지진을 예측할 수 있을까? 과학 vs 신화

The Difference: Prediction vs Forecasting

In everyday language, "prediction" and "forecasting" are often used interchangeably, but in earthquake science they mean fundamentally different things. An earthquake predictionPrediction claims to specify exact time, place, and magnitude of a future earthquake — currently impossible. Forecasting provides probabilistic estimates of earthquake likelihood over time periods. specifies the location, magnitude, and time of a future earthquake with sufficient precision and reliability to be useful for evacuations or other concrete actions. An earthquake forecast, in contrast, specifies the probability of an earthquake exceeding a given magnitude in a given area over a given time period — a statistical statement, not a precise prediction. The distinction matters enormously for public policy. Governments cannot evacuate a city based on a 10 percent probability of a Mw 7.0 earthquake in the next decade. But they can — and do — design buildings, update land-use plans, and prepare emergency services based on probabilistic forecasts. As of the current state of science, reliable deterministic earthquake prediction in the operational sense does not exist. Probabilistic forecasting, however, is highly developed and forms the scientific basis for modern seismic hazard assessment.

Failed Prediction Attempts in History

The history of earthquake prediction is littered with claims that initially attracted attention but did not survive scientific scrutiny. The VAN method, developed by Greek researchers Varotsos, Alexopoulos, and Nomikos in the 1980s, claimed to predict earthquakes from anomalous electrical signals in the ground (seismic electric signals or SES). Despite initial enthusiasm, rigorous statistical evaluation showed no predictive skill beyond chance. Radon gas anomalies, groundwater level changes, unusual animal behavior, and electromagnetic anomalies have all been proposed as earthquake precursors at various times, but none has demonstrated consistent, reliable predictive power in controlled scientific tests. The one genuinely successful prediction in earthquake history — the 1975 Haicheng earthquake in China, where unusual animal behavior and ForeshockAn earthquake that occurs before the mainshock in the same region. Foreshocks can only be identified in retrospect — there is no reliable way to distinguish them from ordinary earthquakes beforehand. activity led to a successful evacuation — was followed one year later by the 1976 Tangshan earthquake (which killed an estimated 242,000 people) with no warning. The Haicheng success now appears to have been partly luck.

The Parkfield Experiment

The Parkfield segment of the San Andreas Fault in California was considered a prime candidate for earthquake prediction experiments in the 1980s, because it had apparently produced characteristic Mw 6 earthquakes at roughly 22-year intervals: in 1857, 1881, 1901, 1922, 1934, and 1966. Based on this pattern, a Mw 6 earthquake was predicted for approximately 1988, with a 95 percent confidence window extending to 1992. The earthquake finally occurred in 2004 — 12 years late. While the 2004 event was well-recorded by the dense monitoring network deployed in anticipation, its lateness demonstrated that even apparent periodicity in Earthquake ClusteringThe tendency for earthquakes to occur in clusters (mainshock-aftershock sequences or swarms) rather than randomly in time. Violates the common assumption of independent, random occurrence. cannot serve as the basis for operational prediction.

Why Earthquakes Are Fundamentally Unpredictable

Modern seismology suggests that deterministic earthquake prediction may be inherently impossible, not merely technically difficult. Earthquakes are the result of stick-slip friction on faults — a process with sensitive dependence on initial conditions. The state of stress on a fault is heterogeneous at all scales; small ForeshockAn earthquake that occurs before the mainshock in the same region. Foreshocks can only be identified in retrospect — there is no reliable way to distinguish them from ordinary earthquakes beforehand.s or slow-slip events that might seem to signal an impending rupture may alternatively arrest without generating a large event. The transition from stable sliding to dynamic rupture is a nonlinear, potentially chaotic process: tiny perturbations in fault stress — perhaps from small distant earthquakes, changes in groundwater pressure, or even ocean tidal loading — can either trigger or prevent a rupture. This sensitivity means that even with perfect knowledge of current fault stress, predicting the exact time of the next rupture may be physically impossible.

The Role of Foreshocks

ForeshockAn earthquake that occurs before the mainshock in the same region. Foreshocks can only be identified in retrospect — there is no reliable way to distinguish them from ordinary earthquakes beforehand.s — earthquakes that precede the mainshock on the same fault — occur before approximately 40–70 percent of large earthquakes, but they are only recognizable as foreshocks in hindsight. At the time they occur, there is no way to distinguish a foreshock from any other small earthquake. The probability that a small earthquake will be followed by a larger earthquake on the same fault is computable using statistical models (such as ETAS — the Epidemic-Type Aftershock Sequence model), and these models do produce short-term probability increases that can be communicated to emergency managers. But the absolute probability remains low enough that routine evacuations based on foreshock activity alone would cause enormous social disruption for very few genuine precursors.

Earthquake Forecasting: Probabilistic Approaches

While deterministic prediction has failed, probabilistic earthquake forecasting has matured into a rigorous, quantitative science. Probabilistic forecasting combines geological fault data (slip rates, recurrence intervals from PaleoseismologyThe study of prehistoric earthquakes through geological evidence such as fault trenches, uplifted terraces, and tsunami deposits. Extends the earthquake record back thousands of years.), seismological observations (historical catalogs, b-valueThe slope of the Gutenberg-Richter frequency-magnitude relationship. A b-value near 1.0 is typical; higher values indicate more small earthquakes relative to large ones. Changes may signal stress changes. analysis, Gutenberg-Richter LawA statistical law describing the relationship between earthquake frequency and magnitude: for each unit increase in magnitude, earthquakes become about 10 times less frequent. statistics), and geodetic data (strain rates from GPS) to estimate the probability of an earthquake exceeding a given magnitude in a given area over a given time period. The Working Group on California Earthquake Probabilities, for example, estimates that there is roughly a 60 percent probability of a Mw 6.7+ earthquake striking the San Francisco Bay Area in the next 30 years. These forecasts are updated as new data become available and are presented in probabilistic seismic hazard maps that inform building codes, insurance rates, and public policy.

Operational Earthquake Forecasting

A newer and more applied form of probabilistic forecasting is operational earthquake forecasting (OEF) — the real-time updating of earthquake probabilities following significant events, especially for aftershock sequences. After a major earthquake, the probability of damaging aftershocks is substantially elevated, following Omori's LawAn empirical law describing the decay rate of aftershock frequency over time: the rate of aftershocks decreases roughly as the inverse of time since the mainshock. in its time decay. Agencies including the USGS (United States Geological Survey)The primary US government agency responsible for monitoring earthquakes, operating the National Earthquake Information Center, and publishing real-time earthquake data worldwide., the Istituto Nazionale di Geofisica e Vulcanologia (INGV) in Italy, and GNS Science in New Zealand now issue regularly updated OEF products during significant aftershock sequences, providing emergency managers with probabilistic guidance on the elevated risk of additional damaging events. This represents a practical, scientifically defensible application of Earthquake ClusteringThe tendency for earthquakes to occur in clusters (mainshock-aftershock sequences or swarms) rather than randomly in time. Violates the common assumption of independent, random occurrence. statistics to real-world emergency management.

PSHA: The Current Best Practice

Probabilistic Seismic Hazard Analysis (PSHA)A method for quantifying earthquake hazard that considers all possible earthquake sources, magnitudes, and ground motion levels, expressing results as probability of exceeding specific shaking levels. — Probabilistic Seismic Hazard Analysis — is the gold standard for quantifying earthquake hazard for engineering and policy purposes. PSHA integrates over all possible earthquake sources (fault sources and area sources), all possible magnitudes on each source (Gutenberg-Richter LawA statistical law describing the relationship between earthquake frequency and magnitude: for each unit increase in magnitude, earthquakes become about 10 times less frequent. magnitude-frequency relationships), all possible distances from source to site, and ground motion prediction equations (empirical models relating magnitude and distance to peak ground acceleration, Peak Ground Acceleration (PGA)The maximum acceleration of the ground during an earthquake, measured in g (gravitational acceleration). A key parameter in earthquake engineering for designing structures., and spectral accelerations) to compute the probability that ground shaking will exceed any given level at a site in a given time period. The output is a hazard curve: for every shaking level, the annual probability of exceedance. PSHA results are expressed as the peak ground acceleration with a given probability of being exceeded in 50 years — for example, the 2 percent in 50 years (approximately 2,500-year return period) hazard level used in US building codes. This framework is fundamentally probabilistic, acknowledging the irreducible uncertainty in earthquake occurrence while still providing quantitative, decision-relevant hazard estimates.

The Future of Earthquake Science

The frontier of earthquake science is not deterministic prediction but rather the progressive reduction of uncertainty in probabilistic forecasts. Denser Seismic NetworkA coordinated group of seismograph stations that continuously monitor earthquake activity. The Global Seismographic Network (GSN) includes 150+ stations providing worldwide coverage.s and GPS arrays are improving our knowledge of fault behavior. Machine learning is uncovering subtle seismicity patterns that precede some large earthquakes, potentially providing modest short-term probability increases. New PaleoseismologyThe study of prehistoric earthquakes through geological evidence such as fault trenches, uplifted terraces, and tsunami deposits. Extends the earthquake record back thousands of years. studies are lengthening the earthquake record on poorly understood faults. Laboratory experiments on rock friction are revealing the micro-mechanical processes that control the transition from stable creep to dynamic rupture. None of this is likely to produce the kind of hour-specific, location-specific prediction that the public imagines when they ask "Can we predict earthquakes?" — but it is steadily improving the probabilistic tools that save lives through better-designed buildings, more targeted land-use planning, and smarter emergency preparedness.