The complex dynamics of earthquake fault systems: new approaches to forecasting and nowcasting of earthquakes

Abstract

Charles Richter's observation that 'only fools and charlatans predict earthquakes,' reflects the fact that despite more than 100 years of effort, seismologists remain unable to do so with reliable and accurate results. Meaningful prediction involves specifying the location, time, and size of an earthquake before it occurs to greater precision than expected purely by chance from the known statistics of earthquakes in an area. In this context, 'forecasting' implies a prediction with a specification of a probability of the time, location, and magnitude. Two general approaches have been used. In one, the rate of motion accumulating across faults and the amount of slip in past earthquakes is used to infer where and when future earthquakes will occur and the shaking that would be expected. Because the intervals between earthquakes are highly variable, these long-term forecasts are accurate to no better than a hundred years. They are thus valuable for earthquake hazard mitigation, given the long lives of structures, but have clear limitations. The second approach is to identify potentially observable changes in the Earth that precede earthquakes. Various precursors have been suggested, and may have been real in certain cases, but none have yet proved to be a general feature preceding all earthquakes or to stand out convincingly from the normal variability of the Earth's behavior. However, new types of data, models, and computational power may provide avenues for progress using machine learning that were not previously available. At present, it is unclear whether deterministic earthquake prediction is possible. The frustrations of this search have led to the observation that (echoing Yogi Berra) 'it is difficult to predict earthquakes, especially before

Estimating the Epicenter of a Future Strong Earthquake in Southern California, Mexico, and Central America by Means of Natural Time Analysis and Earthquake Nowcasting

“…When focusing in the region N W (shown in Figure 1 ), more than twenty-eight EQs with a magnitude M have occurred there since 1989. In the present paper, we employ the natural time analysis (NTA) [ 5 , 6 , 7 , 8 , 9 , 10 ] and earthquake nowcasting (EN) [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] aiming at forecasting the epicenter of such a strong future EQ.…”

Thermolab: A Thermodynamics Laboratory for Nonlinear Transport Processes in Open Systems

“…One of the primary goals in Earth Science is to understand the physical processes that govern natural phenomena. This will facilitate prediction of potential hazardous events, such as earthquakes, volcanic eruptions, and related threats such as tsunamis (Fagents et al., 2013; Geller, 1997; Rundle et al., 2021; Ulrich et al., 2019). Predicting causes and effects of climate change, the reduction of global warming by storing CO 2 or the safety of storing nuclear waste, strongly rely on the understanding of underlying coupled physical processes (Orr, 2018; X. Zhang et al., 2022).…”

InSAR Data Analysis using Deep Neural Networks

“…Occurrence of earthquakes is commonly explained by the theory of plate tectonics [12]. The spatiotemporal dataset, that are used to understand earthquake dynamics and consequent seismic hazards, primarily include tabular seismicity data and Earth's surface displacement from space geodesy [9,11]. In recent years, the dependency on the dense InSAR data has increased manifold due to the inadequate resolution of global navigation satellite system (GNSS) stations [2].…”

“…As a consequence, several methods have been proposed to deal with InSAR data processing in order to infer the underlying crustal structure, fault kinematics, and earthquake potential in a defined region [4,7]. Moreover, due to inherent diversity and exponentially rising volume of SAR interferograms, machine learning models may be employed to extract meaningful information in terms of "hidden-variables" [11]. Such models use feature vectors to simultaneously process millions of data points and they are less sensitive to human biases [1].…”