Crowdsourced earthquake intelligence powered by Lagrangian Tensor Generating Logic. Buildings as data. Seismic physics as open science.
In 794 AD, Emperor Kanmu moved the capital from Nara (Heijo-kyo) to Kyoto (Heian-kyo), beginning 400 years of cultural flourishing. In 2026, Zweichain moves from Heijo to Heian — a new platform era for seismic intelligence.
Mass matrix — floor inertia per story level. Diagonal for classical shear buildings.
Damping matrix — energy dissipation (Rayleigh form: αM + βK). Identified from shake response.
Stiffness matrix — structural coupling tensor. TGL identifies this from sensor data.
Ground excitation — accelerometer time series. Input from ZweiMedia iOS app or IoT floor sensors.
Smartphones and IoT floor sensors become a nationwide structural health monitoring network. Each building is treated as a coupled oscillator system — sensors on each floor, TGL algorithms identify the full structural tensor (K, C, M matrices) governing inter-floor seismic response.
The tensor, stored in an .nmz file,
enables per-building digital twin simulation — not statistical grid estimates,
but physics-first structural identification at building resolution.
POST /v3/shake — accelerometer float array from iOS / IoT sensor
TGL computation — TensorFlow identifies K, C matrices via optimization
Generate .nmz — structural tensor file with building_class, model_id
Simulation — run 3.11 or Nankai scenario against the model
A portable binary/JSON descriptor encoding the full structural identity of a building:
mass, stiffness, and damping matrices plus sensor geometry and confidence scores.
Named like NMZ-Z-W3B-1778780307.
{
"model_id": "NMZ-Z-W3B-1778780307",
"tgl": "Z-TGL",
"building_class": "W3B", // Wood, 3-story, Base-isolated
"generated": 1778780307, // UNIX epoch
"M": [[1.00,0,0],[0,0.92,0],[0,0,0.85]], // Mass matrix
"K": [[2.24,-1.12,0],[-1.12,2.10,-0.98],[0,-0.98,0.86]], // Stiffness
"C": [[0.15,-0.07,0],[-0.07,0.14,-0.07],[0,-0.07,0.08]], // Damping
"sensors": [
{ "floor": 0, "lat": 35.0116, "lng": 135.7681, "device": "ios" },
{ "floor": 1, "lat": 35.0116, "lng": 135.7681, "device": "iot" },
{ "floor": 2, "lat": 35.0116, "lng": 135.7681, "device": "iot" }
],
"confidence": 0.94,
"rmse": 0.0031,
"scenario_311_score": "Excellent",
"scenario_nankai_score": "Good"
}
Six interconnected layers — from raw seismic sensing to blockchain settlement, digital twin simulation, and real estate investment intelligence.
Submit your own Tensor Generating Logic algorithm via GitHub URL. Run sandboxed in Docker against real building shake data. Ranked by RMSE against ground-truth floor accelerations. Reward: NMZ tokens.
Where ŷ is your algorithm's predicted floor acceleration (next 500ms window) and y is the ground-truth sensor reading.
Input: accelerometer time series per floor · Output: predicted acceleration for target floor. Docker container receives shake data via stdin JSON, returns predictions.
Crowdsourced seismic sensing → per-building tensor (K, C, M) identification. .nmz file format. TGL open competition. REIT portfolio scoring.
Normal mode seismology at continental scale. Spherical harmonic decomposition. .ppt file format — from building tensor to planetary tensor.
Encode the seismic tensor as a quantum Hamiltonian. VQE / QAOA optimization on QPU. Quantum-native structural system identification.
"In the Heian period, Kyoto became the center of science, art, and governance for four centuries. We move our platform — from Nara's Heijo to Kyoto's Heian-kyo — to build the infrastructure that makes every building in Japan seismically legible."— Zweichain, May 2026
Merit-based reward system. TGL algorithm developers, building data contributors, and seismic sensor operators earn NMZ. Catastrophe bonds, REIT risk scoring, and insurance products settle on-chain via Zweichain smart contracts.
nmz_live_... for production, nmz_test_... for sandbox.