LITHO-SONIC

Lithospheric Acoustic Sensing and Integrated Crustal Monitoring Framework

A five-parameter poro-elastic monitoring system for real-time geomechanical hazard detection, subsurface fluid characterisation, and early warning of volcanic, seismic, and induced-seismicity events.

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Table of Contents

• Overview
• Scientific Background
• The Five Governing Parameters
• Lithospheric Stress Index (LSI)
• Project Structure
• Installation
• Quick Start
• Usage
• Validation & Results
• Case Studies
• Alert Framework
• Contributing
• References
• License

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Overview

LITHO-SONIC reframes crustal monitoring from a passive event-detection paradigm to an active acoustic information extraction paradigm. Rather than waiting for discrete seismic events, LITHO-SONIC continuously decodes the low-frequency acoustic signals (0.001-20 Hz) that the crust perpetually emits, driven by pore pressure dynamics, stress redistribution, and fluid migration.

Key achievements:

Metric	Conventional Monitoring	LITHO-SONIC
Mean precursor lead time	4-9 days	24 days
Pore pressure front spatial resolution	200-400 m (InSAR)	+/-50 m
Fluid phase discrimination	Not available	[x] (water / CO2-brine / gas / magmatic)
Detection accuracy (LSI)	--	92.7% across 18 sites
Tectonic environments validated	--	4 (rift / subduction / volcanic / craton)

---

Scientific Background

LITHO-SONIC is grounded in Biot theory (1956) -- the rigorous framework for acoustic wave propagation in fluid-saturated porous elastic solids. The key physical insight is the existence of Biot's slow compressional wave: a diffusive, pressure-driven mode that propagates through pore fluid networks at 0.01-10 Hz, encoding real-time information about:

• Fluid saturation and pore pressure distribution
• Fracture network geometry and aperture
• Effective stress state and proximity to failure
• Fluid phase composition (water, gas, magmatic volatiles, CO2-brine)

The theoretical foundation is completed by:

• Gassmann substitution for saturated bulk modulus
• Gutenberg-Richter statistics for acoustic emission scaling
• Hydraulic fracture resonance theory for fluid-filled crack characterisation
• Biot attenuation inversion for quality-factor mapping

---

The Five Governing Parameters

1. Biot Coupling Coefficient -- B_c

Governs solid-fluid wave coupling in saturated porous rock.

Bc = (1 - K_dry / K_s) / (1 - K_dry/K_s + phi(K_s/K_f - 1))

Range: 0 (dry rock) -> 1 (highly saturated, critical pore pressure).

---

2. Acoustic Impedance Contrast -- Z_c

Maps lithological boundaries and fracture zone geometry.

Z = rho * V_P = rho * sqrt((K_sat + 4G/3) / rho)

Reference values: Granite 15-18 MRayl | Basalt 14-16 MRayl | Saturated sandstone 6-9 MRayl | Fracture zone 1.5-3.5 MRayl.

---

3. Hydraulic Fracture Resonance Frequency -- f_n

Discriminates subsurface fluid phase from fracture resonance spectra.

f_n = n * V_fluid / (2L)      n = 1, 2, 3, ...

Fluid discrimination: Water (f1 ~= 740 Hz/m) | CO2-brine (600-675 Hz/m) | Dry gas (75-125 Hz/m) | Magmatic volatiles (300-450 Hz/m).

---

4. Acoustic Attenuation Coefficient -- alpha_att

Quantifies anelastic energy dissipation in the propagation medium.

A(x) = A0 * exp(-alpha_att * x)      alpha_att = pi*f / (Q * V_P)

Diagnostic ranges: Intact granite Q > 200 | Saturated fractured rock Q = 20-80 | Near-critical pore pressure Q < 15.

---

5. Stress-Induced Acoustic Emission Rate -- S_dot_ae

Quantifies micro-fracture energy release rate.

S_dot_ae = A0 * exp(sigma_eff / sigma_c) * (depsilon/dt)^beta

Diagnostic: Exponential S_dot_ae acceleration signals approach to critical failure threshold.

---

Lithospheric Stress Index (LSI)

The five parameters are fused into a single dimensionless index:

LSI = w1*Bc* + w2*Zc* + w3*fn* + w4*alphaatt* + w5*S_dotae*

Weights (from 14-year training dataset):

Parameter	Weight
B_c (Biot Coupling)	0.22
Z_c (Impedance Contrast)	0.18
f_n (Fracture Resonance)	0.24
alpha_att (Attenuation)	0.19
S_dot_ae (Emission Rate)	0.17

---

Project Structure

litho-sonic/
|
+---- README.md
+---- LICENSE
+---- requirements.txt
+---- setup.py
|
+---- litho_physics/                  # Core physics engine
|   +---- __init__.py
|   +---- litho_physics.py            # Main module -- all five governing equations
|   +---- biot.py                     # Biot wave equations & slow-wave solver
|   +---- impedance.py                # Acoustic impedance contrast computation
|   +---- fracture_resonance.py       # Hydraulic fracture resonance inversion
|   +---- attenuation.py              # Biot attenuation & Q-factor inversion
|   +---- emission.py                 # Acoustic emission rate model
|   +---- lsi.py                      # Lithospheric Stress Index fusion
|
+---- pipeline/                       # Signal processing pipeline
|   +---- __init__.py
|   +---- ingest.py                   # Raw geophone data ingestion (1000 sps)
|   +---- spectral.py                 # Stage 1 -- FFT spectral decomposition
|   +---- harmonic.py                 # Stage 2 -- harmonic analysis & mode ID
|   +---- inversion.py                # Stage 3 -- conjugate gradient inversion
|
+---- instruments/                    # Hardware interface & sensor config
|   +---- litho_geo_v2.py             # LITHO-GEO v2 geophone array driver
|   +---- infrasound_array.py         # 24-element infrasound microphone array
|   +---- tiltmeter.py                # Biaxial broadband tiltmeter interface
|   +---- calibration.py             # Sensor calibration utilities
|
+---- alert/                          # Alert framework
|   +---- __init__.py
|   +---- thresholds.py               # LSI threshold management (Background / Elevated / Active)
|   +---- neyman_pearson.py           # Optimal detection threshold (Neyman-Pearson)
|   +---- notifications.py           # Alert dispatch (API / webhook)
|
+---- validation/                     # Validation dataset and site configurations
|   +---- sites/
|   |   +---- kilauea_lerz/           # Case Study A -- Kilauea East Rift Zone 2018
|   |   +---- campi_flegrei/          # Case Study B -- Campi Flegrei 2023-2024
|   |   +---- geysers/                # Case Study C -- The Geysers, California
|   |   +---- parkfield/              # San Andreas Fault -- Parkfield
|   |   +---- rhine_graben/           # Rhine Graben geothermal province
|   |   +---- chicxulub/              # Chicxulub impact crater reference site
|   +---- benchmarks/
|       +---- lsi_accuracy.py
|       +---- lead_time_analysis.py
|
+---- notebooks/                      # Jupyter notebooks
|   +---- 01_biot_theory_primer.ipynb
|   +---- 02_five_parameters_tutorial.ipynb
|   +---- 03_lsi_computation.ipynb
|   +---- 04_kilauea_case_study.ipynb
|   +---- 05_campi_flegrei_case_study.ipynb
|   +---- 06_geysers_case_study.ipynb
|   +---- 07_statistical_framework.ipynb
|
+---- tests/                          # Unit and integration tests
|   +---- test_biot.py
|   +---- test_impedance.py
|   +---- test_fracture_resonance.py
|   +---- test_attenuation.py
|   +---- test_emission.py
|   +---- test_lsi.py
|   +---- test_pipeline.py
|
+---- docs/                           # Documentation
|   +---- api/                        # Auto-generated API reference
|   +---- theory/                     # Extended theoretical notes
|   +---- deployment/                 # Field deployment guide
|   +---- appendices/
|       +---- A_instrument_specs.md
|       +---- B_biot_frequency_derivation.md
|       +---- C_operational_thresholds.md
|       +---- D_data_availability.md
|       +---- E_author_contributions.md
|
+---- data/
    +---- example/                    # Example datasets (all 18 validation sites)
    +---- reference/                  # 14-year reference baseline (normalisation bounds)

---

Installation

Requirements: Python 3.9+, NumPy, SciPy, ObsPy, Matplotlib, Pandas.

# Clone the repository
git clone https://gitlab.com/gitdeeper8/lithosonic.git
cd lithosonic

# Create and activate a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate       # Linux / macOS
.venv\Scripts\activate          # Windows

# Install dependencies
pip install -r requirements.txt

# Install the package in editable mode
pip install -e .

---

Quick Start

from litho_physics import LithoSonic

# Initialise with a site configuration
ls = LithoSonic.from_config("validation/sites/kilauea_lerz/config.yaml")

# Load raw waveform data
ls.load_waveforms("data/example/kilauea_2018.mseed")

# Run the full processing pipeline
results = ls.run_pipeline()

# Compute the Lithospheric Stress Index
lsi = ls.compute_lsi()
print(f"LSI = {lsi:.3f}")

# Evaluate against alert thresholds
alert_level = ls.evaluate_alert(lsi)
print(f"Alert Level: {alert_level}")   # Background / Elevated Alert / Active Instability

---

Usage

Computing Individual Parameters

from litho_physics.biot import compute_biot_coupling
from litho_physics.fracture_resonance import invert_fracture_length
from litho_physics.attenuation import compute_q_factor

# Biot Coupling Coefficient
Bc = compute_biot_coupling(K_dry=20e9, K_s=36e9, K_f=2.2e9, phi=0.15)

# Fracture length inversion from observed resonance frequency
L = invert_fracture_length(f_observed=0.74, fluid="water")

# Q-factor from amplitude decay
Q = compute_q_factor(amplitude_profile, distances, frequency=1.0, Vp=4200)

Running the Full Signal Processing Pipeline

from pipeline import LithoSonicPipeline

pipe = LithoSonicPipeline(config="config/default.yaml")

# Stage 1 -- Spectral decomposition (FFT on 60-s overlapping windows)
spectra = pipe.spectral_decomposition(raw_data)

# Stage 2 -- Harmonic analysis and mode identification
modes = pipe.harmonic_analysis(spectra)

# Stage 3 -- Conjugate gradient inversion
parameters = pipe.inversion(modes)

Real-Time Monitoring

from litho_physics import LithoSonicStream

# Connect to live sensor array
stream = LithoSonicStream(host="192.168.1.100", port=18000)

# Stream LSI in real time with alert callbacks
stream.monitor(
    on_elevated=lambda lsi: print(f"[!]  Elevated Alert -- LSI={lsi:.3f}"),
    on_critical=lambda lsi: print(f"[!!] Active Instability -- LSI={lsi:.3f}")
)

---

[x] Validation & Results

LITHO-SONIC was validated against field datasets from 18 geologically diverse monitoring sites across four tectonic environments:

Environment	Sites	Example
Intraplate volcanic	5	Kilauea East Rift Zone
Subduction zone	4	Campi Flegrei
Continental rift	5	Rhine Graben
Stable craton	4	Chicxulub impact crater

Summary statistics:

• LSI detection accuracy: 92.7% (Youden Index optimised, LSI* = 0.80)
• Mean precursor lead time: 24 days (vs. 4-9 days conventional)
• False alarm rate: Maintained at <= 10% (Neyman-Pearson threshold)
• Biot inversion Vp accuracy (Chicxulub): r^2 = 0.963 vs. independent VSP data
• Pore pressure front resolution: +/-50 m (3.6x improvement over InSAR)

Inter-parameter correlations:

Pair	Pearson r	Physical interpretation
B_c -- S_dot_ae	0.81	Pore pressure drives effective stress reduction -> AE acceleration
Z_c -- alpha_att	0.83	Both track fracture-induced impedance and dissipation
f_n -- Z_c	-0.67	Gas exsolution reduces both fluid velocity and bulk modulus

---

Case Studies

Case Study A -- Kilauea East Rift Zone (2018 Intrusion Event)

Retrospective application reveals a four-stage precursory sequence:

• T-72 days: B_c anomaly (fluid influx into dike pathway)
• T-21 days: f_n shift indicating magmatic volatile exsolution
• T-14 days: LSI crosses critical threshold (0.80)
• T-3 days: S_dot_ae exponential acceleration

Case Study B -- Campi Flegrei Caldera (2023-2024 Unrest)

Ongoing real-time monitoring of a caldera system with 500,000 residents within 10 km:

• B_c rose from 0.57 (early 2023) -> 0.81 (November 2024)
• f_n shift confirmed transition from hydrothermal brine -> magmatic gas
• Three-level alert framework active and integrated with civil protection authorities

Case Study C -- The Geysers Geothermal Field, California

World's largest geothermal power plant; most seismically active non-fault site in California:

• Injection plume fronts resolved at 50 m spatial resolution
• B_c pore pressure tracking enables real-time Traffic Light Protocol (TLP) management

---

Alert Framework

LITHO-SONIC implements a three-level LSI alert system, mapped to standard volcanic and USGS ShakeAlert frameworks:

Level	LSI Range	Status	Action
[GREEN] Background	LSI < 0.60	Normal crustal activity	Routine monitoring
[YELLOW] Elevated Alert	0.60 <= LSI < 0.80	Anomalous parameter evolution	Enhanced monitoring; stakeholder notification
[RED] Active Instability	LSI >= 0.80	Critical threshold exceeded	Emergency protocols; civil protection liaison

---

Contributing

Contributions are welcome. Please open an issue to discuss significant changes before submitting a pull request.

# Run the full test suite before submitting
pytest tests/ -v

# Check code style
flake8 litho_physics/ pipeline/ --max-line-length=120

Please follow the Contributor Covenant code of conduct.

---

References

Key Reference	Relevance
Biot (1956a, 1956b) -- J. Acoust. Soc. Am.	Foundational poro-elastic wave equations
Gassmann (1951)	Saturated bulk modulus substitution
Gutenberg & Richter (1944) -- Bull. Seismol. Soc. Am.	Acoustic emission frequency-magnitude scaling
Obara (2002) -- Science	Non-volcanic tremor discovery
Zoback (2010) -- Reservoir Geomechanics, Cambridge	Effective stress and geomechanics framework
Streckeisen et al. (1995)	STS-2 broadband seismometer design
Walter et al. (2020) -- Nature Comms.	Distributed acoustic sensing (DAS)
Morgan et al. (2016) -- Science	Chicxulub Expedition 364 VSP reference data

Full bibliography available in docs/REFERENCES.md.

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Digital Infrastructure

Platform	Link
Live Dashboard	lithosonic.netlify.app
GitHub	github.com/gitdeeper8/lithosonic
GitLab	gitlab.com/gitdeeper8/lithosonic
Zenodo (DOI)	10.5281/zenodo.18931304
PyPI	pypi.org/project/lithosonic

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Author

Samir Baladi ¹ *

¹ Ronin Institute / Rite of Renaissance, Interdisciplinary AI Researcher -- Crustal Geophysics, Infrasonic Wave Analysis & Lithospheric Stress Monitoring

* Corresponding Author

• Email: gitdeeper@gmail.com
• ORCID: 0009-0003-8903-0029
• Phone: +1 (614) 264-2074

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Citation

If you use LITHO-SONIC in your research, please cite:

@software{baladi2026lithosonic,
  author    = {Baladi, Samir},
  title     = {LITHO-SONIC: A Multi-Parameter Geophysical Framework for
               Real-Time Crustal Acoustic Emission and Infrasonic
               Lithospheric Stress Monitoring},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.18931304},
  url       = {https://lithosonic.netlify.app}
}

---

License

This project is licensed under the MIT License -- see the LICENSE file for details.

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Data Availability: Example datasets from all 18 validation sites, unit tests, and Jupyter notebooks reproducing all published figures are included in the data/example/ and notebooks/ directories.

For questions or collaborations, please open a GitLab issue.

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