Data-driven rational function neural networks: a new method for generating analytical models of rock physics

TL;DR

This paper introduces a data-driven neural network approach called Rational Function Neural Networks (RafNN) for modeling rock physics, enabling the derivation of analytical velocity models directly from observational data without complex physical derivations.

Contribution

The paper presents RafNN, a novel neural network method that reconstructs physically meaningful rock velocity models solely from data, bypassing traditional complex physical modeling.

Findings

RafNN accurately reconstructs Gassmann's equation from data.

The method derives analytical velocity models consistent with physical principles.

RafNN requires only observational data, simplifying model construction.

Abstract

Seismic wave velocity of underground rock plays important role in detecting internal structure of the Earth. Rock physics models have long been the focus of predicting wave velocity. However, construction of a theoretical model requires careful physical considerations and mathematical derivations, which means a long research process. In addition, various complicated situations often occur in practice, which brings great difficulties to the application of theoretical models. On the other hand, there are many empirical formulas based on real data. These empirical models are often simple and easy to use, but may be not based on physical principles and lack a proper formulation of physics. This work proposed a rational function neural networks (RafNN) for data-driven rock physics modeling. Based on the observation data set, this method can deduce a velocity model which not only satisfies the actual data distribution, but also has a proper mathematical form reflecting the inherent rock physics. The Gassmann's equation, which is the most commonly used theoretical model relating bulk modulus of porous rock to mineral composition, porosity and fluid, is perfectly reconstructed by using data-driven RafNN. The advantage of this method is that only observational data sets are required to extract model equations, and no complex mathematical and physical processes are involved. This work opens up for the first time a new avenue on constructing analytical expression of velocity models using neural networks and field data, which is of great interest for exploring the heterogeneous structure of the Earth.