Earthquake forecasting is considered to be the “holy grail” in seismology. Many forecasting methods have been suggested over decades. Some of them are based on geophysical observations related to the preparatory process of an event, and others are less obviously associated with the physical properties of an earthquake. One of the most prominent examples of the latter is the forecast of large earthquakes based on anomalous animal behavior before the event (see Woith et al., 2018 and references therein). Many authors claim to have been successful in predicting single events. However, the rules defining a “successful prediction” are often ill defined. Therefore, a proper evaluation of the predictive power of a proposed precursor should include at least the following pieces of information: (a) the number of successful predictions (earthquake with precursor), (b) the number of false alarms (precursor without earthquake), and (c) the number of failures-to-predict (earthquake without precursor). To determine these numbers, the alarm volume within time, space, and “strength” (e.g., magnitude range or ground motion range) has to be well defined by the prediction scheme. A flexible and easy tool for this purpose is the Molchan (or error) diagram (Molchan, 1990). Here, we apply this technique in order to study whether or not the anticipatory patterns between animal and seismic activity reported by Wikelski et al. (2020) (hereinafter referred to as WK2020) have significant forecasting skills. We restrict our analysis to a statistical evaluation of the forecasting power and refrain from commenting on the plausibility of such patterns or on the modeling technique used to generate the proposed precursor. In other words, we consider the time series of the proposed precursory signal without questioning its origin. For this analysis, we use the data, which have been provided online by WK2020. Received: 10 September 2020 | Accepted: 12 October 2020 DOI: 10.1111/eth.13105
question whether it was even possible. Demonstrably successful predictions of large earthquakes have not occurred, and the few claims of success are controversial
Earthquake prediction is an operational objective within the broader framework of earthquake forecasting, specifically representing a forecast where uncertainty and error margins are sufficiently minimized to allow decision makers to implement drastic or immediate public safety measures. It is traditionally defined as the specification of the time, location, and magnitude of future earthquakes wit
Predictions are deemed significant if they can be shown to be successful beyond random chance. Therefore, methods of statistical hypothesis testing are used to determine the probability that an earthquake such as is predicted would happen anyway (the null hypothesis). The predictions are then evaluated by testing whether they correlate with actual earthquakes better than the null hypothesis.
In many instances, however, the statistical nature of earthquake occurrence is not simply homogeneous. Clustering occurs in both space and time. In southern California about 6% of M≥3.0 earthquakes are "followed by an earthquake of larger magnitude within 5 days and 10 km." In central Italy 9.5% of M≥3.0 earthquakes are followed by a larger event within 48 hours and 30 km. While such statistics are not satisfactory for purposes of prediction (giving ten to twenty false alarms for each successful prediction) they will skew the results of any analysis that assumes that earthquakes occur randomly in time, for example, as realized from a Poisson process. It has been shown that a "naive" method based solely on clustering can successfully predict about 5% of earthquakes; "far better than 'chance'".
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In the span of the first few years after Japan’s defeat in World War II, five of Japan’s leading earth scientists came forward to warn the nation that major earthquakes would soon occur. They (almost) never did. This article focuses on those predictions to highlight the debates that shaped early postwar efforts in Japan to make scientists, and earth scientists in particular, guardians of the public’s safety. It draws on multiple archival collections, participant accounts and popular media coverage to explore the tensions between individual scientists and newly formed, officially sanctioned bod
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