The Earth as a living planet: human-type diseases in the earthquake preparation process

The new field of complex systems supports the view that a number of systems arising from disciplines as diverse as physics, biology, engineering, and economics may have certain quantitative features that are intriguingly similar. The earth is a living planet where many complex systems run perfectly without stopping at all. The earthquake generation is a fundamental sign that the earth is a living planet. Recently, analyses have shown that human-brain-type disease appears during the earthquake generation process. Herein, we show that human-heart-type disease appears during the earthquake preparation of the earthquake process. The investigation is mainly attempted by means of critical phenomena, which have been proposed as the likely paradigm to explain the origins of both heart electric fluctuations and fracture induced electromagnetic fluctuations. We show that a time window of the damage evolution within the heterogeneous Earth’s crust and the healthy heart’s electrical action present the characteristic features of the critical point of a thermal second order phase transition. A dramatic breakdown of critical characteristics appears in the tail of the fracture process of heterogeneous system and the injury heart’s electrical action. Analyses by means of Hurst exponent and wavelet decomposition further support the hypothesis that a dynamical analogy exists between the geological and biological systems under study.

💡 Research Summary

The paper proposes a bold interdisciplinary analogy between the preparation phase of earthquakes and the electrical activity of the human heart, suggesting that both systems exhibit similar dynamical behavior characteristic of a second‑order thermal phase transition. The authors begin by framing the Earth as a “living planet” whose continuous complex processes, such as fault rupture, are interpreted as biological‑like phenomena. Building on earlier work that identified brain‑type disease signatures in pre‑seismic electromagnetic emissions, they hypothesize that a “heart‑type disease” – i.e., pathological cardiac electrical patterns – can likewise be detected in the later stages of the earthquake preparation process.

The theoretical backbone relies on critical phenomena: as a heterogeneous crust approaches failure, stress accumulation drives the system toward an critical point where fluctuations become scale‑free. In parallel, the healthy heart’s electrophysiology is described as a network of excitable cells that operates near a critical regime, producing long‑range temporal correlations (LRTC) measurable by the Hurst exponent (H). The authors argue that both systems should therefore display similar statistical signatures.

To test this, they analyze two data sets: (1) electromagnetic recordings collected during the 30‑day window preceding several moderate to large earthquakes, and (2) 24‑hour continuous electrocardiograms (ECG) from a cohort of healthy volunteers and patients with acute myocardial infarction. For each time series they compute the Hurst exponent, perform continuous wavelet transforms, and apply a sliding‑window statistical test to separate “critical” intervals (high H, dominant low‑frequency power) from “post‑critical” intervals (drop in H, emergence of high‑frequency noise).

The results show that pre‑seismic electromagnetic data exhibit H≈0.78 and a pronounced amplification of low‑frequency wavelet coefficients, closely matching the H≈0.75 and stable low‑frequency power observed in normal ECG recordings. In the terminal phase of fracture – termed the “damage tail” – the Hurst exponent collapses, and the wavelet spectrum becomes dominated by high‑frequency components. This pattern mirrors the ECG of myocardial infarction patients, where QRS complexes become distorted and high‑frequency noise increases. The authors interpret these parallel transitions as evidence of a dynamical analogy: both the crustal fault network and the cardiac conduction system evolve toward a critical point, then undergo a rapid, non‑critical breakdown when the threshold is exceeded.

The discussion acknowledges several caveats. First, the measurement environments differ dramatically: electromagnetic sensors are subject to geomagnetic noise, while ECG recordings contend with muscular and motion artifacts. Second, the underlying physical mechanisms (stress‑induced microcracking versus ion channel dynamics) are fundamentally distinct, raising the question of whether statistical similarity alone justifies a biological interpretation of seismic data. Third, the sample sizes are modest, and the statistical significance of the observed Hurst exponent changes is not rigorously tested against appropriate null models. The authors also note that the term “human‑type disease” is metaphorical and does not imply a direct clinical translation.

Despite these limitations, the paper contributes an intriguing proof‑of‑concept that complex‑systems tools—Hurst analysis, wavelet decomposition, and critical‑point theory—can be applied across geophysical and biomedical domains. The authors suggest future work should involve larger, multi‑sensor networks, integration of machine‑learning classifiers, and real‑time monitoring to assess whether the identified statistical precursors can improve earthquake forecasting or early detection of cardiac events. In sum, the study offers a speculative but thought‑provoking bridge between seismology and cardiology, highlighting both the promise and the methodological challenges of cross‑disciplinary complex‑systems research.