Quantitative Modelling of Marine Seismic Hazard Chains: Topology, Core Amplification Nodes, and Implications

Shi, Yang; Hu, Chunyu; Zhang, Zhenguo

doi:10.5194/egusphere-2026-4167

Preprints

https://doi.org/10.5194/egusphere-2026-4167

Preprints

09 Sep 2026

| 09 Sep 2026

Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

Quantitative Modelling of Marine Seismic Hazard Chains: Topology, Core Amplification Nodes, and Implications

Yang Shi, Chunyu Hu, and Zhenguo Zhang

Abstract. Earthquakes in marine settings may trigger long chains of secondary hazards. These chains propagate from the seafloor to coastal infrastructure and on to society, yet they have rarely been quantified as a whole. To address this gap, this study proposes a quantitative model of marine seismic hazard chains that combines a catastrophe-dynamics formulation with directed-graph analysis. This network draws on documented secondary hazards from dozens of major tsunamis and includes 39 nodes. This study characterises its topology through degree centrality, network density, centralisation, mean path length, and a layer-resolved net-flow measure. The result is a single-source, dual-hub, multi-sink cascade that is shallow, with most hazards reachable within about two steps. Submarine and coastal earthquakes drive the whole chain, while tsunamis and critical infrastructure failure act as the two high-throughput hubs that amplify and route the flow. Critical network failure is the principal convergence node, where these losses accumulate. The model reveals a monotonic cascade from geological triggers to socioeconomic losses, in which the engineering layer is the gateway through which natural hazards convert into technological failures. A comparison with a mainland earthquake chain at a common taxonomic resolution shows that the two systems share the same four-layer architecture. The mainland earthquake chain exhibits stronger centralisation while the marine tsunami chain presents a more distributed structure. These findings suggest that the controllable engineering hubs, rather than the uncontrollable seismic sources, are the most effective targets for disaster mitigation.

Received: 12 Jul 2026 – Discussion started: 09 Sep 2026

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Yang Shi, Chunyu Hu, and Zhenguo Zhang

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'Comment on egusphere-2026-4167', Anonymous Referee #1, 09 Sep 2026 reply
The topic is interesting. However, the study has fundamental problems. The paper claims to present a quantitative model. In reality, it analyzes a manually constructed binary network. The mathematical framework is not used. Most conclusions are already determined by the selected nodes and their classification.
I apologize for the large number of comments. However, the current manuscript requires substantial changes.
This is not a quantitative catastrophe-dynamics model. No transition rate is estimated. No probability is calculated. No model is calibrated. The analysis uses only a binary edge list.

Equations 1–13 are disconnected from the results. The authors introduce , , and . None of them is estimated from observations. These equations add complexity but no real analysis.

The definition of A is unclear. It is first defined using . It is later treated as an empirical adjacency matrix. These are not automatically the same thing.

The propagation horizon of ten steps has no clear role. The authors later use an infinite series. The final analysis uses only reachability. The choice of ten steps appears meaningless.

The dataset construction is poorly explained. The paper does not provide a clear event-selection procedure. It does not explain how the 366 chains were identified.

The edge-coding process is subjective. The authors mention expert encoding. They provide no information about the experts, coding rules, agreement, or conflict resolution.

The network may reflect reporting bias. Recent and famous disasters have better documentation. Older events have fewer available records. This can strongly affect the number of reported links.

Every edge receives the same weight. A link reported once is treated like a link reported many times. This discards important information about frequency and reliability.

The taxonomy is highly subjective. It combines physical hazards, infrastructure failures, social outcomes, and broad consequence categories in one network.

Critical infrastructure failure is an extremely broad node. Its high degree is therefore expected. The paper presents this result as a discovery. It is largely created by the node definition.

Several aggregation choices are difficult to defend. Liquefaction, uplift, subsidence, methane blowout, and snow avalanche are grouped as mass slides. These are different processes.

Volcanic eruptions are grouped with earthquakes. Tsunamis are grouped with floods. Insufficient rescue is grouped with healthcare degradation. These choices appear arbitrary.

The mainland network is not described properly. Its event selection, data sources, and coding process are missing. A fair comparison is therefore impossible.

The phrase “energy waterfall” is incorrect. The study counts edges. It does not calculate energy, intensity, probability, or loss.

Degree is repeatedly interpreted as amplification. Degree only measures connectivity. It does not show that a node increases hazard intensity or damage.

The intervention claims are unsupported. Removing a node from a graph is not equivalent to preventing a real infrastructure failure.

No intervention cost is considered. No failure probability is included. No avoided damage is calculated. The term “optimal intervention” is unjustified.

The node-removal analysis is biased. Removing a node also removes every pair involving that node. Part of the reported reduction is therefore automatic.

The so-called hindcast is not genuinely independent. The three events are famous historical disasters. Palu is even discussed in the Introduction.

The paper provides no evidence that the three validation events were selected before examining the network. The analysis appears post hoc.

The reported coverage is only 74 percent. More than one-quarter of the links are missing. This is not strong validation.

The authors dismiss the missing links as a rare tail. This is not justified. Rare pathways can produce the most unexpected and serious consequences.

The robustness analysis tests very broad conclusions. Earthquakes remain source nodes because this is an earthquake-triggered network. Consequences remain sinks because they were defined as consequences.

Bootstrapping cannot validate assumptions built into the taxonomy. It only resamples the same subjective coding decisions.

The main uncertainty comes from human judgment. The bootstrap does not include coding disagreement, missing documents, or alternative causal interpretations.

The high-frequency coverage analysis may contain data leakage. The paper does not explain whether the held-out events were used to identify high-frequency links.

The mean path length excludes unreachable pairs. This can make the network appear artificially shallow.

A short topological path does not mean a short response time. The edges have no physical duration. The claim about a short intervention window is unsupported.

The paper calls the network a single-source cascade. It also reports two pure sources. This is inconsistent.

Causality is assumed. A documented sequence does not necessarily prove that one event caused another.

Many events can happen simultaneously. Infrastructure failure, network failure, transport disruption, and business interruption may not have a unique causal order.

The paper repeatedly uses the words probability, amplification, prediction, and optimization. None of these quantities is actually estimated.

The claim that the network is fundamentally complete is too strong. The accumulation curve does not fully saturate. The external events also add 14 new links.

The claim that missing data have no influence is unsupported. The authors only show that four broad conclusions remain unchanged.

The mitigation recommendations go far beyond the evidence. A binary network cannot determine the most effective real-world investment strategy.

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Citation: https://doi.org/10.5194/egusphere-2026-4167-RC1

Yang Shi, Chunyu Hu, and Zhenguo Zhang

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Latest update: 09 Sep 2026

Short summary

Earthquakes beneath the sea rarely end with the shaking. They can raise tsunamis, flood coasts, break ports and power lines, and end in economic and social losses. We traced these chains of events, documented across dozens of major tsunamis, and joined them into one network to see how disaster travels. The chain is short, and most damage funnels through a few hotspots, above all failing infrastructure. Since the quake itself cannot be prevented, protecting these hotspots is the best defence.