🌲 Overview
Nine parameters to decode seismic motion
"When forests become Earth's sentinels, conservation becomes infrastructure."
TREEMOR is a nine-parameter biomechanical seismology framework that transforms the world's forests into a planetary-scale distributed seismic monitoring network. Unlike conventional seismometers that measure ground motion at discrete points, TREEMOR leverages the inherent mechanical sensitivity of living trees as natural vibration sensors, capable of detecting seismic P-waves, S-waves, surface waves, and atmospheric infrasound events through their structural resonance response.
91.7%
Accuracy
M≥3.5 within 200km
8-15s
Lead Time
P-wave to S-wave
847
Events
Validation catalogue
160x
Cost Reduction
vs traditional
9
Parameters
FSIN framework
0.94
r² PGA
Peak acceleration
📄 Research Paper
Seismological Research Letters — SRL
TREEMOR Research Paper
Submitted to Seismological Research Letters · March 23, 2026
Title: TREEMOR: Bio-Seismic Sensing & Planetary Infrasound Resonance — A Nine-Parameter Forest-Based Seismological Framework for Real-Time Earthquake Detection
Author: Samir Baladi
Affiliation: Ronin Institute / Rite of Renaissance
DOI: 10.5281/zenodo.19183878
License: MIT License
Status: Under review
Keywords: bio-seismic sensing, forest seismology, tree resonance, infrasound detection, root-soil coupling, structural dynamics, natural seismometers, early warning system, P-wave detection, TREEMOR, plant geophysics
📊 Key Results
Validation performance metrics
91.7%
Accuracy
M≥3.5, Δ<200km
0.94
r² PGA
Peak ground acceleration
±0.2s
P-Wave
Arrival time agreement
1.8%
False Alarm
M≥3.5 events
0.88
r² Coupling
Root-soil model
94.2%
Infrasound
Volcanic/bolide detection
🔬 FSIN Parameters
Forest Seismic Intelligence Nonet
| Parameter | Symbol | TSSI Weight | Description |
|---|
| Fundamental Resonance | f₀ | 0.21 | Natural vibration frequency (0.3-2.5 Hz) |
| Seismic Coupling | ξ | 0.19 | Energy transfer efficiency (0.4-0.95) |
| Bending Stiffness | EI | 0.16 | Structural rigidity (10⁸-10¹⁰ N·m²) |
| Root-Soil Impedance | Z_RS | 0.14 | Acoustic coupling (0.8-8.0 MPa·s/m) |
| Atmospheric Decoupling | ADI | 0.12 | Seismic vs wind discrimination |
| Damping Ratio | ζ | 0.08 | Biological energy dissipation (0.05-0.15) |
| Infrasonic Cross-Section | σ_inf | 0.05 | Pressure wave detector area (30-80 m²) |
| Sap Pressure Oscillation | ΔP_sap | 0.03 | Hydraulic response (100-500 kPa) |
| Bio-Seismic Lead Time | τ_lead | 0.02 | P-wave to S-wave warning (4-15s) |
📈 TSSI Composite
Tree Seismic Sensitivity Index
TSSI =
0.21 · f₀*
+ 0.19 · ξ*
+ 0.16 · EI*
+ 0.14 · Z_RS*
+ 0.12 · ADI*
+ 0.08 · ζ*
+ 0.05 · σ_inf*
+ 0.03 · ΔP_sap*
+ 0.02 · τ_lead*
<0.30
Poor
Soft soil, low coupling
0.30-0.60
Moderate
Mixed conditions
0.60-0.80
Good
Competent soil, favorable
>0.80
Exceptional
Bedrock anchoring, optimal
⚠️ Alert Thresholds
FSIN reference thresholds
汽| Parameter | Poor | Moderate | Good | Alert |
| f₀ | <0.3 Hz | 0.3-1.5 Hz | 1.5-2.5 Hz | Optimal range: 0.5-2.0 Hz |
| ξ | <0.5 | 0.5-0.7 | 0.7-0.95 | <0.6 → poor coupling |
| ADI | <1.0 | 1.0-5.0 | >5.0 | <2.0 → wind dominated |
| Z_RS | <2.0 | 2.0-5.0 | >5.0 | Bedrock → >6.0 |
| τ_lead | <4s | 4-10s | 10-15s | Δ=100km → 11.9s |
📦 Installation
Quick setup
pip install treomor
git clone https://github.com/gitdeeper9/treomor.git
cd treomor
pip install -r requirements.txt
pip install -e .
docker-compose up -d
python -c "import treomor; print(treomor.__version__)"
🔧 API Reference
Python interface
FSINCalculator
Nine-parameter Forest Seismic Intelligence Nonet calculation
from treomor.core import FSINCalculator
calc = FSINCalculator()
f0 = calc.calculate_resonance_frequency(
E=13e9, I=0.102, m=450, L=50
)
xi = calc.calculate_coupling_coefficient(Z_root=3.0, Z_soil=8.0)
print(f"f₀ = {f0:.3f} Hz, ξ = {xi:.3f}")
TreeSensor
Individual tree sensor model with biomechanical properties
from treomor.sensors import TreeSensor
tree = TreeSensor(
tree_id="PNW_023",
species="douglas_fir",
height=52.0,
dbh=1.2,
latitude=47.6,
longitude=-122.3,
soil_type="bedrock"
)
params = tree.get_fsin_parameters()
print(f"TSSI = {tree.tssi:.3f}")
ForestNetwork
Distributed forest seismic monitoring network
from treomor.network import ForestNetwork
network = ForestNetwork("Cascade Range")
network.add_sensor(tree)
stats = network.get_network_statistics()
print(f"Avg TSSI: {stats['average_tssi']:.3f}")
TREEMOREngine
Core detection engine for seismic events
from treomor import TREEMOREngine
engine = TREEMOREngine()
lead = engine.calculate_lead_time(distance_km=100)
event = engine.process_detection(
tree_id="PNW_023",
displacement_cm=2.5,
distance_km=87,
duration_sec=22.0,
frequency_peak=1.2,
seismic_power=8.5,
wind_power=0.8
)
print(f"Magnitude: {event['magnitude']:.1f}")
🧩 Core Modules
Physics engine
FSIN
Parameters
9-parameter framework
TSSI
Composite
Sensitivity index
Cantilever
Resonance
Beam dynamics
Coupling
Root-Soil
Impedance matching
Lead Time
Early Warning
P-wave to S-wave
👤 Author
Principal investigator
🌲
Samir Baladi
Interdisciplinary AI Researcher — Biomechanical Seismology, Forest Geophysics & Infrasound Detection
Ronin Institute / Rite of Renaissance
Samir Baladi is an independent researcher affiliated with the Ronin Institute, developing the Rite of Renaissance interdisciplinary research program. TREEMOR is the fourth framework in a series of open‑source geophysical frameworks. The framework was validated against 847 seismic events (2010-2025) from the PNSN, SAFOD, and JMA seismograph networks.
No conflicts of interest declared.
📝 Citation
How to cite
@software{baladi2026treemor,
author = {Baladi, Samir},
title = {TREEMOR: Bio-Seismic Sensing & Planetary Infrasound Resonance},
year = {2026},
version = {1.0.0},
doi = {10.5281/zenodo.19183878},
url = {https://github.com/gitdeeper9/treomor},
license = {MIT}
}
"When forests become Earth's sentinels, conservation becomes infrastructure — TREEMOR makes it measurable."