A machine learning tool for interpretation of Mass Transport Deposits from seismic data

Priyadarshi Chinmoy Kumar,Kalachand Sain

doi:10.1038/s41598-020-71088-6

Priyadarshi Chinmoy Kumar, Kalachand Sain

Open Access

https://doi.org/10.1038/s41598-020-71088-6

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Journal: Scientific Reports	Publication Date: Aug 24, 2020
Citations: 4	License type: open-access

Affiliation: Wadia Institute of Himalayan Geology

Abstract

Machine learning is a tool that allows machines or intelligent systems to learn and get equipped to solve complex problems in predicting reliable outcome. The learning process consists of a set of computer algorithms that are employed to a small segment of data with a view to speed up realistic interpretation from entire data without extensive human intervention. Here we present an approach of supervised learning based on artificial neural network to automate the process of delineating structural distribution of Mass Transport Deposit (MTD) from 3D reflection seismic data. The responses, defined by a set of individual attributes, corresponding to the MTD, are computed from seismic volume and amalgamated them into a hybrid attribute. This generated new attribute, called as MTD Cube meta-attribute, does not only define the subsurface architecture of MTD distinctly but also reduces the human involvement thereby accelerating the process of interpretation. The system, after being fully trained, quality checked and validated, automatically delimits the structural geometry of MTDs within the Karewa prospect in northern Taranaki Basin off New Zealand, where MTDs are evidenced.

Highlights

Mass Transport Deposits (MTDs), occurring in different tectonic and depositional settings, are defined as gravity induced slope failure deposits that include creeps, slides, slumps and debris flows[1−6]
Though the single attribute technology has been successful in interpreting MTDs from seismic data, several authors[28−29] demonstrated the downside of such approach, where a single attribute hardly ever responds to a particular geological target
The present study attempts to demonstrate a semi-automatic approach for the interpretation of Karewa MTD within the Karewa 3D prospect (Fig. 1) from reflection seismic data based on artificial neural networks

Summary

Introduction

Mass Transport Deposits (MTDs), occurring in different tectonic and depositional settings, are defined as gravity induced slope failure deposits that include creeps, slides, slumps and debris flows[1−6]. The present study attempts to demonstrate a semi-automatic approach for the interpretation of Karewa MTD within the Karewa 3D prospect (Fig. 1) from reflection seismic data based on artificial neural networks In this process, a human analyst is tasked with analysing a small part of the data, which the algorithm uses as input in order to complete the rest of the analysis automatically— accelerating the process. Seismic attributes at randomly selected few example locations labelled by an interpreter are used to train the system (see the section “the MTD cube meta-attribute” in the Supplementary Note for detailed explanation) Such neural training outputs a hybrid attribute, called the MTD cube or MTDC meta-attribute (defined for the first time) that conspicuously delimits the geometry and distribution of Karewa MTD and augments interpretation of entire reflection seismic data with a much reduced human intervention

Results

Discussion

Conclusion