Improving earthquake prediction accuracy in Los Angeles with machine learning

This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy. By synthesizing existing research and integrating novel predictive features, we developed a robust subset capable of estimating the maximum potential earthquake magnitude. Our standout achievement is the creation of a feature set that, when applied with the Random Forest machine learning model, achieves a high accuracy in predicting the maximum earthquake category within the next 30 days. Among sixteen evaluated machine learning algorithms, Random Forest proved to be the most effective. Our findings underscore the transformative potential of machine learning and neural networks in enhancing earthquake prediction accuracy, offering significant advancements in seismic risk management and preparedness for Los Angeles.

Reports on progress in physics the complex dynamics of earthquake fault systems: new approaches to forecasting and nowcasting of earthquakes

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 they happen.' However, because success would be of enormous societal benefit, the search for methods of earthquake prediction and forecasting will likely continue. In this review, we note that the focus is on anticipating the earthquake rupture before it occurs, rather than characterizing it rapidly just after it occurs. The latter is the domain of earthquake early warning, which we do not treat in detail here, although we include a short discussion in the machine learning section at the end.