# Radar in 7500 m Well Based on Channel Adaptive Algorithm

## TL;DR

A radar system for deep wells uses a new communication method to work in extreme conditions and accurately maps underground structures.

## Contribution

A channel-adaptive 'communication + acquisition' architecture for deep-well radar is proposed and validated at 7500 m depth.

## Key findings

- The radar system achieved a GR-curve correlation of 0.92 with third-party geological logs.
- The casing reflector at ~7250 m was clearly detected with a bottom depth error of 0.013%.
- The system operates robustly in harsh deep-well environments using cost-effective FPGA hardware.

## Abstract

Deep-well radar telemetry over ultra-long cables suffers from strong frequency-selective attenuation and impedance drift under high temperature and pressure. We have proposed a channel-adaptive “communication + acquisition” architecture for a 7500 m borehole radar system. The scheme integrates spread-spectrum time domain reflectometry (SSTDR; m-sequence with BPSK) to monitor the cable in situ, identify termination/cable impedance, and adaptively match the load, thereby reducing reflection-induced loss. On the receiving side, we combine time domain adaptive equalization—implemented as an LMS-driven FIR filter—with frequency domain OFDM equalization based on least-squares (LS) channel estimation, enabling constellation recovery and robust demodulation over the distorted channel. The full processing chain is realized in real time on a Xilinx Artix-7 (XC7A100T) FPGA with module-level reuse and pre-stored training sequences for efficient hardware scheduling. In a field deployment in the Shunbei area at 7500 m depth, radar results show high agreement with third-party geological logs: the GR-curve correlation reaches 0.92, the casing reflector at ~7250 m is clearly reproduced, and the key bottom depth error is 0.013%. These results verify that the proposed system maintains stable communication and accurate imaging in harsh deep-well environments while remaining compact and implementable on cost-effective hardware.