||Computer Engineering and Networks Laboratory

Data Exploration using Unsupervised Feature Extraction for Mixed Seismic SignalsMatthias Meyer, Samuel Weber, Jan Beutel

26.04.2017Matthias Meyer 1

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 2

||Computer Engineering and Networks Laboratory

Unique, multi-sensor, long-term field experiments and data sets Strongly heterogeneous Complementary sensing Continuously streaming data Demanding processing and

storage requirements

Little knowledge about data Which features are important? Which features form a cluster?

26.04.2017Matthias Meyer 3

Our Driver – Process Understanding in the Mountain Cryosphere

Open, online data access – http://data.permasense.ch

||Computer Engineering and Networks Laboratory

Matterhorn Hörnligrat, 3500m a.s.l., SE aspect Site of large rockfall event – July 2003, ~2’500m3

Seismometer (SM) Accelerometer (AM) Acoustic sensor (AS) Crackmeters (CR) Rock temperature L1-DGPS (GPS) High-resolution camera Weather station

26.04.2017Matthias Meyer 4

The Matterhorn Permafrost Observatory

[A. Hasler, S. Gruber and J. Beutel: Kinematics of steep bedrock permafrost. J. Geophys. Res., 117, F01016.

Weber, S., Beutel, J., Faillettaz, J., Hasler, A., Krautblatter, M., and Vieli, A.: Quantifying irreversible movement in steep, fractured bedrock permafrost on Matterhorn (CH), The Cryosphere, 11, 567-583.]

||Computer Engineering and Networks Laboratory

Matterhorn Hörnligrat, 3500m a.s.l., SE aspect Site of large rockfall event – July 2003, ~2’500m3

Seismometer (SM) Accelerometer (AM) Acoustic sensor (AS) Crackmeters (CR) Rock temperature L1-DGPS (GPS) High-resolution camera Weather station

26.04.2017Matthias Meyer 5

Acoustic and Seismic Emission Sensing

Semi-automaticFeature Extraction

ExplorationContext

||Computer Engineering and Networks Laboratory

Three sensor types cover large frequency range Profiling experiment to establish “ground truth”

26.04.2017Matthias Meyer 6

Acoustic and Seismic Emission Spectrum Profiling

||Computer Engineering and Networks Laboratory

Temporal and spectral patterns define acoustic and seismic events Example: Geophone waveforms

Acoustic and Seismic Event Characteristics

726.04.2017Matthias Meyer

Rockfall Strong Wind Mountaineers

||Computer Engineering and Networks Laboratory

Convolutional neural networks have proven effective for image classification Invariant to translations Best classifier in image classification competitions

Use CNNs on audio spectrograms Capture time dependency of audio events Patterns can be identified

Spectrogram can reveal common event class affinities

26.04.2017Matthias Meyer 8

Event Detection Using Algorithms

||Computer Engineering and Networks Laboratory

Continuous audio stream subdivided into overlapping spectrogram frames

Extract event characteristics Usage of convolutional neural network Problem: Characteristics are unknown

Autoencoder Transform spectrogram frames into reduced intermediate representation Try to recreate the input image from this representation and optimize parameters Assumption: Recreation performs better if autoencoder optimizes for event characteristics Intermediate representation can resemble characteristics

26.04.2017Matthias Meyer 9

Concept for Unsupervised Data Exploration

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 10

Unsupervised Feature Extraction Method – Training

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 11

Unsupervised Feature Extraction Method – Detection

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 12

Visualize Distances of Feature Vectors

Training Data Set (Labeled)[N. Takahashi, T. Naghibi, B. Pfister and L. Van Gool: Deep convolutional neural networks and data augmentation for acoustic event recognition. Proc. Interspeech, September 2016.]

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 13

Unsupervised Feature Extraction – Checked Against Labels

Training Data Set (Labeled)[N. Takahashi, T. Naghibi, B. Pfister and L. Van Gool: Deep convolutional neural networks and data augmentation for acoustic event recognition. Proc. Interspeech, September 2016.]

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 14

Applied on a Real Data Set – Field Work Day in June 2015

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 15

Data Exploration and Visualization

Labels are not known a-priori Interference from non-relevant factors

Information from multiple sensors can be combined ~ 2 years of content require intuitive analysis

Large scale data processing and interactive analysis necessary

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 16

Data Management Infrastructure

||Computer Engineering and Networks Laboratory

Web front-end Multi-year content Access to heterogeneous

data sources Server-side pre-computed

waveforms Responsive interaction

(zoom, pan,...) Data statistics

26.04.2017Matthias Meyer 17

Data Visualization

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 18

Identification of Unwanted Interference

||Computer Engineering and Networks Laboratory

Timeline-based labeling

Stored in online database

Event catalogue

Example: Mountaineer

26.04.2017Matthias Meyer 19

Event Labeling

||Computer Engineering and Networks Laboratory 26.04.2017Matthias Meyer 20

Combination with Cluster Analysis

Preliminary clustering and outlier detection

Facilitates labeling

ETH ZurichComputer Engineering and Networks LabGeodesy and Geodynamics LabMicro and Nanosystems

Federal Office for the EnvironmentUniversity of Zurich

Department of Geography