All Is Motion

Signal Science

Archaeology and SETI: Long-Distance Signal Science Across Spacetime

Archaeology is inverse signal reconstruction. Every artifact, every feature, every landscape modification represents a degraded signal emitted by past human activity (motion patterns). Our task is to reconstruct those original signals from incomplete, noisy data subject to environmental decay.

This insight connects archaeology to a broader paradigm: long-distance signal science across spacetime.

  • SETI searches for signals across space from contemporary but distant civilizations
  • Archaeology searches for signals across time from past civilizations at the same location

Both face the same fundamental challenge: recovering meaningful patterns from degraded information where the original context is lost.

From Motion Traces to Information:

Archaeological features are not static “objects”—they are motion traces registered in a substrate. A handaxe records the motion pattern of knapping. A territorial boundary encodes patterns of movement and resource control. Laetoli footprints preserve actual locomotion.

By treating archaeological data as degraded spatiotemporal signals, we can:

  1. Apply signal processing techniques (filtering, correlation, spectral analysis)
  2. Use machine learning for pattern recognition in high-dimensional spaces
  3. Integrate heterogeneous data sources through signal fusion
  4. Model uncertainty and decay processes explicitly
  5. Detect patterns invisible to traditional typological analysis

Time as Spatial Manifolds:

Traditional archaeological databases organize data chronologically—sequences of periods, phases, typologies. This forces temporal relationships into arbitrary linear structures.

The signal-based approach embeds archaeological sites as points in unified 3D spatial manifolds where temporal relationships are encoded geometrically. This enables:

  • GPU-accelerated processing of entire landscapes simultaneously
  • Integration with astronomical models (orbital mechanics, solar cycles)
  • Detection of periodic patterns across millennia
  • Quantification of temporal uncertainty as geometric properties

Assembly Theory & Complexity:

Recent work in assembly theory provides a framework for quantifying the “assembly index” of complex objects—essentially, how many steps are required to build them. Archaeological signals naturally map onto this framework: more complex motion patterns produce artifacts with higher assembly indices.

This connects archaeological inference to information theory, thermodynamics, and the fundamental question: what signatures distinguish products of intelligent activity from natural processes?

Validation:

The Ireland RMP (Record of Monuments and Places) project demonstrates these principles in practice. Using signal correlation across 150,000+ monuments spanning 6,000 years, we recovered statistically significant territorial boundaries and detected historical patterns—all through treating archaeological distributions as degraded signals.

Connections: